digital health dissertation

University of Minnesota

Digital conservancy.

•   University Digital Conservancy Home
• University of Minnesota Twin Cities
• Dissertations and Theses
• Dissertations

View/ Download file

Persistent link to this item, appears in collections, description, suggested citation, udc services.

• About the UDC
• How to Deposit
• Policies and Terms of Use

Related Services

• University Archives
• U of M Web Archive
• UMedia Archive
• Copyright Services
• Digital Library Services
• News & Events
• Staff Directory
• Subject Librarians
• Vision, Mission, & Goals

Advertisement

Digital health: trends, opportunities and challenges in medical devices, pharma and bio-technology

• Published: 11 April 2023
• Volume 11 , pages 11–30, ( 2023 )

Cite this article

• Naresh Kasoju   ORCID: orcid.org/0000-0002-8700-0729 1 ,
• N. S. Remya   ORCID: orcid.org/0000-0003-4068-4147 1 ,
• Renjith Sasi   ORCID: orcid.org/0000-0002-6430-2556 1 ,
• S. Sujesh 1 ,
• Biju Soman   ORCID: orcid.org/0000-0003-0748-0839 1 ,
• C. Kesavadas   ORCID: orcid.org/0000-0003-4914-8666 1 ,
• C. V. Muraleedharan   ORCID: orcid.org/0000-0002-8398-0379 1 ,
• P. R. Harikrishna Varma 1 &
• Sanjay Behari 1  

10k Accesses

7 Citations

17 Altmetric

Explore all metrics

Digital health interventions refer to the use of digital technology and connected devices to improve health outcomes and healthcare delivery. This includes telemedicine, electronic health records, wearable devices, mobile health applications, and other forms of digital health technology. To this end, several research and developmental activities in various fields are gaining momentum. For instance, in the medical devices sector, several smart biomedical materials and medical devices that are digitally enabled are rapidly being developed and introduced into clinical settings. In the pharma and allied sectors, digital health-focused technologies are widely being used through various stages of drug development, viz. computer-aided drug design, computational modeling for predictive toxicology, and big data analytics for clinical trial management. In the biotechnology and bioengineering fields, investigations are rapidly growing focus on digital health, such as omics biology, synthetic biology, systems biology, big data and personalized medicine. Though digital health-focused innovations are expanding the horizons of health in diverse ways, here the development in the fields of medical devices, pharmaceutical technologies and biotech sectors, with emphasis on trends, opportunities and challenges are reviewed. A perspective on the use of digital health in the Indian context is also included.

Avoid common mistakes on your manuscript.

1 Introduction

Digital health is a rapidly growing field that offers exciting opportunities for innovation and improvement in healthcare delivery. The goal of digital health is to make healthcare more efficient, accessible, and effective, by leveraging the power of digital technology to collect, analyze, store and share health data. Electronic Health Records (EHRs), telemedicine, mobile health apps, wearable devices, the internet of medical things and cutting-edge digital technology constitute digital health. The digital health market has been growing rapidly in recent years and is expected to continue its growth trajectory in the near future. The global digital health market size was valued at approximately US$211 billion in 2022 and is expected to grow at a compound annual growth rate (CAGR) of 18.6% from 2023 to 2030 [ 1 ]. In the Indian context, the digital health market is reported to be about US$12.2 billion in 2023 and is projected to reach US$25.64 billion by 2027 with a CAGR of about 20.4% [ 2 ]. The digital health market is highly fragmented and is characterized by a large number of small and medium-sized enterprises operating in various segments, such as wearable devices, telemedicine, EHRs, and mobile health apps. Major players in the digital health market include Apple, Google, Philips, Medtronic, and Roche, among others [ 3 ].

The growth of the digital health market can be attributed to several factors, including the increasing adoption of smartphones and other digital devices, the growing demand for remote monitoring and telemedicine services, and the increasing focus on the development of digital health solutions to address the challenges posed by the COVID-19 pandemic. Several groups are working on various aspects of digital health, and the number of scientific publications in this area has been growing rapidly in recent years (Fig.  1 ).

An overview of the publication trends in digital health as available from the web of science ( a ), and details of sectors where much of the research work is being focused ( b )

The medical device sector has seen significant innovations in digital health in recent years. These range from wearable devices, remote monitoring systems, telemedicine devices, and electronic drug dispensing units to smart inhalers. These innovations in the medical device sector have the potential to greatly improve healthcare delivery and patient outcomes by providing more efficient and effective ways to monitor and manage health. Similarly, in the pharma sector, Digital Health Technologies (DHTs) are being used in many ways. Several DHTs are being used to speed up the drug development process through (i) drug design by virtual screening tools, (ii) reducing animal usage by predictive toxicology, and (iii) streamlining clinical trials by digital data management. In the biotechnology and bioengineering front, developments in the field of omics, synthetic and systems biology, big data and precision medicine are leaning towards digital health. This review gives an overview of the trends, opportunities and challenges for digital health innovations in the medical device, pharma and bio-allied fields. Although there are pervious review articles on digital health in general, the current review was the first of its kind covering digital health technologies across the key segments in the health sector i.e. medical devices, pharma and biotechnology.

2 Developments in medical devices and allied technologies toward digital health

2.1 medical devices.

Digital health-focused medical devices are devices that utilize digital technologies to improve health and healthcare. These devices are playing an increasingly important role in improving healthcare delivery by enabling remote patient monitoring, increasing access to medical services, and reducing healthcare costs. They also offer the potential for improved patient outcomes by enabling early detection and intervention in medical conditions. However, there are challenges associated with the use of these devices, such as the need for appropriate regulatory oversight, privacy concerns, and attention to cybersecurity risks. Examples of digital health-focused medical devices are described below.

2.1.1 Wearable devices

Wearable technology has been an active area of research in recent years, with numerous advances. These devices such as smartwatches and fitness trackers monitor various aspects of a person's health, such as heart rate, sleep patterns, and physical activity [ 4 ]. Some wearable devices also have features such as ECG monitoring and fall detection. The latest wearable medical devices global market report underlines that the market would grow from US$22.44 billion (2022) to US$27.37 billion by the end of 2023, with a predicted annual growth rate of 21.9%. It further suggests that the trend will continue with the same CAGR to reach $60.48 billion in 2027. Following are some of the latest trends and developments in this area:

(a) Augmented reality (AR) integrated wearable devices It is an area that has seen significant growth in recent years [ 5 , 6 ]. Researchers are exploring ways to integrate AR technology into wearable devices to create an enhanced user experience. This could include things like displaying information directly on a user's field of vision or providing additional context to the real world.

(b) Artificial Intelligence (AI) integrated wearable devices AI is also being integrated into wearable devices to provide users with more advanced features and capabilities [ 7 ]. For example, wearable devices could use AI to analyze data from various sensors and make predictions about a user's health or provide personalized recommendations.

(c) Energy harvesting wearable devices This is another area of research in wearable technology [ 8 ]. Researchers are developing devices that can generate their power through movement, body heat, or other sources, which would make them more self-sufficient and reduce the need for charging.

A schematic representation of wearable, minimally invasive/implantable devices integrated with DHTs is presented in Fig.  2 .

A schematic illustration of an array of wearable, minimally invasive/implantable devices integrated with DHTs. Reproduced with permission from [ 9 ]. © 2016 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim

2.1.2 Diagnostic devices

These are devices used for diagnostic purposes, such as glucose meters for diabetic patients, spirometry devices for pulmonary function testing, and portable ultrasound machines. Following are some of the latest trends and developments in this area:

(a) Point-of-care (POC) diagnostics POC testing devices are becoming increasingly popular as they allow for rapid diagnostic testing at the point of care. These devices are designed to be small, portable, and easy to use, making it possible to diagnose a wide range of conditions in a variety of settings [ 10 ].

(b) Non-invasive diagnostics Non-invasive diagnostic devices are being developed to provide a more comfortable and convenient testing experience for patients [ 11 ]. For example, devices that use breath analysis or skin sensors to diagnose conditions are being developed, eliminating the need for invasive procedures such as blood tests.

(c) Nanotechnology-based diagnostics Nanotechnology is being used to develop new diagnostic devices that are more sensitive and efficient [ 12 ]. For example, nanoparticle-based devices are being developed that can detect specific biomarkers in blood and other body fluids, allowing for the early detection of diseases such as cancers.

2.1.3 Therapeutic devices

These are devices used for treatment, such as insulin pumps, implantable cardiac pacemakers, and deep brain stimulation devices. Following are some of the latest trends and developments in this area:

(a) Wearable therapeutics These devices are becoming increasingly popular as they allow for continuous and non-invasive treatment. For example, wearable devices are being developed to deliver electrical stimulation to the brain to treat conditions such as depression or to deliver drugs directly to the site of an injury [ 13 , 14 ].

(b) Non-invasive stimulators Non-invasive stimulation devices, such as transcranial magnetic or electrical stimulators, are being developed to treat a variety of conditions, including depression, anxiety, and chronic pain. These devices use magnetic or electrical fields to stimulate specific regions of the brain, providing a safe and non-invasive alternative to traditional treatments [ 15 ].

(c) Regenerative therapeutics Regenerative medicine is a growing area of research, and therapeutic devices are being developed to support the growth and regeneration of damaged tissue [ 16 ]. For example, devices are being developed to deliver growth factors to the site of an injury, promoting tissue repair and regeneration.

2.1.4 Medical imaging devices

These are devices used for imaging the body, such as X-ray machines, CT scanners, and MRI machines. Following are some of the latest trends and developments in this area:

(a) Artificial Intelligence-enabled classification and detection which is being used to improve the accuracy and efficiency of medical imaging devices [ 17 ]. For example, AI algorithms can be trained to identify patterns in medical images, such as X-rays or CT scans, helping healthcare providers make more informed diagnoses and prognoses.

(b) Medical imaging-assisted customized 3D printing products medical imaging-assisted 3D printing technology is being used to create customized medical devices, such as surgical models, prosthetics and implantable systems [ 18 , 19 ]. This can be particularly useful for patients with complex medical conditions, as 3D printing allows for the creation of devices that are tailored to the individual's specific needs.

(c) Non-Invasive Imaging Invasive imaging modalities are followed for diagnosing complicated medical conditions, such as coronary angiography for diagnosing coronary artery stenosis. To this end, non-invasive imaging devices that use ultrasound or optical imaging to visualize internal organs or tissues are being developed for detecting various conditions, such as coronary artery stenosis, myocardial infarction, liver metastasis, and beyond [ 20 ].

A schematic representation of AI-based medical image detection and analysis in the context of COVID-19 is presented in Fig.  3 .

A schematic showing deep learning-based medical image detection and analysis in the context of COVID-19. Reproduced from [ 21 ]. © The Authors 2021

2.1.5 Telemedicine devices

These are devices that enable remote patient monitoring and teleconsultations, such as remote patient monitoring systems, webcams, and handheld devices with cameras and communication capabilities. Following are some of the latest trends and developments in this area.

(a) Wearable telemedicine devices These devices are becoming increasingly popular, as they allow for continuous monitoring of a patient's health status [ 22 ]. These devices can track vital signs, such as heart rate and blood pressure, and transmit the data to healthcare providers for analysis.

(b) Remote diagnostic and intervention tools These tools are being developed to allow healthcare providers to diagnose and attend to conditions remotely [ 11 , 23 ]. For example, some telemedicine devices are equipped with cameras and other tools that allow healthcare providers to examine and intervene as necessary to attend to a patient remotely.

(c) Integration with Electronic Health Records Telemedicine devices are being integrated with EHRs to provide a more comprehensive view of a patient's health status [ 24 , 25 ]. This integration can help healthcare providers make more informed decisions about a patient's treatment plan, as they have access to a patient's complete medical history.

A schematic representation showing various DHT-enabled telemedicine modalities is presented in Fig.  4 .

A schematic showing various avenues of digital health technology-enabled telemedicine modalities. Reproduced from [ 26 ]. © The Authors 2021

2.2 Medical materials

As in many other avenues, the advent of digital health paved the way for tremendous development in the field of material science and related research [ 27 ]. Modern material science can contribute smart materials and analytical tools suitable for developing wearable medical devices and sensors required for this purpose. The wearable devices could help in the monitoring of chronic health conditions, therapy, diagnosis, rehabilitation, and tracking of physical activities [ 28 ]. Timely interventions supported by the real-time monitoring of health parameters using wearable devices would save the lives of many. The cloud-based operation of wearable medical devices enables medical professionals to monitor real-time vital parameters and to plan the requirement of physical visits, changes in therapy, and modalities for disease management. A schematic representation showing the work-flow behind cloud-based device performance is presented in Fig.  5 .

Schematic representation of work flow behind a cloud-based wearable device

The materials used for the fabrication of smart wearable medical devices should be biocompatible, flexible/wearable, lightweight, cost-effective, and smart enough to generate transmittable signals in response to changes in physiological parameters such as arterial pulse, body temperature, humidity, motion, and biomarkers in body fluids. Recognition of signals and their transformation are the two fundamental processes associated with any sensors used in the healthcare sector [ 29 ]. In wearable medical devices, the sensors respond to various parameters such as pressure, strain, temperature, the concentration of biomarkers, etc., and generate transmittable electronic/optical signals. Pressure/strain sensors, humidity/gas sensors, electrochemical sensors, colorimetric sensors, etc., are the major types of wearable sensors employed in the healthcare sector [ 30 ]. Even though many inorganic and metallic materials are available with excellent conductivity and sensing capability, inflexibility hinders their application in wearable devices.

Recently a wide variety of advanced smart materials have been utilized for developing wearable devices for healthcare applications. The most prominent ones are described below.

2.2.1 Ionic liquids

Ionic liquids were frequently used for the development of wearable sensors owing to their flexibility, conductivity, broad electrochemical window, better miscibility, negligible toxicity, and low vapor pressure [ 31 ]. Ionic liquid-based smart materials have been reported for a wide range of healthcare applications.

Wearable strain/pressure sensors: Ionic liquid smart devices could convert mechanical strain into processable and transmissible electrical signals in both resistive and capacitive modes. wearable motion sensors could be suitable for monitoring the elderly or rehabilitating population to assess their progress and to provide intervention as and when required [ 32 ].

Thermal sensors: As ionic liquids and ionic liquid crystals are capable of thermal transitions of their phases; they could be employed for monitoring the body temperatures of patients [ 33 ].

Breathing monitors: Ionic liquid-based wearable strain sensors were also reported to monitor the breathing events of patients with COPD or sleep apnoea. Stomach attachment of IL-based wearable breath rate sensors would provide alarms during dangerous breath variations or apnoea [ 34 ].

Sensors for cardiovascular parameters: Ionic liquid-based wearable devices were reported for ECG and EMG recordings [ 35 ].

Others: Ionic liquid-based devices were reported for monitoring skin humidity and evolved gases [ 36 ], glucose or lactate levels and pH from sweat [ 37 ], and for applications in therapeutics and drug delivery [ 38 ].

2.2.2 Carbon materials

A wide array of carbon nanomaterials like carbon nanotubes (CNTs), graphene-based materials, and carbon black (CB) were exploited for healthcare applications. Low cost, mass production capability, biocompatibility, and good mechanical and conduction behaviors made them suitable for generating smart medical devices. Carbon-based smart devices could be fabricated by a variety of methods such as chemical vapor deposition, drop casting, spin coating, screen or inkjet printing, and vacuum filtration. Their major applications are:

Wearable sensors for strain, pressure, temperature and humidity [ 39 ].

Biosensors for biomarker detection [ 40 ].

Others: bone and cartilage regeneration, Bioimaging, and Breath analysis [ 41 ].

An overview of various applications of DHT-enabled carbon nanomaterials in medical and other allied fields is presented in Fig.  6 .

An overview of various applications of DHT-enabled carbon nanomaterials in medical and other allied fields. Reproduced from [ 42 ]. © The Authors 2021

2.2.3 Gold nanomaterials

Gold nanomaterials are known to have better electrical conductivity, mechanical flexibility, biocompatibility, and a wide electrochemical sensing window. Their surfaces could be modified by suitable chemical reactions to improve their electrical and optical behaviors to fine-tune sensing capabilities.

Wearable strain/pressure sensor: Mechanical perturbation on the nano-dimensional gold is converted into a readable electrical signal. They mainly follow a resistance-type, capacitance-type, piezoelectric-type, or triboelectric-type transduction mechanism. Strain/pressure sensors could be applied in soft robotics, human–machine interactions, human motion detection systems, and in health monitoring [ 43 ].

Humidity sensors for human breath analysis: Humidity sensors function on the variation of the impedance values of the membranes with respect to humidity variations [ 44 ].

Others: Gold nanomaterial-based wearable biosensors were reported for various biomarkers, Wearable pH sensors, and bioimaging therapeutics and drug delivery [ 45 ].

In addition to these materials other nano materials, conducting polymers and smart materials were reported to be contributing to the area of digital health [ 46 ]. Thus, material science research flourished extensively due to the arrival of digital health platforms. In addition to material science research, the analytical modalities were also influenced by the rapid development of digital health. NIR and Raman-based non-invasive disease monitoring strategies were reported for the detection of disease conditions and measuring vital parameters [ 47 , 48 ]

3 Developments in pharma and allied areas toward digital health

The tremendous expansion of DHTs at both customer and professional levels has opened a better arena for the effective utilization of digital resources for the benefit of human welfare. In this context, DHTs are playing an increasingly important role in the delivery of pharmaceutical care. Despite the widespread acceptance of personalized technologies in pharma health care, the DHT system is not comprehensively reviewed in terms of drug discovery and development. Drug discovery and development is a complex and multi-step process that involves multiple stages generally taking a time frame of 10–12 years [ 49 ]. The following is a general flowchart that outlines the process of drug discovery:

Target identification and validation: In this stage, researchers identify and validate a biological target (e.g., a protein, gene, or pathway) that is involved in the disease process. They use various high-throughput screening techniques to identify the small molecule or biological entities (hits) that modulate the activity of the target and have potential therapeutic effects [ 50 ].

Lead optimization: In the optimization stage, the researchers optimize the potency, selectivity, pharmacokinetics, and pharmacodynamics of the lead compounds to produce lead candidates [ 51 ].

Pre-clinical evaluation: This is the stage where the researcher conducts a series of in vitro and in vivo studies to evaluate the safety, efficacy, pharmacokinetics, and pharmacodynamics of the lead candidates.

Clinical trials: Lead candidates that have passed preclinical testing are then tested in human clinical trials to evaluate their safety and efficacy in a larger population.

Regulatory approval: If the clinical trials are successful, the drug is then submitted to regulatory agencies for approval.

Marketing and sales: only after the drug gets approved, it can be manufactured and marketed for therapeutic use.

Post-market surveillance: This will be an indefinite process making the regulators monitor the efficacy and safety of the drug throughout its lifetime.

In this context, digital technologies are playing an increasingly important role in the development of new drugs. Some of the key ways that digital technologies are being used in drug development are presented in Fig.  7 and are detailed in the following sections.

An overview of applications of digital health technologies in drug discovery and development

3.1 Drug design

Computer-aided drug design (CADD) is the process of creating new drugs based on a thorough understanding of the biological target and its interaction with potential drugs [ 52 ]. It is a multi-disciplinary field that combines knowledge from chemistry, biology, pharmacology, and computational modeling to develop new drugs. There are several in silico approaches in practice for computer-aided drug design. This includes,

Pharmacophore modeling It is a computational approach used to predict the molecular features that are responsible for a molecule's biological activity. This is a useful tool for drug discovery and design, as it allows researchers to identify key structural features that are important for a molecule's interaction with its target protein, and to design new molecules that are likely to have similar activities. There are two types of pharmacophore modeling. (a) Ligand-based drug design This approach is based on the structural information of the active ligands that bind to the target. In a study by Kist et al. [ 53 ] by employing a ligand-based drug design approach, novel potential inhibitors of the mTor pathway were identified as having comparable or better properties to that of the classic drug rapamycin. (b) Structure-based drug design This strategy uses the three-dimensional structure of the biological target to design drugs that fit into specific pockets or active sites on the target, effectively blocking its activity. An example of a structure-based drug design strategy is reported in the development of 5 LOX inhibitors, a therapeutic target for asthma as well as other inflammatory diseases [ 54 ]. Catalyst software package, LigandScout, MOE (Molecular Operating Environment, Schrodingers Maestro, PyRx are some of the software packages used to generate pharmacophore models.

Drug target fishing This is a computational approach that is used to identify potential drug targets for a particular disease or biological process. And the goal of drug target fishing is to find proteins or other molecular targets that are likely to be involved in the disease or process of interest and to design drugs that can interact with these targets in a specific way to produce a therapeutic effect [ 55 ]. Different approaches could be employed for drug target identification that includes (i) homology modeling: comparing the structure of a protein of interest to the structures of other related proteins that are already known targets and identifying conserved regions in the protein structure that could serve as a potential target [ 56 ], (ii) bioinformatics: analyzing biological data, such as genomic sequences, transcriptomic data, and protein–protein interaction data, to identify potential drug targets [ 57 ]. (iii) systems biology: studying the complex interactions between different biological components in a particular disease or process [ 58 ] and (iv) High-Throughput screening [ 59 ]. The experimental setup in this context is the molecular docking and molecular dynamics (MD) simulation. There are several widely used software programs available for molecular docking, including AutoDock, Glide, Leadit, and eHiTS. There are several widely used software programs available for molecular dynamics simulation, including GROMACS, AMBER, NAMD, CHARMM, and LAMMPS that have wide applications in drug discovery.

3.2 Pre-clinical research

Pre-clinical research in drug development is a phase where a variety of experiments are conducted to assess the safety and efficacy of a new drug candidate. These may include toxicity studies to determine the potential for harmful effects, pharmacology studies to evaluate the drug’s interactions with the body, and efficacy studies to determine the potential therapeutic benefits of the drug. They involve animal models and the administration of the drugs to animals to assess any adverse effects and determine the optimal dosing regimen. Animal testing has been used for decades to evaluate the safety and efficacy of new drug candidates, but there is growing concern about the ethical and scientific limitations of this approach [ 60 ]. As a result, there is a growing interest in developing alternative methods to animal testing. In silico modeling of biological systems is one such approach that could be employed as an alternative to animal testing. The computer simulations of biological systems predict the behavior of a particular biological system, and hence, are used to evaluate the safety and efficacy of potential new drugs. In silico models can also be used to analyze large amounts of data, such as gene expression data or proteomics data generated following the interaction of the drug with the biological system. By using AI and Machine Learning (ML) algorithms, these models can identify patterns in the data that would not be easily noticeable by a human researcher, providing new insights into the biological mechanisms underlying disease and thereby helping in the appropriate intervention strategies. The following are the commonly used tools in predicting safety and toxicity in pre-clinical research.

QSAR (Quantitative Structure–Activity Relationship) models These are computational tools that are used to predict the biological activity of a chemical compound based on its molecular structure [ 61 ]. ML algorithms, such as artificial neural networks, decision trees, and support vector machines, are then used to identify relationships between molecular descriptors and biological activities [ 62 ]. Several software programs can be used for QSAR modeling, including KNIME, Pipeline Pilot, MOE (Molecular Operating Environment), OChem and R.

Virtual Toxicity Predictors (VTPs) Unlike QSAR models, virtual toxicity predictor software tools use molecular modeling and simulation to predict the toxicity of a potential new treatment based on its molecular structure. QSAR models typically provide a quantitative prediction of the toxicity of a chemical compound, while VTPs can provide more detailed information about the potential toxicity mechanisms [ 63 ]. Some of the popular software programs used for virtual toxicity prediction include ToxCast, Toxtree, VEGA (Virtual Expert System for Toxicity Assessment), OSIRIS, Leadscope and eTOXlab.

ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) analysis It helps in understanding the pharmacokinetics and pharmacodynamics of a drug and is a crucial step in the drug discovery process [ 64 ]. There are several software programs available for ADMET analysis, including both commercial and open-source options. Some popular choices include Schrodinger, Simulations Plus, OpenEye, Pipeline Pilot, Molsoft ICM-Pro, SwissADME, DEREK. However, the choice of software will depend on the specific needs of the user and the type of ADMET analysis being performed.

PBPK (Physiologically-Based Pharmacokinetic) modeling This type of modeling takes into account the anatomy and physiology of the body to simulate the distribution and elimination of drugs. The main goal of PBPK modeling is to predict how a drug will behave in the body based on the known physiological properties of the drug and the individual being treated.[ 65 ]. Examples of software available for PBPK modeling include SimCyp GastroPlus, PK-Sim, ADAPT II, PKQuest, MCSim.

3.3 Clinical trials

The primary goal of a clinical trial is to determine if a new drug is effective in treating a specific medical condition and if it is safe for human use. Clinical trials are usually conducted in three phases, each of which provides increasing amounts of information about the drug's safety and effectiveness. Phase 1 trials typically involve a small number of healthy volunteers and are designed to test the drug's safety and identify any side effects. Phase 2 trials involve a larger number of patients with the specific medical condition the drug is intended to treat. These trials are designed to test the drug's effectiveness and gather additional information about its safety. Phase 3 trials involve an even larger number of patients and are the final stage of testing before a drug is submitted for approval by regulatory agencies [ 66 ]. These trials are designed to provide a more complete picture of the drug's benefits and risks and to confirm its effectiveness.

The DHTs are rapidly changing the way clinical trials are conducted. They are being used to streamline the clinical trial process, allowing for faster and more efficient trials[ 67 ] Here are some examples of DHTs used in clinical trials:

Electronic Patient-Reported Outcomes (ePRO) ePRO is a digital tool that allows patients to report symptoms, side effects, and other outcomes directly to the study team. This technology can help improve patient compliance and reduce the need for in-person visits [ 68 ].

Mobile Health (mHealth) Applications mHealth applications can be used to collect data from patients, provide education and support, and monitor health status. This technology can help increase patient engagement and improve data quality [ 69 ].

Electronic Clinical Outcome Assessments (eCOA) eCOA is a digital tool that allows patients to self-report outcomes, such as quality of life, using a smartphone or tablet. This technology can help reduce the burden on patients and improve data quality [ 70 ].

Telemedicine Telemedicine technology, such as video conferencing, can be used to conduct virtual visits with patients. This technology can help reduce the need for in-person visits, increase patient convenience, and improve patient engagement [ 71 ].

Wearable Devices Wearable devices, such as smartwatches and fitness trackers, can be used to collect data on physical activity, sleep patterns, and other health metrics. This technology can help improve data quality and increase patient engagement [ 72 ].

3.4 Post-market surveillance

DHTs have revolutionized the way drugs are monitored after they have been approved and entered the market. In the past, post-market surveillance of drugs relied heavily on passive systems, where healthcare professionals and patients reported adverse events or side effects. However, with the advent of DHTs, this process has become more proactive and efficient. Some examples of DHTs used in post-market surveillance of drugs include:

Electronic health records EHRs provide a centralized platform for healthcare professionals to report and track adverse events associated with drugs. This information can then be analyzed to identify potential safety issues with a drug [ 73 ].

Mobile health (mHealth) applications mHealth apps allow patients to easily report adverse events or side effects from their smartphones. This provides a more direct and convenient way for patients to report issues, which can lead to quicker identification of safety concerns [ 74 ].

Clinical trials platforms Clinical trial platforms have become increasingly digital, allowing for real-time monitoring of drug safety during the clinical trial phase. This information can then be used in post-market surveillance to identify potential safety issues with a drug [ 75 ].

Real-world data (RWD) platforms RWD platforms gather and analyze data from a variety of sources, including EHRs, claims data, and patient-generated data, to provide a more complete picture of a drug's safety profile. This information can be used to identify potential safety issues and monitor the effectiveness of drugs in real-world settings [ 76 ].

Overall, digital technologies are playing a critical role in drug discovery and development, helping to improve the speed, efficiency, and accuracy of the drug development process. This is leading to the discovery of new treatments for a wide range of diseases and conditions, improving patient outcomes and transforming the healthcare industry.

4 Developments in bio and allied technologies toward digital health

Digital health in biotechnology and bioengineering refers to the use of digital technologies to develop and improve biotechnology and bioengineering products and applications. Digital health is playing a critical role in these fields by enabling the development of more sophisticated models of biological systems and by facilitating the optimization of bio-based healthcare product design and manufacture. Following are some of the key areas where the innovation-driven science and technological advances can transform digital health worldwide.

4.1 Omics biology

Omics is a field of study in medicine that encompasses various sub-disciplines such as genomics, transcriptomics, proteomics, metabolomics, and epigenomics. The goal of omics is to understand the underlying mechanisms of biological processes and diseases by looking at the collective behavior of all the molecules involved, such as genes, proteins, and metabolites. By combining data from these various omics disciplines, researchers can gain a more comprehensive understanding of the molecular basis of health and disease, which may lead to the development of new diagnostic tools, therapies, and personalized medicine approaches.

Omics technologies are also playing an increasingly important role in digital health, where they can be leveraged to improve the accuracy and precision of health assessments, diagnoses, and treatments. Following are a few examples of how Omics technologies can aid in digital health.

(a) Personalized medicine Omics data can be used to predict an individual's risk for developing certain diseases, monitor their health status over time, and tailor treatments to their unique genetic profile [ 77 ].

(b) Predictive analytics Predictive models based on omics data can be used to identify individuals who are at high risk for a disease, such as cancer, and to monitor their health status over time, allowing for early intervention and improved outcomes [ 78 ].

(c) Clinical decision support Clinical decision support systems that incorporate omics data can provide healthcare providers with real-time information and recommendations to help them make more informed treatment decisions for their patients [ 79 ].

(d) Omics data collecting medical devices medical devices that collect omics data, such as continuous glucose monitoring systems, can be used to monitor a patient's health status and to provide early warning signs of potential health issues.

(e) Omics data integrated telemedicine platforms that incorporate omics data can provide remote healthcare services, such as virtual consultations, to individuals in remote or underserved communities, improving access to care and outcomes [ 80 ].

A schematic representation showing omics-based DHTs towards personalized medicine is presented in Fig.  8 .

A schematic showing potential applications of omics-based digital health technologies toward personalized medicine. Reproduced from [ 81 ]. © The Authors 2018

4.2 Big data analytics

Big data refers to extremely large and complex data sets that are generated from various sources, including EHRs, medical imaging, genetic sequencing, and other sources. The market size for big data in digital health has been growing rapidly in recent years, driven by the increasing adoption of DHTs and the growing demand for data-driven decision-making in healthcare. According to market research, the global big data in digital health market was valued at approximately US$ 39.7 billion in 2022 and is expected to grow at a CAGR of 19.2% from 2022 to 2032 [ 82 ]. However, the use of big data in medicine also raises concerns about privacy, security, and the ethics of data collection and analysis. It is important to address these concerns and ensure that the benefits of big data are maximized while minimizing its risks.

Big data plays a critical role in digital health by providing the vast amounts of information that are needed to drive innovation and improve patient outcomes.

(a) Big electronic health records data analytics One of the main ways that big data is used in digital health is through the analysis of EHRs and other sources of health-related data [ 83 ]. EHRs contain a vast amount of patient information, including demographic data, medical history, lab results, and other information. By analyzing this data, healthcare providers can gain insights into patient populations and identify trends and patterns that can inform decision-making and improve patient care.

(b) Big wearable devices data analytics big data is used in digital health through the analysis of data generated by wearable devices and other DHTs [ 84 ]. These devices generate vast amounts of data, including information about physical activity, sleep patterns, and other health-related metrics. This data can be used to track health status, monitor disease progression, and inform treatment plans.

(c) Big omics data analytics Another way that big data is used in medicine is through the analysis of genetic data [ 85 ]. Advances in genetic sequencing technologies have enabled the rapid and cost-effective generation of large amounts of genetic data, which can be used to identify the genetic basis of diseases and inform the development of personalized medicine.

(d) Big imaging data analytics Big data is also being used in medical imaging to improve diagnosis and treatment [ 86 ]. For example, advanced algorithms can be used to analyze medical images to identify patterns and anomalies that may indicate disease. This information can then be used to inform diagnosis and treatment planning.

(e) Big data and predictive analytics Big data is also being used in digital health to develop predictive models and algorithms that can improve health outcomes. For example, ML algorithms can be trained on large data sets to identify patterns and relationships that can inform decision-making and improve disease management [ 87 ].

4.3 Personalized/precision medicine

Personalized medicine, also known as precision medicine, is a medical approach that takes into account individual differences in genes, environment, and lifestyle to develop a customized approach to healthcare [ 88 ]. The goal of personalized medicine is to provide the right treatment, at the right time, for the right patient. In traditional medicine, treatments are often based on a "one-size-fits-all" approach, which does not take into account the unique differences between individuals. However, with the advancement of genomic technologies and the increasing availability of patient data, it is now possible to tailor treatments to the specific needs of each patient. It is therefore important to note that personalized medicine is still in its early stages of development, and more research is needed to fully realize its potential.

Several DHTs are playing a critical role in personalized medicine viz. EHRs, telemedicine tools, wearable devices, big data analytics, additive manufacturing, AI/ML-based algorithms, and several personalized mobile apps. These personalized DHTs are playing an increasingly important role in precision medicine, providing healthcare providers with the tools they need to deliver more effective and efficient care to patients. Examples of personalized medicine include:

(a) Precision or personalized care Precision oncology is a type of personalized medicine that uses genetic information to tailor cancer treatments to the specific needs of each patient [ 89 ]. Precision psychiatry is a type of personalized medicine that uses genetic information to tailor psychiatric treatments to the specific needs of each patient [ 90 ].

(b) Precision or personalized drug dosage Personalized drug therapy is a type of personalized medicine that uses genetic information to determine the most effective drug for a particular patient [ 91 ].

(c) Precision surgical models The use of digital technologies such as computer-aided designing and manufacturing helps in scanning the defect site and manufacturing a surgical model utilizing 3D printing for enabling the surgeons to plan the surgery effectively and efficiently [ 92 ].

(d) Personalized regenerative therapies Advanced tissue engineering and regenerative medicine technologies such as 3D bioprinting help in bio-fabricating a tissue that is not only biocompatible but also fits precisely to the defect size of the patient [ 93 ].

(e) Precision public health Proactive use of technology brings in new avenues to address many age-old public health issues. Optimal use of geographic information systems (GIS) and other spatiotemporal analysis will help in more precise and timely field-level interventions, which are crucial in the early detection and control of infectious diseases. This is well documented in the control strategies of the recent Zika outbreak in the USA. In Florida, only two small counties were to be put under lockdown, that too for a short while, to arrest the spread of Zika in 2016 [ 94 ]. The predict and prevent framework is a futuristic method to address the burgeoning problem of non-communicable diseases through precision public health. The use of Electronic medical Support for Public health (ESP) and its visualization platform Riscape helps in long-term follow-up and real-time intervention in the surveillance of NCDs [ 95 ].

A schematic representation showing DHT-enabled personalized medicine is presented in Fig.  9 .

A schematic showing integration of several technologies toward personalized/precision digital health. Reproduced with permission from [ 88 ]. © 2019 Elsevier Ltd

4.4 Synthetic biology

Synthetic biology is a multidisciplinary field that combines biology and engineering to design and construct novel biological systems for various applications [ 96 ]. It involves the manipulation of genetic material and metabolic pathways in living organisms to create new functions or modify existing ones. In synthetic biology, researchers use a combination of molecular biology techniques, computational modeling, and engineering principles to design, build, and test biological systems. These systems can be used in a wide range of applications, including the creation of new medicines, the development of biosensors, and beyond. One of the key features of synthetic biology is the use of standard biological parts, such as genes and regulatory elements, that can be combined and reused to create complex biological systems. This modular approach allows for rapid design and testing, as well as the potential for large-scale deployment of these systems. Synthetic biology has the potential to revolutionize many areas of biotechnology, medicine, and beyond, but it also requires careful consideration and oversight to ensure its safe and responsible development.

Synthetic biology has the potential to play a significant role in digital health [ 97 ]. One example of how synthetic biology can be used in digital health is the development of biosensors [ 98 ]. Biosensors are devices that use biological components, such as enzymes or antibodies, to detect specific substances. Synthetic biology can be used to design and construct biosensors that are specific for certain biomarkers, such as glucose or cholesterol, which can be used for continuous monitoring of health status. The results can be transmitted wirelessly to a digital platform for analysis and interpretation, enabling remote monitoring and disease management. Another example is the use of synthetic biology in the development of personalized medicine [ 99 ]. By using synthetic biology to design and engineer cells, researchers can create new therapeutic interventions that are tailored to a specific individual's needs [ 100 ]. For example, synthetic biology can be used to create cells that produce a specific protein or to correct genetic mutations that cause disease. These cells can then be monitored and controlled using digital technologies, enabling real-time monitoring of treatment efficacy and enabling adjustments to the therapy as needed. Overall, synthetic biology has the potential to revolutionize digital health by enabling the development of new technologies that can be integrated into digital platforms to improve health and healthcare.

4.5 Systems biology

Systems biology is an interdisciplinary field of study that aims to understand the complex relationships between the components of biological systems, such as cells, tissues, and organs [ 101 ]. It seeks to understand how these systems interact and function as a whole, rather than simply focusing on individual components in isolation. Systems biology approaches biological systems from a holistic perspective, using computational and mathematical models to simulate the interactions between different components and to predict the behavior of the system as a whole. It also incorporates high-throughput data from various sources, such as genomics, proteomics, and metabolomics, to create a comprehensive view of the system. One of the key goals of systems biology is to understand the underlying mechanisms of disease and to develop new therapeutic strategies that target the root causes of diseases, rather than just their symptoms. It also seeks to improve our understanding of the interactions between different components of the body and how they contribute to health and disease. In addition, systems biology is playing an important role in the development of personalized medicine, as it provides a framework for integrating patient-specific data and generating personalized models of disease.

Systems biology can play a critical role in digital health in several ways as follows. (a) Predictive Modelling: Systems biology can be used to build predictive models of disease, which can help healthcare providers to identify individuals at risk of developing certain conditions and to develop targeted prevention strategies [ 102 ]. (b) Clinical Decision Support: Systems biology can be used to develop clinical decision support systems, which can help healthcare providers to make more informed treatment decisions based on the latest scientific evidence and patient-specific data [ 103 ]. (c) Clinical Trial Design: Systems biology can be used to inform the design of clinical trials, by helping to identify the most promising therapeutic targets and to predict the outcomes of different treatment strategies [ 104 ]. (d) Data Integration and Management: Systems biology can be used to integrate and manage large amounts of patient data, including genomic, proteomic, and clinical data, to create a comprehensive view of the patient and to inform the development of personalized treatment plans [ 105 ]. (e) Monitoring and Evaluation: Systems biology can be used to monitor the effectiveness of treatments and to evaluate the impact of treatments on patient outcomes [ 106 ]. Overall, systems biology offers a framework for integrating and analyzing large amounts of patient data and for developing personalized models of disease, which can inform the development of more effective and efficient treatment strategies.

A schematic representation showing DHTs integrated systems biology approaches toward effective clinical decisions are presented in Fig.  10 .

A schematic showing a systems biology approach toward developing super models for effective clinical decisions. Reproduced from [ 107 ]. © The Authors 2015

5 Challenges associated with digital health technologies

With opportunities comes risks, and the same is true for the DHTs that present several challenges along with their opportunities [ 108 ].

(a) Privacy, security and ethical concerns With the increasing growth of mobile-based health apps and connected health systems, much the data including the personal information of patients is being collected. For instance, privacy and security are major concerns in digital health, with the sensitive nature of health data, making it a prime target for hackers. Also, DHTs had several ethical issues including the question of who owns the data.

(b) Interoperability This is another major challenge in digital health, with different digital health systems not being able to communicate with each other effectively. This makes it difficult for healthcare providers to access and share patient data, which can negatively impact patient outcomes.

(c) Regulatory framework Regulation is yet another bottleneck in digital health, with different countries having different regulations and guidelines for DHTs.

(d) Public awareness As many healthcare providers and patients are resistant to change and not fully understanding the benefits of DHTs, creating awareness about its benefits and risks is necessary for ensuring that DHTs are accepted by the general population.

(e) Legislative issues The laws of the land should appreciate the newer trends in science and technology for their optimal use. For example, vagueness in the legal validity of digital prescriptions was a major hurdle in the update of telemedicine in India before the hurried enactment of the Telemedicine Practice Guidelines in 2020, in the wake of the Covid-19 pandemic [ 109 , 110 ].

6 Digital health—an Indian perspective

6.1 digital health—initiatives from the government of india.

Digital health is a rapidly growing field that involves the use of digital technologies to improve health and healthcare delivery in India [ 111 ]. In recent years, there has been a significant investment in digital health infrastructure and initiatives, and a tremendous increase in the use of DHTs by both healthcare providers and patients in India. Examples of digital health initiatives in India include:

(a) Telemedicine Telemedicine services are widely available in India and are being used to provide remote consultations and support to patients in rural and underserved areas [ 112 ].

(b) Electronic health records Several hospitals have initiated programs to establish EHRs for all their clients, and even a comprehensive nationwide EHR platform is under consideration for the storage and sharing of patient health information between healthcare providers [ 113 ].

(c) mHealth The use of mobile health technologies, such as mobile apps and SMS-based services, is widespread in India and is being used to deliver health information and services to patients [ 114 ].

(d) Digital health marketplaces Digital health marketplaces, such as online pharmacies and telemedicine platforms, are becoming increasingly popular in India and are providing patients with access to a wide range of health products and services [ 115 ].

(e) Artificial intelligence in healthcare AI is being used in various applications in the Indian healthcare system, such as in radiology, oncology, and cardiology, to improve diagnosis and treatment [ 116 ].

Several institutions in India are working on digital health, including (a) Indian Council of Medical Research (ICMR): The ICMR is the main body responsible for promoting and coordinating biomedical research in India and has been involved in several digital health initiatives. (b) Apollo Hospitals: Apollo Hospitals is one of the largest healthcare groups in India and is a pioneer in the use of DHTs, including telemedicine and EHRs. (c) Tata Consultancy Services (TCS): TCS is a leading technology and consulting company in India and is involved in several digital health initiatives, including the development of EHRs and telemedicine solutions. (d) MedTech Zone: MedTech Zone is a digital health accelerator program in India that supports the development of early-stage digital health start-ups. (e) AI in Healthcare India: AI in Healthcare India is a non-profit organization that promotes the use of AI in healthcare in India and provides a platform for the exchange of ideas and knowledge on AI in healthcare. These are just a few of the many institutions in India that are involved in digital health initiatives.

Several companies in India are working on digital health, including (a) Practo and Doctor on Call: Practo and Doctor on Call are prominent digital health companies in India offering online doctor appointments including remote consultations and support to patients. (b) NetMeds, Tata 1 mg, Medlife and PharmEasy: these are prominent online platforms for ordering medicines and booking diagnostic tests. (c) HealthKart and HealthifyMe: these are some prominent digital health platforms in India that provide health supplements, and zpersonalized health and wellness coaching. (d) GoQii: GoQii is a digital health platform in India that provides personalized health and wellness coaching and wearable fitness trackers. (e) Besides, several small to medium scale digital health services offer online diagnostic services. These are just a few of the many companies in India that are involved in digital health initiatives. The Indian digital health market is rapidly growing, and many more companies and start-ups are entering the market.

The National Health Authority of the Government of India supports digital health through several schemes. Some of the prominent schemes include (a) Digital India: Digital India is a government initiative in India that aims to transform India into a digitally empowered society and knowledge economy. The initiative includes several components related to digital health, including CoWin and Arogya Sethu ( https://digitalindia.gov.in/ ). (b) e-Health & Telemedicine: various Information & Communication Technologies (ICT)-enabled initiatives are undertaken for improving the efficiency and effectiveness of the public healthcare system ( https://main.mohfw.gov.in/Organisation/departments-health-and-family-welfare/e-Health-Telemedicine ). (c) Ayushman Bharat Digital Mission (ABDM ): through this mission, the Govt. of India aims to develop the backbone necessary to support the integrated digital health infrastructure of the country ( https://abdm.gov.in/ ). (d) National Digital Health Mission (NDHM): The NDHM is a government-led health mission in India that aims to provide universal health coverage to all the citizens in the country through digital technologies ( https://www.makeinindia.com/national-digital-health-mission ). (e) National Health Stack (NHS): The program aims to facilitate the collection of comprehensive healthcare data to aid in policymaking, allocation of resources and identification of needy populations for health schemes. These are just a few of the many government schemes and it is seen that the Indian government is committed to promoting and supporting digital health initiatives in the country. A flyer released by Govt. of India on NDHM is presented in Fig.  11 .

A schematic representation showing various components of the National Digital Health Mission initiative by the Government of India. Adopted with permission from [ 117 ]. © 2023 Sanskriti IAS

Despite the significant opportunities and progress in digital health in India, several challenges need to be addressed, including the need for robust privacy and security measures, the need for greater investment in digital health infrastructure, and the need for greater training and capacity building for healthcare providers.

6.2 Digital health—initiatives from the state of Kerala (India)

Kerala is at the forefront of implementing EHRs for its population. Well before the era of the Individual Health ID of the Ayushman Digital Health Mission, Govt. of Kerala launched its ambitious eHealth Kerala project to create EHRs for all of its citizens in 2016. Though not perfect, it has added to the impetus of digitalization of the health sector in Kerala, thanks to the earlier implementation of the District Health System software (DHIS2) in all the 1000-plus public health institutions in the state.

The health workers in Kerala are familiar with digital health tools, and many public health centers use electronic medical records. However, the use of data for decision-making is not yet a norm in the health system, nor the public health area. Reasons for this low use of information are many, the lack of a clear data policy on who can have access to the data at various levels is a major one.

The state could take advantage of the e-Sanjeevani telemedicine platform during the Covid-19 pandemic to cater to the healthcare needs of its population. In the current e-Health Kerala project, there is a facility to do telemedicine consultations within the regular consultation hours. A few institutions in Kerala, like the Regional Cancer Centre (RCC) and SCTIMST, are going ahead with the doctor-to-doctor e-Sanjeevani consultations.

6.3 Digital health—initiatives from SCTIMST, Trivandrum (India)

Sree Chitra Tirunal Institute for Medical Sciences and Technology (SCTIMST) is an Institution of National Importance under the Department of Science and Technology, Govt. of India. The institute is known for its high-quality advanced treatment of cardiac and neurological disorders, indigenous development of technologies for biomedical devices and public health training and research. The Institute has three wings—the Hospital, Biomedical Technology Wing and the Achutha Menon Centre for Health Science Studies (AMCHSS). The institute is proactive in catching up with the latest technologies in the field including DHTs.

In the clinical scenario, the medical wing of the institute has a custom-built electronic medical record system for its clinical services, entirely created by the in-house computer division. In recent years, the institute has incorporated newer standards, including the SNOMED-CT coding for the diagnostic fields. The anonymized data extraction from this system has supported many research initiatives of the institute. Similarly, SCTIMST has created a fully geo-referenced mapping of its field practice area covering around 35,000 households (a population of 1.32 lakhs) with community participation [ 118 ]. The experiences from such and similar initiatives have given confidence to the state government to undertake more challenging digital health interventions like the e-Health Kerala project. Besides, the various divisions of the medical wing are actively involved in the development of various DHTs in clinical settings.

The Biomedical Technology Wing of the Institute was instrumental in nurturing the Indian medical device industry through know-how development and transfer, providing internationally accredited testing services and offering technology incubation facilities for young entrepreneurs. The BMT wing is actively involved in the development of DHT-enabled medical devices such as para-corporeal left ventricular assist device, centrifugal blood pump with blood flowmeter, deep brain stimulator system for movement disorders, intracranial electrodes, optical peripheral nerve stimulator, 3D printed liver and skin tissue constructs for regenerative applications, PT/INR sensing devices, loop-mediated isothermal amplification-based diagnostic kits, implantable cardioverter defibrillator, programmable hydrocephalus shunt, implantable micro infusion pump with wireless recharging system, and POC kits for sepsis and chlamydia trachomatis. Besides, there are many innovative projects based on smart biomaterials and combinational medical devices under development.

The AMCHSS, the public health division of SCTIMST, was a partner in the customization of the DHIS-2 software for the Indian context, which was piloted in its field practice area in Athiyannur block in Thiruvananthapuram. This led to many field-based research initiatives on digital health, spanning from its use in infectious diseases [ 119 , 120 , 121 , 122 ] to non-communicable diseases [ 123 , 124 , 125 ]. In recent times AMCHSS is moving ahead with infectious disease modeling and the use of data science approaches to large-scale data [ 126 , 127 , 128 , 129 , 130 ]. Lately, the ICMR has entrusted AMCHSS with the analysis of the COVID-19 test data for the entire country. Besides, the AMCHSS is actively involved in the development of various public-health-focused DHTs.

7 Conclusions

Digital health technologies (DHTs) aim to improve the healthcare system across the globe. By providing more accurate diagnoses, enabling more effective treatments and improving patient engagement and compliance, DHTs have the potential to significantly improve patient outcomes. By enabling more efficient and effective delivery of healthcare services and by reducing the need for in-person visits, DHTs can reduce healthcare costs. DHTs can increase access to healthcare through means of remote consultations and support to patients in remote and underserved areas. By compiling patient data from several healthcare providers a more comprehensive digital health record of each patient can be created, providing more accurate and comprehensive patient data to healthcare providers. Thus DHTs can significantly improve healthcare delivery by enabling informed clinical decisions. Further big data can be analyzed by AI-clinical decision support systems to aid the healthcare provider.

Digital health is a rapidly developing field and DHTs are providing new opportunities for innovation and growth, and thus are transforming the medical, pharma, biotech and allied fields. In the medical devices sector, innovations in the field of smart materials, wearable devices, and AI/ML-based systems are rapidly being introduced for clinical use. In the pharma sector, the use of digital technologies has widespread use through various stages of drug development viz. drug design, preclinical validation, and clinical trial. In the biotech and bioengineering sector, digital technologies are aiding in the development of precision and personalized medicinal products. This means there is a growing opportunity for start-ups and established companies to develop new and innovative DHTs. However, a word of caution is necessary to beware of the risks associated with DHTs including ethical and technical concerns.

Digital Health Market Size, Share & Trends Report, 2030. https://www.grandviewresearch.com/industry-analysis/digital-health-market

Digital Health—India. https://www.statista.com/outlook/dmo/digital-health/india

Smartphone-based patient monitoring global market report 2022. https://www.businesswire.com/news/home/20230104005482/en/Smartphone-Based-Patient-Monitoring-Global-Market-Report-2022-Featuring-Leading-Players---Apple-Boston-Scientific-Cerner-Medtronic-and-Phillips-Healthcare---ResearchAndMarkets.com

Dunn J, Runge R, Snyder M (2018) Wearables and the medical revolution. Pers Med 15:429–448. https://doi.org/10.2217/pme-2018-0044

Article   Google Scholar  

Venkatesan M, Mohan H, Ryan JR et al (2021) Virtual and augmented reality for biomedical applications. Cell Rep Med 2:100348. https://doi.org/10.1016/j.xcrm.2021.100348

Vinolo Gil MJ, Gonzalez-Medina G, Lucena-Anton D et al (2021) Augmented reality in physical therapy: systematic review and meta-analysis. JMIR Serious Games 9:e30985. https://doi.org/10.2196/30985

Chen M, Decary M (2020) Artificial intelligence in healthcare: an essential guide for health leaders. Healthc Manag Forum 33:10–18. https://doi.org/10.1177/0840470419873123

Li L, Lou Z, Chen D et al (2018) Recent advances in flexible/stretchable supercapacitors for wearable electronics. Small 14:1702829. https://doi.org/10.1002/smll.201702829

Choi S, Lee H, Ghaffari R et al (2016) Recent advances in flexible and stretchable bio-electronic devices integrated with nanomaterials. Adv Mater 28:4203–4218. https://doi.org/10.1002/adma.201504150

Gous N, Boeras DI, Cheng B et al (2018) The impact of digital technologies on point-of-care diagnostics in resource-limited settings. Expert Rev Mol Diagn 18:385–397. https://doi.org/10.1080/14737159.2018.1460205

Salem M, Elkaseer A, El-Maddah IAM et al (2022) Non-invasive data acquisition and iot solution for human vital signs monitoring: applications. Limit Future Prospects Sens 22:6625. https://doi.org/10.3390/s22176625

Belushkin A, Yesilkoy F, Altug H (2018) Nanoparticle-enhanced plasmonic biosensor for digital biomarker detection in a microarray. ACS Nano 12:4453–4461. https://doi.org/10.1021/acsnano.8b00519

Kar A, Ahamad N, Dewani M et al (2022) Wearable and implantable devices for drug delivery: applications and challenges. Biomaterials 283:121435. https://doi.org/10.1016/j.biomaterials.2022.121435

Long Y, Li J, Yang F et al (2021) Wearable and implantable electroceuticals for therapeutic electrostimulations. Adv Sci 8:2004023. https://doi.org/10.1002/advs.202004023

Pilotto A, Rizzetti MC, Lombardi A et al (2021) Cerebellar rTMS in PSP: a double-blind sham-controlled study using mobile health technology. Cerebellum 20:662–666. https://doi.org/10.1007/s12311-021-01239-6

Farahani M, Shafiee A (2021) Wound healing: from passive to smart dressings. Adv Healthc Mater 10:2100477. https://doi.org/10.1002/adhm.202100477

Gore JC (2020) Artificial intelligence in medical imaging. Magn Reson Imaging 68:A1–A4. https://doi.org/10.1016/j.mri.2019.12.006

Pugliese L, Marconi S, Negrello E et al (2018) The clinical use of 3D printing in surgery. Update Surg 70:381–388. https://doi.org/10.1007/s13304-018-0586-5

Sun L, Wong Y (2019) Personalized three-dimensional printed models in congenital heart disease. J Clin Med 8:522. https://doi.org/10.3390/jcm8040522

Sirajuddin A, Mirmomen SM, Kligerman SJ et al (2021) Ischemic heart disease: noninvasive imaging techniques and findings. Radiographics 41:E990–E1021. https://doi.org/10.1148/rg.2021200125

Yang D, Martinez C, Visuña L et al (2021) Detection and analysis of COVID-19 in medical images using deep learning techniques. Sci Rep 11:19638. https://doi.org/10.1038/s41598-021-99015-3

Shan R, Sarkar S, Martin SS (2019) Digital health technology and mobile devices for the management of diabetes mellitus: state of the art. Diabetologia 62:877–887. https://doi.org/10.1007/s00125-019-4864-7

Ong DSY, Poljak M (2020) Smartphones as mobile microbiological laboratories. Clin Microbiol Infect 26:421–424. https://doi.org/10.1016/j.cmi.2019.09.026

Coons SJ, Eremenco S, Lundy JJ et al (2015) Capturing patient-reported outcome (PRO) data electronically: the past, present, and promise of epro measurement in clinical trials. Patient Cent Outcomes Res 8:301–309. https://doi.org/10.1007/s40271-014-0090-z

Dinh-Le C, Chuang R, Chokshi S, Mann D (2019) Wearable health technology and electronic health record integration: scoping review and future directions. JMIR MHealth UHealth 7:e12861. https://doi.org/10.2196/12861

Shen Y-T, Chen L, Yue W-W, Xu H-X (2021) Digital technology-based telemedicine for the COVID-19 pandemic. Front Med 8:646506. https://doi.org/10.3389/fmed.2021.646506

Guo C, Ashrafian H, Ghafur S et al (2020) Challenges for the evaluation of digital health solutions—a call for innovative evidence generation approaches. NPJ Digit Med 3:110. https://doi.org/10.1038/s41746-020-00314-2

Kim J, Campbell AS, de Ávila BE-F, Wang J (2019) Wearable biosensors for healthcare monitoring. Nat Biotechnol 37:389–406. https://doi.org/10.1038/s41587-019-0045-y

Koydemir HC, Ozcan A (2018) Wearable and implantable sensors for biomedical applications. Annu Rev Anal Chem 11:127–146. https://doi.org/10.1146/annurev-anchem-061417-125956

Wang C, Xia K, Wang H et al (2019) Advanced carbon for flexible and wearable electronics. Adv Mater 31:1801072. https://doi.org/10.1002/adma.201801072

Correia DM, Fernandes LC, Fernandes MM et al (2021) Ionic liquid-based materials for biomedical applications. Nanomaterials 11:2401. https://doi.org/10.3390/nano11092401

Choi DY, Kim MH, Oh YS et al (2017) Highly stretchable, hysteresis-free ionic liquid-based strain sensor for precise human motion monitoring. ACS Appl Mater Interfaces 9:1770–1780. https://doi.org/10.1021/acsami.6b12415

Yamada S, Toshiyoshi H (2020) Temperature sensor with a water-dissolvable ionic gel for ionic skin. ACS Appl Mater Interfaces 12:36449–36457. https://doi.org/10.1021/acsami.0c10229

Zhang H, Lowe A, Kalra A, Yu Y (2021) A flexible strain sensor based on embedded ionic liquid. Sensors 21:5760. https://doi.org/10.3390/s21175760

Yu Z, Wu P (2021) Water-resistant ionogel electrode with tailorable mechanical properties for aquatic ambulatory physiological signal monitoring. Adv Funct Mater 31:2107226. https://doi.org/10.1002/adfm.202107226

Article   MathSciNet   Google Scholar  

Esteves C, Palma SICJ, Costa HMA et al (2022) Tackling humidity with designer ionic liquid-based gas sensing soft materials. Adv Mater 34:2107205. https://doi.org/10.1002/adma.202107205

Curto VF, Fay C, Coyle S et al (2012) Real-time sweat pH monitoring based on a wearable chemical barcode micro-fluidic platform incorporating ionic liquids. Sens Actuators B Chem 171–172:1327–1334. https://doi.org/10.1016/j.snb.2012.06.048

Zandu SK, Chopra H, Singh I (2020) Ionic liquids for therapeutic and drug delivery applications. Curr Drug Res Rev 12:26–41. https://doi.org/10.2174/2589977511666191125103338

Jian M, Wang C, Wang Q et al (2017) Advanced carbon materials for flexible and wearable sensors. Sci China Mater 60:1026–1062. https://doi.org/10.1007/s40843-017-9077-x

Castro KPR, Colombo RNP, Iost RM et al (2023) Low-dimensionality carbon-based biosensors: the new era of emerging technologies in bioanalytical chemistry. Anal Bioanal Chem. https://doi.org/10.1007/s00216-023-04578-x

Das S, Pal M (2020) Review—non-invasive monitoring of human health by exhaled breath analysis: a comprehensive review. J Electrochem Soc 167:037562. https://doi.org/10.1149/1945-7111/ab67a6

Pang J, Bachmatiuk A, Yang F et al (2021) Applications of carbon nanotubes in the internet of things era. Nano-Micro Lett 13:191. https://doi.org/10.1007/s40820-021-00721-4

Yi J, Xianyu Y (2022) Gold nanomaterials-implemented wearable sensors for healthcare applications. Adv Funct Mater 32:2113012. https://doi.org/10.1002/adfm.202113012

Ali I, Chen L, Huang Y et al (2018) Humidity-responsive gold aerogel for real-time monitoring of human breath. Langmuir 34:4908–4913. https://doi.org/10.1021/acs.langmuir.8b00472

Haine AT, Niidome T (2017) Gold nanorods as nanodevices for bioimaging, photothermal therapeutics, and drug delivery. Chem Pharm Bull (Tokyo) 65:625–628. https://doi.org/10.1248/cpb.c17-00102

Jin H, Jin Q, Jian J (2018) Smart materials for wearable healthcare devices. In: Ortiz JH (ed) Wearable technologies. InTech

Google Scholar  

Choo-Smith L-P, Edwards HGM, Endtz HP et al (2002) Medical applications of Raman spectroscopy: from proof of principle to clinical implementation. Biopolymers 67:1–9. https://doi.org/10.1002/bip.10064

Kothari R, Jones V, Mena D et al (2021) Raman spectroscopy and artificial intelligence to predict the Bayesian probability of breast cancer. Sci Rep 11:6482. https://doi.org/10.1038/s41598-021-85758-6

Deore AB, Dhumane JR, Wagh R, Sonawane R (2019) The stages of drug discovery and development process. Asian J Pharm Res Dev 7:62–67. https://doi.org/10.22270/ajprd.v7i6.616

Hughes J, Rees S, Kalindjian S, Philpott K (2011) Principles of early drug discovery: principles of early drug discovery. Br J Pharmacol 162:1239–1249. https://doi.org/10.1111/j.1476-5381.2010.01127.x

Showell GA, Mills JS (2003) Chemistry challenges in lead optimization: silicon isosteres in drug discovery. Drug Discov Today 8:551–556. https://doi.org/10.1016/S1359-6446(03)02726-0

Vemula D, Jayasurya P, Sushmitha V et al (2023) CADD, AI and ML in drug discovery: a comprehensive review. Eur J Pharm Sci 181:106324. https://doi.org/10.1016/j.ejps.2022.106324

Kist R, Timmers LFSM, Caceres RA (2018) Searching for potential mTOR inhibitors: ligand-based drug design, docking and molecular dynamics studies of rapamycin binding site. J Mol Graph Model 80:251–263. https://doi.org/10.1016/j.jmgm.2017.12.015

Aparoy P, Kumar Reddy K, Reddanna P (2012) Structure and ligand based drug design strategies in the development of novel 5- LOX inhibitors. Curr Med Chem 19:3763–3778. https://doi.org/10.2174/092986712801661112

Kanakaveti V, Shanmugam A, Ramakrishnan C et al (2020) Computational approaches for identifying potential inhibitors on targeting protein interactions in drug discovery. In: Advances in protein chemistry and structural biology. Elsevier, pp 25–47

Chikhale RV, Gupta VK, Eldesoky GE et al (2021) Identification of potential anti-TMPRSS2 natural products through homology modelling, virtual screening and molecular dynamics simulation studies. J Biomol Struct Dyn 39:6660–6675. https://doi.org/10.1080/07391102.2020.1798813

Li K, Du Y, Li L, Wei D-Q (2019) Bioinformatics approaches for anti-cancer drug discovery. Curr Drug Targets 21:3–17. https://doi.org/10.2174/1389450120666190923162203

Yu T, Cheng L, Yan X et al (2020) Systems biology approaches based discovery of a small molecule inhibitor targeting both c-Met/PARP-1 and inducing cell death in breast cancer. J Cancer 11:2656–2666. https://doi.org/10.7150/jca.40758

Aldewachi H, Al-Zidan RN, Conner MT, Salman MM (2021) High-throughput screening platforms in the discovery of novel drugs for neurodegenerative diseases. Bioengineering 8:30. https://doi.org/10.3390/bioengineering8020030

Ferdowsian HR, Beck N (2011) Ethical and scientific considerations regarding animal testing and research. PLoS ONE 6:e24059. https://doi.org/10.1371/journal.pone.0024059

Achary PGR (2020) Applications of quantitative structure-activity relationships (QSAR) based virtual screening in drug design: a review. Mini-Rev Med Chem 20:1375–1388. https://doi.org/10.2174/1389557520666200429102334

Staszak M, Staszak K, Wieszczycka K et al (2022) Machine learning in drug design: Use of artificial intelligence to explore the chemical structure–biological activity relationship. WIREs Comput Mol Sci. https://doi.org/10.1002/wcms.1568

Raies AB, Bajic VB (2016) In silico toxicology: computational methods for the prediction of chemical toxicity: computational methods for the prediction of chemical toxicity. Wiley Interdiscip Rev Comput Mol Sci 6:147–172. https://doi.org/10.1002/wcms.1240

Workman P (2003) How much gets there and what does it do?: The need for better pharmacokinetic and pharmacodynamic endpoints in contemporary drug discovery and development. Curr Pharm Des 9:891–902. https://doi.org/10.2174/1381612033455279

Bouzom F, Ball K, Perdaems N, Walther B (2012) Physiologically based pharmacokinetic (PBPK) modelling tools: how to fit with our needs?: PBPK MODELLING TOOLS. Biopharm Drug Dispos 33:55–71. https://doi.org/10.1002/bdd.1767

Mahan VL (2014) Clinical trial phases. Int J Clin Med 05:1374–1383. https://doi.org/10.4236/ijcm.2014.521175

Gold M, Amatniek J, Carrillo MC et al (2018) Digital technologies as biomarkers, clinical outcomes assessment, and recruitment tools in Alzheimer’s disease clinical trials. Alzheimers Dement Transl Res Clin Interv 4:234–242. https://doi.org/10.1016/j.trci.2018.04.003

Bennett AV, Jensen RE, Basch E (2012) Electronic patient-reported outcome systems in oncology clinical practice. CA Cancer J Clin 62:336–347. https://doi.org/10.3322/caac.21150

Gong K, Yan Y-L, Li Y et al (2020) Mobile health applications for the management of primary hypertension: a multicenter, randomized, controlled trial. Medicine (Baltimore) 99:e19715. https://doi.org/10.1097/MD.0000000000019715

Gordon S, Crager J, Howry C et al (2022) Best practice recommendations: user acceptance testing for systems designed to collect clinical outcome assessment data electronically. Ther Innov Regul Sci 56:442–453. https://doi.org/10.1007/s43441-021-00363-z

Galsky MD, Shahin M, Jia R et al (2017) Telemedicine-enabled clinical trial of metformin in patients with prostate cancer. JCO Clin Cancer Inform. https://doi.org/10.1200/CCI.17.00044

Beg S, Handa M, Shukla R et al (2022) Wearable smart devices in cancer diagnosis and remote clinical trial monitoring: transforming the healthcare applications. Drug Discov Today 27:103314. https://doi.org/10.1016/j.drudis.2022.06.014

Patel VN, Kaelber DC (2014) Using aggregated, de-identified electronic health record data for multivariate pharmacosurveillance: a case study of azathioprine. J Biomed Inform 52:36–42. https://doi.org/10.1016/j.jbi.2013.10.009

Needamangalam Balaji J, Prakash S, Park Y et al (2022) A scoping review on accentuating the pragmatism in the implication of mobile health (mHealth) technology for tuberculosis management in India. J Pers Med 12:1599. https://doi.org/10.3390/jpm12101599

Holmén C, Piehl F, Hillert J et al (2011) A Swedish national post-marketing surveillance study of natalizumab treatment in multiple sclerosis. Mult Scler J 17:708–719. https://doi.org/10.1177/1352458510394701

Antonijević Z, Beckman RA (2019) Platform trial designs in drug development: umbrella trials and basket trials. CRC Press, Boca Raton

Battaglini D, Al-Husinat L, Normando AG et al (2022) Personalized medicine using omics approaches in acute respiratory distress syndrome to identify biological phenotypes. Respir Res 23:318. https://doi.org/10.1186/s12931-022-02233-0

Poirion OB, Jing Z, Chaudhary K et al (2021) DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data. Genome Med 13:112. https://doi.org/10.1186/s13073-021-00930-x

Tran KA, Kondrashova O, Bradley A et al (2021) Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Med 13:152. https://doi.org/10.1186/s13073-021-00968-x

Turek C, Wróbel S, Piwowar M (2020) OmicsON—integration of omics data with molecular networks and statistical procedures. PLoS ONE 15:e0235398. https://doi.org/10.1371/journal.pone.0235398

Galeone C, Scelfo C, Bertolini F et al (2018) Precision medicine in targeted therapies for severe asthma: is there any place for “omics” technology? BioMed Res Int 2018:1–15. https://doi.org/10.1155/2018/4617565

Big data analytics in healthcare market. https://www.factmr.com/report/369/big-data-analytics-healthcare-market

Hemingway H, Asselbergs FW, Danesh J et al (2018) Big data from electronic health records for early and late translational cardiovascular research: challenges and potential. Eur Heart J 39:1481–1495. https://doi.org/10.1093/eurheartj/ehx487

Wang L, Alexander CA (2020) Big data analytics in medical engineering and healthcare: methods, advances and challenges. J Med Eng Technol 44:267–283. https://doi.org/10.1080/03091902.2020.1769758

Koppad S, B A, Gkoutos GV, Acharjee A, (2021) Cloud computing enabled big multi-omics data analytics. Bioinforma Biol Insights 15:1177932221. https://doi.org/10.1177/11779322211035921

Morris MA, Saboury B, Burkett B et al (2018) Reinventing radiology: big data and the future of medical imaging. J Thorac Imaging 33:4–16. https://doi.org/10.1097/RTI.0000000000000311

Ngiam KY, Khor IW (2019) Big data and machine learning algorithms for health-care delivery. Lancet Oncol 20:e262–e273. https://doi.org/10.1016/S1470-2045(19)30149-4

Ho D, Quake SR, McCabe ERB et al (2020) Enabling technologies for personalized and precision medicine. Trends Biotechnol 38:497–518. https://doi.org/10.1016/j.tibtech.2019.12.021

Sadler D, Okwuosa T, Teske AJ et al (2022) Cardio oncology: Digital innovations, precision medicine and health equity. Front Cardiovasc Med 9:951551. https://doi.org/10.3389/fcvm.2022.951551

Vajawat B, Varshney P, Banerjee D (2021) Digital gaming interventions in psychiatry: evidence. Appl Chall Psych Res 295:113585. https://doi.org/10.1016/j.psychres.2020.113585

Raijada D, Wac K, Greisen E et al (2021) Integration of personalized drug delivery systems into digital health. Adv Drug Deliv Rev 176:113857. https://doi.org/10.1016/j.addr.2021.113857

Montanhesi PK, Coelho G, Curcio SAF, Poffo R (2022) Three-dimensional printing in minimally invasive cardiac surgery: optimizing surgical planning and education with life-like models. Braz J Cardiovasc Surg. https://doi.org/10.21470/1678-9741-2020-0409

Jamróz W, Szafraniec J, Kurek M, Jachowicz R (2018) 3D printing in pharmaceutical and medical applications—recent achievements and challenges. Pharm Res 35:176. https://doi.org/10.1007/s11095-018-2454-x

Bedford J, Farrar J, Ihekweazu C et al (2019) A new twenty-first century science for effective epidemic response. Nature 575:130–136. https://doi.org/10.1038/s41586-019-1717-y

Canfell OJ, Davidson K, Woods L et al (2022) Precision public health for non-communicable diseases: an emerging strategic roadmap and multinational use cases. Front Public Health 10:256

Cubillos-Ruiz A, Guo T, Sokolovska A et al (2021) Engineering living therapeutics with synthetic biology. Nat Rev Drug Discov 20:941–960. https://doi.org/10.1038/s41573-021-00285-3

Davies JA (2016) Synthetic biology: rational pathway design for regenerative medicine. Gerontology 62:564–570. https://doi.org/10.1159/000440721

Sridhar S, Ajo-Franklin CM, Masiello CA (2022) A framework for the systematic selection of biosensor chassis for environmental synthetic biology. ACS Synth Biol 11:2909–2916. https://doi.org/10.1021/acssynbio.2c00079

Jain KK (2013) Synthetic biology and personalized medicine. Med Princ Pract 22:209–219. https://doi.org/10.1159/000341794

McNerney MP, Doiron KE, Ng TL et al (2021) Theranostic cells: emerging clinical applications of synthetic biology. Nat Rev Genet 22:730–746. https://doi.org/10.1038/s41576-021-00383-3

Zupanic A, Bernstein HC, Heiland I (2020) Systems biology: current status and challenges. Cell Mol Life Sci 77:379–380. https://doi.org/10.1007/s00018-019-03410-z

Lopatkin AJ, Collins JJ (2020) Predictive biology: modelling, understanding and harnessing microbial complexity. Nat Rev Microbiol 18:507–520. https://doi.org/10.1038/s41579-020-0372-5

Irmisch A, Bonilla X, Chevrier S et al (2021) The Tumor Profiler Study: integrated, multi-omic, functional tumor profiling for clinical decision support. Cancer Cell 39:288–293. https://doi.org/10.1016/j.ccell.2021.01.004

McEwen SC, Merrill DA, Bramen J et al (2021) A systems-biology clinical trial of a personalized multimodal lifestyle intervention for early Alzheimer’s disease. Alzheimers Dement Transl Res Clin Interv. https://doi.org/10.1002/trc2.12191

Wolstencroft K, Owen S, Krebs O et al (2015) SEEK: a systems biology data and model management platform. BMC Syst Biol 9:33. https://doi.org/10.1186/s12918-015-0174-y

Kobeissy FH, Guingab-Cagmat JD, Razafsha M et al (2011) Leveraging biomarker platforms and systems biology for rehabilomics and biologics effectiveness research. PM&R 3:S139–S147. https://doi.org/10.1016/j.pmrj.2011.02.012

Brown S-A (2015) Building SuperModels: emerging patient avatars for use in precision and systems medicine. Front Physiol. https://doi.org/10.3389/fphys.2015.00318

Cummins N, Schuller BW (2020) Five crucial challenges in digital health. Front Digit Health 2:536203. https://doi.org/10.3389/fdgth.2020.536203

Board of Governors in Supersession of the Medical Council of India (2020) Telemedicine practice guidelines-enabling registered medical practitioners to provide healthcare using telemedicine

Latifi R, Doarn CR (2020) Perspective on COVID-19: finally, telemedicine at center stage. Telemed E-Health 26:1106–1109. https://doi.org/10.1089/tmj.2020.0132

Gudi N, Lakiang T, Pattanshetty S et al (2021) Challenges and prospects in india’s digital health journey. Indian J Public Health 65:209. https://doi.org/10.4103/ijph.IJPH_1446_20

Dash S, Aarthy R, Mohan V (2021) Telemedicine during COVID-19 in India—a new policy and its challenges. J Public Health Policy 42:501–509. https://doi.org/10.1057/s41271-021-00287-w

Srivastava SK (2016) Adoption of electronic health records: a roadmap for India. Healthc Inform Res 22:261. https://doi.org/10.4258/hir.2016.22.4.261

Madanian S, Parry DT, Airehrour D, Cherrington M (2019) mHealth and big-data integration: promises for healthcare system in India. BMJ Health Care Inform 26:e100071. https://doi.org/10.1136/bmjhci-2019-100071

Al Dahdah M, Mishra RK (2023) Digital health for all: the turn to digitized healthcare in India. Soc Sci Med 319:114968. https://doi.org/10.1016/j.socscimed.2022.114968

Katyayan A, Katyayan A, Mishra A (2022) Enhancing India’s health care during COVID era: role of artificial intelligence and algorithms. Indian J Otolaryngol Head Neck Surg 74:2712–2713. https://doi.org/10.1007/s12070-020-02101-7

National Digital Health Mission. https://www.sanskritiias.com/current-affairs/national-digital-health-mission

Soman B (2014) Participatory GIS in action, a public health initiative from Kerala, India. ISPRS Int Arch Photogramm Remote Sens Spat Inf Sci XL–8:233–237. https://doi.org/10.5194/isprsarchives-XL-8-233-2014

Babu AN, Niehaus E, Shah S et al (2019) Smartphone geospatial apps for dengue control, prevention, prediction, and education: MOSapp, DISapp, and the mosquito perception index (MPI). Environ Monit Assess 191:393. https://doi.org/10.1007/s10661-019-7425-0

Chaudhary S, Soman B (2022) Spatiotemporal analysis of environmental and physiographic factors related to malaria in Bareilly district, India. Osong Public Health Res Perspect 10:10. https://doi.org/10.24171/j.phrp.2021.0304

Singh G, Mitra A, Soman B (2022) Development and use of a reproducible framework for spatiotemporal climatic risk assessment and its association with decadal trend of dengue in India. Indian J Community Med 47:50. https://doi.org/10.4103/ijcm.ijcm_862_21

Valson JS, Soman B (2017) Spatiotemporal clustering of dengue cases in Thiruvananthapuram district. Kerala’ Indian J Public Health 61:74

Sarma PS, Sadanandan R, Thulaseedharan JV et al (2019) Prevalence of risk factors of non-communicable diseases in Kerala, India: results of a cross-sectional study. BMJ Open. https://doi.org/10.1136/bmjopen-2018-027880

Ulahannan SK, Wilson A, Chhetri D et al (2022) Alarming level of severe acute malnutrition in Indian districts. BMJ Glob Health 7:e007798. https://doi.org/10.1136/bmjgh-2021-007798

Valson JS, Kutty VR, Soman B, Jissa VT (2019) Spatial clusters of diabetes and physical inactivity: do neighborhood characteristics in high and low clusters differ? Asia Pac J Public Health. https://doi.org/10.1177/1010539519879322

Mitra A, Soman B, Singh G (2021) An interactive dashboard for real-time analytics and monitoring of COVID-19 outbreak in india: a proof of Concept. arXiv:210809937Cs

Mitra A, Soman B, Gaitonde R et al (2023) Data science approaches to public health: case studies using routine health data from India

Singh G, Patrikar S, Sarma PS, Soman B (2020) Time-dependent dynamic transmission potential and instantaneous reproduction number of COVID-19 pandemic in India. medRxiv. https://doi.org/10.1101/2020.07.15.20154971

Singh G, Srinivas G, Jyothi EK et al (2020) Containing the first outbreak of COVID-19 in a healthcare setting in India: the sree chitra experience. Indian J Public Health 64:240. https://doi.org/10.4103/ijph.IJPH_483_20

Singh G, Soman B (2021) Spatiotemporal epidemiology and forecasting of dengue in the state of Punjab, India: study protocol. Spat Temp Epidemiol 39:100444. https://doi.org/10.1016/j.sste.2021.100444

Download references

Author information

Authors and affiliations.

Sree Chitra Tirunal Institute for Medical Science and Technology, Thiruvananthapuram, 695011, Kerala, India

Naresh Kasoju, N. S. Remya, Renjith Sasi, S. Sujesh, Biju Soman, C. Kesavadas, C. V. Muraleedharan, P. R. Harikrishna Varma & Sanjay Behari

You can also search for this author in PubMed   Google Scholar

Contributions

All authors listed have made an equal and substantial contribution to the work.

Corresponding author

Correspondence to Sanjay Behari .

Ethics declarations

Conflict of interest.

The authors have no relevant financial or non-financial conflicts of interest to disclose.

Rights and permissions

Reprints and permissions

About this article

Kasoju, N., Remya, N.S., Sasi, R. et al. Digital health: trends, opportunities and challenges in medical devices, pharma and bio-technology. CSIT 11 , 11–30 (2023). https://doi.org/10.1007/s40012-023-00380-3

Download citation

Received : 17 February 2023

Accepted : 27 March 2023

Published : 11 April 2023

Issue Date : April 2023

DOI : https://doi.org/10.1007/s40012-023-00380-3

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Medical devices
• Telemedicine
• Biomaterials
• Drug discovery
• Bioengineering
• Find a journal
• Publish with us
• Track your research

This paper is in the following e-collection/theme issue:

Published on 5.2.2024 in Vol 26 (2024)

Mapping Theories, Models, and Frameworks to Evaluate Digital Health Interventions: Scoping Review

Authors of this article:

• Geneviève Rouleau 1, 2, 3 , RN, PhD   ; 
• Kelly Wu 2 , MSc   ; 
• Karishini Ramamoorthi 2 , MSc   ; 
• Cherish Boxall 4 , MSc   ; 
• Rebecca H Liu 2 , PhD   ; 
• Shelagh Maloney 5 , BSc Hons   ; 
• Jennifer Zelmer 6 , MA, PhD   ; 
• Ted Scott 7 , MAppSC, PhD   ; 
• Darren Larsen 8, 9 , MD, CCFP, MPLc   ; 
• Harindra C Wijeysundera 10 , MD, PhD   ; 
• Daniela Ziegler 11 , MSI   ; 
• Sacha Bhatia 12, 13 , MD, MBA   ; 
• Vanessa Kishimoto 2 , MPH   ; 
• Carolyn Steele Gray 14, 15 , PhD   ; 
• Laura Desveaux 2, 15, 16 , MPT, PhD  

1 Nursing department, Université du Québec en Outaouais, Saint-Jérôme, QC, Canada

2 Institute for Health System Solutions and Virtual Care Toronto, Women’s College Hospital, Toronto, ON, Canada

3 Institut du Savoir Montfort, Montfort Hospital, Ottawa, ON, Canada

4 Southampton Clinical Trials Unit, University of Southampton, Southampton, United Kingdom

5 Canada Infoway, Toronto, ON, Canada

6 Healthcare Excellence Canada, Ottawa, ON, Canada

7 School of Nursing, Hamilton Health Sciences, McMaster University, Hamilton, ON, Canada

8 Telus Healthcare Delivery, Women's College Hospital, Toronto, ON, Canada

9 Women's College Hospital Family Health Team, Women's College Hospital, Toronto, ON, Canada

10 Schulich Heart Program, Sunnybrook Health Sciences Centre, Toronto, ON, Canada

11 Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada

12 Ontario Health, Toronto, ON, Canada

13 Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada

14 Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada

15 Institute for Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada

16 Institute for Better Health, Trillium Health Partners, Toronto, ON, Canada

Corresponding Author:

Geneviève Rouleau, RN, PhD

Nursing department

Université du Québec en Outaouais

5, rue Saint-Joseph

Saint-Jérôme, QC, J7Z 0B7

Phone: 1 819 595 3900

Email: [email protected]

Background: Digital health interventions (DHIs) are a central focus of health care transformation efforts, yet their uptake in practice continues to fall short of their potential. In order to achieve their desired outcomes and impact, DHIs need to reach their target population and need to be used. Many factors can rapidly intersect between this dynamic of users and interventions. The application of theories, models, and frameworks (TMFs) can facilitate the systematic understanding and explanation of the complex interactions between users, practices, technology, and health system factors that underpin research questions. There remains a gap in our understanding of how TMFs have been applied to guide the evaluation of DHIs with real-world health system operations.

Objective: This study aims to map TMFs used in studies to guide the evaluation of DHIs. The objectives are to (1) describe the TMFs and the constructs they target, (2) identify how TMFs have been prospectively used (ie, their roles) in primary studies to evaluate DHIs, and (3) to reflect on the relevance and utility of our findings for knowledge users.

Methods: This scoping review was conducted in partnership with knowledge users using an integrated knowledge translation approach. We included papers (eg, reports; empirical quantitative, qualitative, and mixed methods studies; conference proceedings; and dissertations) if primary insights resulting from the application of TMFs were presented. Any type of DHI was eligible. Papers published from 2000 and onward were mainly identified from the following databases: MEDLINE (Ovid), CINAHL Complete (EBSCOhost), PsycINFO (Ovid), EBM Reviews (Ovid), and Embase (Ovid).

Results: A total of 156 studies published between 2000 and 2022 were included. A total of 68 distinct TMFs were identified across 85 individual studies. In more than half (85/156, 55%) of the included studies, 1 of following 6 prevailing TMFs were reported: Consolidated Framework for Implementation Research (n=39); the Reach, Effectiveness, Adoption, Implementation, and Maintenance Framework (n=17); the Technology of Acceptance Model (n=16); the Unified Theory on Acceptance and Use of Technology (n=12); the Diffusion of Innovation Theory (n=10); and Normalization Process Theory (n=9). The most common intended roles of the 6 TMFs were to inform data collection (n=86), to inform data analysis (n=69), and to identify key constructs that may serve as barriers and facilitators (n=52).

Conclusions: As TMFs are most often reported to be applied to support data collection and analysis, researchers should consider more clearly synthesizing key insights as practical use cases to both increase the relevance and digestibility of their findings. There is also a need to adapt or develop guidelines for better reporting DHIs and the use of TMFs to guide evaluation. Hence, it would contribute to ensuring ongoing technology transformation efforts are evidence and theory informed rather than anecdotally driven.

Introduction

Digital health interventions (DHIs) are a central focus of health care transformation efforts worldwide [ 1 - 4 ], yet their uptake in practice continues to fall short of their potential [ 5 - 8 ]. DHIs are complex interventions with multiple components that fulfill a range of functionalities such as supporting communication, decision-making, documentation and maintenance of patient records, diagnosis, and access to therapies. They target a range of patients, health care providers (HCPs), and health system users and are deployed in a variety of settings (eg, hospital, community, and home) [ 9 ] in hopes of delivering on the Quadruple Aim [ 10 , 11 ]. The Quadruple Aim is intended to improve population health, patients’ and caregivers’ experiences, and providers’ experience and to reduce costs. To achieve the desired outcomes, the ideal first step would involve the “determination and optimisation of reach and uptake by the intended population, in the context in which the DHI will be used” [ 10 ]. In reality, and at an increasing rate, DHIs are implemented in practice in the paucity of fulsome evidence of their effect; studies being limited to pilot or feasibility ones [ 12 ]. This is partly a product of the timelines of traditional research and the rapid pace of technology progress [ 13 ]. As a result, evaluations of DHIs may seek to answer various research questions about their effectiveness, associated implementation outcomes [ 10 , 14 ], or both at different stages of the research cycle. For instance, it is appropriate to evaluate the feasibility of DHIs by focusing on implementation outcomes such as reach, adoption, practicability, and acceptability, as well as to determine the impacts of DHIs components on the expected outcomes [ 10 , 15 ]. We refer to evaluation throughout this paper in this broad sense, consistent with our previous work [ 16 ], encompassing the systematic assessment of an intervention’s design, implementation, and outcomes that can judge merit, worth, or significance by combining evidence and values [ 17 , 18 ]. The term “evaluation” is then inclusive of various evaluation activities, different types of evaluation (eg, process evaluation, implementation evaluation, as well as impact and outcome evaluation) purposes, and research questions [ 17 , 18 ].

In reality, a variety of other factors influence the successful (or failed) implementation of DHIs [ 19 ], including but not limited to funding structure, policy, organizational settings, the complex interactions between users, existing routines and processes, the value proposition, and the technology itself [ 20 ]. One way of facilitating the systematic understanding and explanation of the complex interactions between users, practices, technology, and health system factors that underpin research questions [ 20 , 21 ] is to use theories, models, and frameworks (TMFs). There is a wide range of TMFs that have been used in studies of knowledge translation [ 22 , 23 ], and implementation science [ 24 ] (examples of more than 40 TMFs are cited). Heinsch et al [ 24 ] identified 36 theories for informing and explaining eHealth implementation, and Greenhalgh et al [ 20 ] identified 28 technology-specific implementation frameworks. In their systematic review, Bashi et al [ 12 ] identified 11 evaluation frameworks applied in the management of chronic diseases. The authors of those works use either the terms “implementation frameworks” or “evaluation frameworks.” Despite this body of knowledge on TMFs, there remains a gap in our understanding of how TMFs have been and could be applied to guide the reported prospective evaluation of DHIs with real-world health system operations.

The aim of this work was to map the TMFs used in studies to evaluate DHIs. Specifically, our objectives were to (1) describe the TMFs and the constructs they target, (2) identify how TMFs have been used in primary studies (hereafter referred to as the roles of the TMFs), and (3) reflect on the relevance and utility of our findings for knowledge user partners as a post hoc objective. A scoping review was the suited knowledge synthesis approach to map in a comprehensive way the current state of evidence in this given area and to cover a breadth of the literature [ 25 , 26 ].

Protocol and Registration

We conducted a scoping review, informed by the Joanna Briggs Institute methodology [ 27 ]. The protocol has been published previously [ 28 ]. We used the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist to inform reporting [ 29 ].

Integrated Knowledge Translation Approach

This work was conducted in partnership with knowledge users using an integrated knowledge translation strategy [ 30 , 31 ]. The aim of the strategy was to inform the objectives and approach, develop a shared understanding of the findings, and work with knowledge users to understand how the resulting knowledge could be synthesized to support its application in practice. The knowledge users were identified by the leadership of the Centre for Digital Health Evaluation and, especially, by its director and scientific lead at the time of the initiation of the study. Only 1 person out of 7 declined the email invitation. The advisory panel included senior leaders (DL, HCW, SM, JZ, TS, and SB), policy makers (JZ and SB), a researcher (CSG), clinicians (DL, HCW, and SB), and a DHI developer (DL) who are involved in health decision making regarding the evaluation of DHIs. The knowledge users advisory panel provided input to the protocol of the scoping review [ 28 ], supported the refinement of the eligibility criteria of included papers, identified relevant data abstraction elements to prioritize, and assisted in the interpretation of findings. Furthermore, as a mechanism of reflexivity [ 32 ], they shared their vision and experience about using TMFs in their respective context of work. Consequently, it helped to inform the results. Details regarding knowledge users’ profiles, area of expertise, and their application of TMFs are available in Multimedia Appendix 1 . Two newsletters were sent out to inform knowledge users about progress updates and upcoming activities (eg, titles and abstracts screening and data extraction). Knowledge users participated in 6 meetings over Zoom (Zoom Video Communications) between March 2021 and December 2022, with each meeting lasting 60 minutes. Figure 1 illustrates the timeline of the meetings, the topics covered, and some examples of questions discussed.

Critical Appraisal of Individual Sources of Evidence

Given that the purpose of our scoping review was to provide a broad overview of the TMFs used in relation to DHIs and not to recommend evidence to guide clinical practice, the methodological quality of included studies was not assessed; this was prespecified in the protocol [ 28 ] and was still in line with methodological guidance [ 27 ].

Eligibility Criteria

To enhance translation into practice, the eligibility criteria focused on papers in which authors reported having used TMFs prospectively to guide DHI evaluation. Retrospective application of TMFs was excluded. We were interested in understanding how TMFs can be prospectively used when undertaking a theoretical-based evaluation of DHIs.

Type of Interventions

DHIs are defined as the use of various digital technologies (eg, eHealth, telemedicine, patient remote monitoring, smartphone apps, patient sensors, and artificial intelligence) to improve health care delivery [ 33 ]. We included papers that reported on a single DHI and excluded those in which a collection or suite of DHIs was presented (eg, variety of electronic medical records) and those in which DHIs were described broadly (eg, eHealth systems having no identified features or components). This exclusion criterion was mainly decided to narrow down the number of included studies in order to increase the feasibility of performing the knowledge synthesis with the constrained resources we had.

Type of Literature

We included peer-reviewed quantitative, qualitative, and mixed methods empirical studies reporting on the application of TMFs to prospectively guide the evaluation of DHIs in health care. Published gray literature, including conference abstracts or proceedings, dissertations, reports, and white papers, have been included if primary insights resulting from the application of TMFs were presented. Reviews, study protocols, commentaries, and letters to the editor were excluded as they had no primary data.

Type of Participants

No limitations were placed on the user population as long as the evaluation of DHIs in a health care context was described.

Type of TMFs

We included studies that pursued different research questions in which TMFs were applied to guide the evaluation of DHIs. We excluded papers that described the theoretical underpinnings and the overall process of intervention development. We made this decision at the early stage of the scoping review given the abundance of papers focusing on the intervention development; and the limited resources we had in completing the knowledge synthesis.

Information Sources and Search Strategy

The following electronic databases were searched: MEDLINE (Ovid), CINAHL Complete (EBSCOhost), PsycINFO (Ovid), EBM Reviews (Ovid), and Embase (Ovid). A complementary search of Google Scholar was conducted to identify relevant studies. Search strategies were designed by a librarian (DZ) and were peer-reviewed by another senior information specialist prior to execution using the PRESS Checklist [ 34 ]. We imposed no language restrictions and the search extended to studies published in 2000 and onward. The search was initially run on March 10, 2021, and then updated on March 16, 2022. The full electronic search strategy is presented in Multimedia Appendix 2 . Due to resource constraints and delays introduced by the pandemic, we did not apply supplementary search strategies as planned in the protocol (ie, checking the reference lists of included studies, and conducting a forward citation search).

Eligibility Screening Process

Citations obtained from the literature were stored in Endnote (Version X9; Clarivate) [ 35 ] and then uploaded to Covidence (Veritas Health Information) [ 36 ], a web-based collaboration software platform that streamlines the production of systematic and other literature reviews. This software allows multiple reviewers to participate in various stages of the review (ie, screening titles, abstracts, and full texts and identifying discrepancies). We applied a 2-step process for identifying relevant citations. At stage 1, titles and abstracts were independently assessed by 5 reviewers (RHL, GR, KR, CB, and VK). Studies with abstracts fulfilling the criteria were passed to level 2 full-text screening. At this stage, each full text was reviewed by 1 person (a total of 7 team members: RHL, GR, KR, VK, SM, CB, and KW). All reviewers flagged full texts they were unsure about, and these were validated by a second reviewer. A pilot test of the screening strategy was completed using a random sample of 10% of citations and full-text papers prior to full implementation, with the expressed purpose of assessing agreement between reviewers at each level (interrater reliability ≥80% was considered adequate). When agreement was not reached, a third reviewer (RHL and GR) mediated any disagreements.

Data Extraction Process and Data Items

Studies fulfilling the eligibility criteria were extracted in Microsoft Excel. The following study characteristics were collected: reference, country of origin of the first author, study design, and DHI user. We also extracted data specific to the TMFs, including name; constructs; variables or mechanisms; and roles of framework in the study, that is, how it has been applied in research. We pilot-tested the data extraction form by extracting data from the same study as a team and iteratively adapted the form. We had biweekly working meetings to discuss the process, highlight challenges, and identify strategies to mitigate those challenges.

Data Synthesis

We undertook a descriptive quantitative and qualitative data analysis. First, we did simple frequency counts in line with Joanna Briggs Institute guidance [ 27 ] of interventions, frameworks, and roles of the TMFs. Descriptive qualitative content analysis involved categorizing 2 sets of data: DHI users and roles of frameworks. We used the World Health Organization taxonomy [ 37 ] to categorize the DHIs according to their primary targeted users: (1) clients (potential or actual users of health services, including caregivers), (2) HCPs (deliverers of health services), (3) health system managers (“involved in the administration and oversight of public health systems”), and (4) data services (crosscutting functionality supporting various activities focusing on data collection, management, use, and exchange). A single DHI could be categorized in more than 1 domain (eg, it can target both clients and HCPs). We coded TMF roles according to the use classifications outlined by Birken et al [ 38 ] ( Textbox 1 ).

The knowledge users advisory panel informed the synthesis of findings, including the level of detail abstracted from included papers and the approach to DHI classification.

• To identify key constructs that may serve as barriers and facilitators
• To inform data collection
• To guide implementation planning
• To enhance conceptual clarity
• To specify the process of implementation
• To frame an evaluation
• To inform data analysis
• To guide the selection of implementation strategies
• To specify outcomes
• To clarify terminology
• To convey the larger context of the study
• To specify hypothesized relationships between the constructs

Search Results

A total of 10,567 titles or abstracts were identified from the 5 databases, Google Scholar, and other methods, from which 3192 were removed in EndNote by the librarian (DZ). After removing duplicate references, 7375 titles or abstracts were assessed for eligibility. Of these, 6561 papers were excluded based on title and abstract screening and application of the eligibility criteria previously outlined. A total of 814 full-text papers were sought and screened, and 658 were excluded. The list of excluded studies is presented in Multimedia Appendix 3 . Between 2000 and 2022, a total of 156 published papers met the eligibility criteria. The list of these included papers is presented in Multimedia Appendix 4 . The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) study flow diagram [ 39 ] is illustrated in Figure 2 to show the overall process of review selection.

Study Characteristics

The majority of papers were published by researchers in the United States (n=64), Canada (n=19), the United Kingdom (n=14), the Netherlands (n=12), and Australia (n=7). Study designs were largely qualitative (n=63) and mixed methods (n=62), with a smaller number of quantitative studies (n=31). Most DHIs targeted either HCPs (n=67) or clients (n=63), with a few targeting health system managers (n=8), data services (n=3), and a combination of users (n=15).

Identification of Theories and the Most Reported TMFs

In total, 68 distinct TMFs were identified (see Multimedia Appendix 5 ) across 85 individual studies. More than half (85/156, 55%) of included studies used 1 of 6 TMFs, which included the CFIR (Consolidated Framework for Implementation Research; 39 studies), the RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance Framework; 17 studies), the TAM (Technology of Acceptance Model; 16 studies), the UTAUT (Unified Theory on Acceptance and Use of Technology; 12 studies), the DOI (Diffusion of Innovation Theory; 10 studies), and the NPT (Normalization Process Theory; 9 studies). It should be noted that the number of studies across the 6 TMFs is 103; however, because TMFs are used in combination with other theoretical approaches ( Figure 3 ), this number represents 85 individual studies. UTAUT is the theory most frequently used in combination with other TMFs. A descriptive table of those studies that includes references, DHI user, study type, TMF used in combination, and roles of TMFs is presented in Multimedia Appendix 6 . Our results focus on synthesizing insights across the 6 prevailing TMFs, being reported in 9 studies or more, allowing us to synthesize their application across different studies and contexts of evaluation. The constructs of the prevailing TMFs are described and summarized in the Multimedia Appendix 7 .

The most common intended roles of the 6 TMFs were to inform data collection (n=86), to inform data analysis (n=69), to identify key constructs that may serve as barriers and facilitators (n=52), to organize and report the study findings (n=47), and to frame an evaluation (n=18; see Table 1 ). TMFs were applied to pursue various roles, that is, they served multiple purposes. The average number of distinct roles per TMFs is as follows: RE-AIM (n=3), CFIR (n=2.9), NPT (n=2.56), DOI (n=1.9), UTAUT (n=1.75), and TAM (n=1.69).

a TMF: theory, model, and framework.

b CFIR: Consolidated Framework for Implementation Research.

c RE-AIM: Reach, Effectiveness, Adoption, Implementation, and Maintenance Framework.

d TAM: Technology of Acceptance Model.

e UTAUT: Unified Theory on Acceptance and Use of Technology.

f DOI: Diffusion of Innovation Theory.

g NPT: Normalization Process Theory.

DHIs and Intended Users Associated With Top 6 Frameworks

DHIs targeting clients and HCPs are the most frequently reported (see Table 2 ). RE-AIM and DOI were used for DHIs designed for clients, while CFIR, TAM, UTAUT, and NPT have been used primarily with DHIs involving HCPs. DHIs targeting clients included patient portals [ 40 , 41 ], web-based self-management interventions [ 42 , 43 ], and mobile health diet apps [ 44 ]. DHIs targeting HCPs included (but are not limited to) mobile apps targeting patients’ smoking cessation [ 45 ] and medication adherence counseling [ 46 , 47 ], telemedicine and telehealth [ 48 , 49 ], cancer prevention decision support tools [ 50 ], e-consultation between primary care providers and specialty care expertise [ 51 , 52 ], and e-learning for dementia caregiver education [ 53 ]. Two studies targeted health system managers and an information system for case-based surveillance [ 54 ] and a patient-reported outcome data collection system [ 55 ]. Two studies focused on data services including big data analytics [ 56 ], the former being reported in combination with clients, and an electronic patient falls reporting system [ 57 ].

a DHI: digital health intervention.

b HCPs: health care providers.

c CFIR: Consolidated Framework for Implementation Research.

d RE-AIM: Reach, Effectiveness, Adoption, Implementation, and Maintenance Framework.

e TAM: Technology of Acceptance Model.

f UTAUT: Unified Theory on Acceptance and Use of Technology.

h DOI: Diffusion of Innovation Theory.

Current Gaps Between Prevailing TMFs Used in Research and in Practice by Knowledge Users

The knowledge users reflected that most of the prevailing TMFs identified in this scoping review were not familiar to them. They have used different TMFs in practice (see Multimedia Appendix 1 ) that reflect their interest in capturing the process of implementation and for outcome-driven evaluation approaches that would help them understand whether DHIs work or not. One first example is the NPT, applied to implement and evaluate the effectiveness of the electronic patient-reported outcome (ePRO) mobile app and portal system. ePRO was designed to enable goal-oriented care delivery in interprofessional primary care practices [ 58 ]. In this study, many types of outcomes were of interest to produce early evidence of effectiveness (or ineffectiveness) of the ePRO and its mechanisms of action: the context (eg, sociodemographic data and barriers of adopting ePRO), process (eg, usability), and outcome measures (eg, patients’ quality of life, provider-level effectiveness in delivering care to patients with chronic illness). The Quadruple Aim [ 59 ] was also used by 5 knowledge users to evaluate the impact of DHIs on health system performance with outcomes such as equitable access, cost reduction, patient-provider relationships, providers’ burnout, and work-life balance [ 11 ]. The Benefits Evaluation Framework [ 60 ] was also well-known and used by knowledge users. Similarly to TAM and UTAUT, it aims to describe factors influencing eHealth success (eg, system quality, information quality, and user satisfaction), with the addition of the resulting impacts (or outcomes) of DHIs in terms of care quality (eg, effectiveness and health outcomes), access services, and productivity (eg, efficiency).

Principal Findings

While a wide range of TMFs (n=68) have been used to guide the evaluation of DHIs, 6 main TMFs are used consistently by researchers. These TMFs were used in a variety of roles and were broadly applied across types of DHI and target user groups, demonstrating their flexibility in academic practice. These 6 TMFs were not commonly used by nor familiar to many of the knowledge users, highlighting the disconnect between academic and health system practice. Our discussion presents the 3 key insights from these conversations in relation to our results: specifically, how the application of prevailing TMFs in the literature could be used in health system decision-making, how to bridge the persistent gap between academic knowledge and health system practice, and lessons learned about how future work might bridge this gap.

Insights From the Application of Prevailing TMFs

The findings allowed us to identify a higher number of TMFs (n=68) than those reported previously by Heinsch et al [ 24 ] (n=36) and Greenhalgh et al [ 20 ] (n=28). Our findings corroborate the ones in Heinsh et al [ 24 ], in which 5 of our prevailing TMFs (except RE-AIM) have been identified. Furthermore, at least 3 TMFs (TAM, DOI, and NPT) identified in our review were cited as a groundwork for technology implementation frameworks as identified by Greenhalgh et al [ 20 ]. While this highlights the variability of TMFs used in the evaluation process of DHIs, they are most often used to inform data collection and data analysis, aligning with the findings of Birken et al [ 38 ].

While different types of outcomes were of interest to knowledge users deriving from their use of TMFs (see Multimedia Appendix 1 ), that is, service (eg, efficiency or cost, effectiveness, and access to care), client (eg, patients’ quality of life), and implementation (eg, adoption and sustainability), as aligned with the literature [ 15 ], only RE-AIM included an explicit effectiveness outcome domain. Recently, CFIR has been extended to include implementation outcomes and innovation (ie, intervention) outcomes as part of CFIR 2.0 [ 61 , 62 ]. CFIR 2.0 outcomes are inclusive of both purchase and operating costs (the innovation cost) [ 61 ]—an element that is central to decision-making within resource-constrained systems [ 63 ]. As an example, the perceived advantage of a mobile app from the perspective of HCPs (an implementation determinant) may impact their uptake and referral rate to their patients (an implementation outcome). This is distinct from patient motivation to use the app (an innovation determinant) which will impact weight loss (an innovation outcome). These distinct categories were included to focus attention “squarely on the way that context shapes intermediate results and conditions, such as user acceptance, which in turn influence classic measures of an intervention’s ultimate aims or outcomes” [ 64 ]. This highlights the need to consider a chain of short-term proxy outcomes (eg, acceptability of DHIs and adaptation to novel contexts), including the attributes of context [ 65 ], if we want to capture the likely benefit of the DHIs [ 10 ]. This would help to address the disconnect between (less) attention paid to context in comparison with effectiveness outcomes [ 10 , 65 ].

Bridging the Gap Between Academic and Health System Practice

Our work echoes the opportunity for researchers to better understand the realities of health care practice and operations [ 66 ]. This can be achieved by understanding the context in which knowledge users operate, their values and professional experience from the early beginning of the project [ 67 , 68 ], and assessing the usefulness of TMFs in supporting their routine decision-making. Relatedly, there is an opportunity for researchers to support knowledge users in understanding how to leverage insights from the literature to better achieve their desired outcomes. Greenhalgh et al [ 20 ] observed a tendency across DHI implementations “to assume the issues to be addressed were simple or complicated (hence knowable, predictable, and controllable) rather than complex (that is, inherently not knowable or predictable but dynamic and emergent).” Explicitly highlighting how TMFs can mitigate the inherent challenges that knowledge users face in evaluating interventions may help to address this gap.

Our knowledge user panel recommended presenting the TMFs as practical use cases to illustrate their real-world application potential—specifically in nonacademic settings, where implementation and evaluation activities are part of routine operations. The knowledge users shared an interest in TMFs that could support scaling up DHIs and understanding their effectiveness and impact on patient outcomes. Through our discussion with the advisory panel, the CFIR and RE-AIM frameworks were identified as aligning with the dual purpose of guiding the implementation effectiveness (CFIR), as well as scalability and sustainability (RE-AIM) [ 69 ]. The use cases were constructed to highlight the utility of the TMFs as well as to demonstrate how and to what end they have been used in research ( Multimedia Appendices 8 and 9 ).

Lessons Learned and Limitations

A natural evolution of this work would be to provide knowledge users with an easy-to-use tool to select a TMF that aligns with their operational needs and local context. The Theory, Model, and Framework Comparison and Selection Tool [ 70 ] can help scientists and practitioners select the most appropriate TMF to meet their needs and realize the potential that a given DHI may, or may not, bring in its intended context. Users are directed to a web tool and repository of TMFs [ 71 ] which includes a tutorial for novice users and guidance on how to address their research or practice questions.

Despite the desire among our team to classify DHIs according to their primary function (eg, to communicate with clients and to transmit information), we were constrained by the variability in how DHI-related information was reported. A standardized reporting structure inclusive of DHI function, setting, target users, and intended outcomes would help to facilitate learning across systems and studies as health care becomes increasingly technology enabled. Guideline for reporting evidence-based practice educational interventions and teaching [ 72 ] and the Template for Intervention Description and Replication checklist [ 73 ] are both used to better report interventions. However, adapting those guidelines to the specificities of DHIs would be valuable in future work. In addition, Krick et al [ 74 ] developed a comprehensive digital nursing technology outcome framework that allows the identification of effective outcomes. This outcome framework can be a good starting from which other types of outcomes (such as implementation) can be added. This would potentially address the desire of knowledge users to understand effectiveness outcomes at a categorical level (eg, the effectiveness of DHIs by functional category or setting), which we were unable to achieve due to the heterogeneity of outcomes and terminology used to describe DHIs.

While this work engaged knowledge users from the study conception, our search strategy did not capture the Benefits Evaluation Framework—the primary framework they used in practice despite its application in more than 50 organizationally-led evaluations [ 75 ]. This limited our ability to systematically compare TMFs routinely used in the academic literature with those routinely used in practice, which is likely to provide further insights into how knowledge users collect, synthesize, report, and digest evaluation insights. Future integrated knowledge translation projects would benefit from investing time upfront to better understand how knowledge users and team members approach their work and which resources and tools they rely on to ensure research is better positioned to address persistent gaps between academic knowledge to operational practice. Another limitation in the process is that we used the information as reported by the authors to classify the roles of TMFs. We did not interpret the various roles, such as “to convey the larger context of the study” or “to frame an evaluation.” Simply put, if authors did not clearly report their intended purposes for using TMFs, we did not extract the information. Hence, we did not explore to what extent the claimed theory was used. Birken et al [ 38 ] highlight that providing guidance for theory selection may encourage implementation scientists to use theories in a meaningful way and discourage superficial use and misuse. Our findings pointed out that the prevailing TMFs were used in combination with other TMFs: adding to the challenges of aligning and using meaningfully the use of multiple TMFs. Reporting guidelines for the use of TMFs to guide evaluation would be an avenue for future research.

Conclusions

The findings of this scoping review illustrate the range of TMFs applied to support the evaluation of a breadth of DHIs. As TMFs are most often applied to support data collection and analysis, researchers should consider more clearly synthesizing key insights as practical use cases to both increase the relevance and digestibility of their findings. The opportunity to develop a standardized reporting structure inclusive of DHI function, setting, target users, and intended outcomes is quickly becoming a crucial need to ensure ongoing technology transformation efforts are evidence informed rather than anecdotally driven. Finally, guidance on how to effectively report the use of TMFs to guide evaluation would also be needed.

Acknowledgments

This work was supported by the Centre for Digital Health Evaluation and by research funds of LD. GR was granted a Fonds de recherche du Québec Santé postdoctoral scholarship at the time of this review (#302821). GR had protected time to lead and work on that scoping review. The authors would like to acknowledge the work of those people who took part in the activities at different stages of the project: Simon Minich, Abigail Appiahene-Affriyie, Marlena Dang Nguyen, Meagan Lacroix, and Julie Vo.

The publication costs for this article have been covered by Canada Health Infoway Inc, a non-profit corporation funded by the Government of Canada. The funders had no role in study design, data collection, and analysis, or decision to publish the manuscript.

Conflicts of Interest

None declared.

Knowledge users profiles and their use of theories, models, and frameworks.

Full search strategy.

List of excluded papers.

List of included studies.

List of all theories, models and frameworks.

Descriptive table most prevailing theories, models and frameworks.

Constructs of prevailing theories, models, and frameworks.

Use cases of the Consolidated Framework for Implementation Research.

Use cases of the Reach, Effectiveness, Adoption, Implementation, and Maintenance Framework.

PRISMA-ScR checklist.

• Notice: Health Canada's approach to digital health technologies. Government of Canada. 2018. URL: https:/​/www.​canada.ca/​en/​health-canada/​services/​drugs-health-products/​medical-devices/​activities/​announcements/​notice-digital-health-technologies.​html [accessed 2021-10-02]
• Delivering 21st century IT support for the NHS. Department of Health. United Kingdom. Department of Health; 2002. URL: https:/​/webarchive.​nationalarchives.gov.uk/​ukgwa/​20130107105354/​http:/​/www.​dh.gov.uk/​prod_consum_dh/​groups/​dh_digitalassets/​@dh/​@en/​documents/​digitalasset/​dh_4067112.​pdf [accessed 2024-01-03]
• Walsh M, Chipperfield A. Australian national telehealth think tank. J Telemed Telecare. 2000;6(6):353. [ FREE Full text ] [ CrossRef ] [ Medline ]
• WHO guideline: recommendations on digital interventions for health system strengthening. World Health Organization. 2019. URL: https://www.who.int/publications/i/item/9789241550505 [accessed 2021-10-02]
• de Bont A, Bal R. Telemedicine in interdisciplinary work practices: on an IT system that met the criteria for success set out by its sponsors, yet failed to become part of every-day clinical routines. BMC Med Inform Decis Mak. 2008;8:47. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Kuipers P, Humphreys JS, Wakerman J, Wells R, Jones J, Entwistle P. Collaborative review of pilot projects to inform policy: a methodological remedy for pilotitis? Aust N Z Health Policy. 2008;5:17. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Wyatt JC, Sullivan F. eHealth and the future: promise or peril? BMJ. 2005;331(7529):1391-1393. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Sundin P, Callan J, Mehta K. Why do entrepreneurial mHealth ventures in the developing world fail to scale? J Med Eng Technol. 2016;40(7-8):444-457. [ CrossRef ] [ Medline ]
• Krick T, Huter K, Domhoff D, Schmidt A, Rothgang H, Wolf-Ostermann K. Digital technology and nursing care: a scoping review on acceptance, effectiveness and efficiency studies of informal and formal care technologies. BMC Health Serv Res. 2019;19(1):400. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Murray E, Hekler EB, Andersson G, Collins LM, Doherty A, Hollis C, et al. Evaluating digital health interventions: key questions and approaches. Am J Prev Med. 2016;51(5):843-851. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Bhatti S, Dahrouge S, Muldoon L, Rayner J. Using the quadruple aim to understand the impact of virtual delivery of care within Ontario community health centres: a qualitative study. BJGP Open. 2022;6(4):BJGPO.2022.0031. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Bashi N, Fatehi F, Mosadeghi-Nik M, Askari MS, Karunanithi M. Digital health interventions for chronic diseases: a scoping review of evaluation frameworks. BMJ Health Care Inform. 2020;27(1):e100066. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Marcolino MS, Oliveira JAQ, D'Agostino M, Ribeiro AL, Alkmim MBM, Novillo-Ortiz D. The impact of mHealth interventions: systematic review of systematic reviews. JMIR Mhealth Uhealth. 2018;6(1):e23. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Hrynyschyn R, Prediger C, Stock C, Helmer SM. Evaluation methods applied to digital health interventions: what is being used beyond randomised controlled trials?-a scoping review. Int J Environ Res Public Health. 2022;19(9):5221. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Proctor E, Silmere H, Raghavan R, Hovmand P, Aarons G, Bunger A, et al. Outcomes for implementation research: conceptual distinctions, measurement challenges, and research agenda. Adm Policy Ment Health. 2011;38(2):65-76. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Brual J, Rouleau G, Fleury C, Strom M, Koshy M, Rios P, et al. The Pan-Canadian digital health evaluation framework and toolkit: final report (version 1.0). Canadian Network for Digital Health Evaluation. 2022. URL: https://cndhe.womenscollegehospital.ca/network/conceptual-model/ [accessed 2024-01-03]
• What is evaluation? Better Evaluation. 2022. URL: https://www.betterevaluation.org/getting-started/what-evaluation [accessed 2023-11-03]
• Treasury HM. The Green Book: Central Government Guidance on Appraisal and Evaluation. UK. OGL Press; 2022.
• Ross J, Stevenson F, Lau R, Murray E. Factors that influence the implementation of e-health: a systematic review of systematic reviews (an update). Implement Sci. 2016;11(1):146. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Greenhalgh T, Wherton J, Papoutsi C, Lynch J, Hughes G, A'Court C, et al. Beyond adoption: a new framework for theorizing and evaluating nonadoption, abandonment, and challenges to the scale-up, spread, and sustainability of health and care technologies. J Med Internet Res. 2017;19(11):e367. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Venkatesh V, Bala H. Technology acceptance model 3 and a research agenda on interventions. Decision Sci. 2008;39(2):273-315. [ CrossRef ]
• Colquhoun HL, Letts LJ, Law MC, MacDermid JC, Missiuna CA. A scoping review of the use of theory in studies of knowledge translation. Can J Occup Ther. 2010;77(5):270-279. [ CrossRef ] [ Medline ]
• Esmail R, Hanson HM, Holroyd-Leduc J, Brown S, Strifler L, Straus SE, et al. A scoping review of full-spectrum knowledge translation theories, models, and frameworks. Implement Sci. 2020;15(1):11. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Heinsch M, Wyllie J, Carlson J, Wells H, Tickner C, Kay-Lambkin F. Theories informing eHealth implementation: systematic review and typology classification. J Med Internet Res. 2021;23(5):e18500. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Arksey H, O'Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005;8(1):19-32. [ CrossRef ]
• Munn Z, Pollock D, Khalil H, Alexander L, Mclnerney P, Godfrey CM, et al. What are scoping reviews? providing a formal definition of scoping reviews as a type of evidence synthesis. JBI Evid Synth. 2022;20(4):950-952. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Peters MDJ, Marnie C, Tricco AC, Pollock D, Munn Z, Alexander L, et al. Updated methodological guidance for the conduct of scoping reviews. JBI Evid Synth. 2020;18(10):2119-2126. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Soobiah C, Cooper M, Kishimoto V, Bhatia RS, Scott T, Maloney S, et al. Identifying optimal frameworks to implement or evaluate digital health interventions: a scoping review protocol. BMJ Open. 2020;10(8):e037643. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Tricco AC, Lillie E, Zarin W, O'Brien KK, Colquhoun H, Levac D, et al. PRISMA extension for Scoping Reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. 2018;169(7):467-473. [ FREE Full text ] [ CrossRef ] [ Medline ]
• A guide to researcher and knowledge-user collaboration in health research. Canadian Institutes of Health Research. 2012. URL: https://cihr-irsc.gc.ca/e/44954.html [accessed 2022-11-18]
• Guide to knowledge translation planning at CIHR: integrated and end-of-grant approaches. Canadian Institutes of Health Research. 2012. URL: https://cihr-irsc.gc.ca/e/45321.html#a3 [accessed 2022-11-18]
• Alley S, Jackson SF, Shakya YB. Reflexivity: a methodological tool in the knowledge translation process? Health Promot Pract. 2015;16(3):426-431. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Snowdon A. Digital health: a framework for healthcare transformation. Healthcare Information and Management Systems Society. 2020. URL: https://www.himss.org/resources/digital-health-framework-healthcare-transformation-white-paper [accessed 2021-08-30]
• McGowan J, Sampson M, Salzwedel DM, Cogo E, Foerster V, Lefebvre C. PRESS peer review of electronic search strategies: 2015 guideline statement. J Clin Epidemiol. 2016;75:40-46. [ FREE Full text ] [ CrossRef ] [ Medline ]
• EndNote Version X9. Philadelphia, PA. Clarivate Version; 2013. URL: https:/​/support.​clarivate.com/​Endnote/​s/​article/​Citing-the-EndNote-program-as-a-reference?language=en_US [accessed 2024-01-03]
• Covidence. Veritas Health Innovation. Melbourne, Australia.; 2022. URL: https://www.covidence.org/ [accessed 2024-01-03]
• WHO | Classification of digital health interventions v1.0. World Health Organization. WHO. World Health Organization URL: https://www.who.int/publications/i/item/WHO-RHR-18.06 [accessed 2021-10-02]
• Birken SA, Powell BJ, Shea CM, Haines ER, Kirk MA, Leeman J, et al. Criteria for selecting implementation science theories and frameworks: results from an international survey. Implement Sci. 2017;12(1):124. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JPA, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. PLoS Med. 2009;6(7):e1000100. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Emani S, Peters E, Desai S, Karson AS, Lipsitz SR, LaRocca R, et al. Who adopts a patient portal?: an application of the diffusion of innovation model. J Innov Health Inform. 2018;25(3):149-157. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Lalitaphanit K, Theeraroungchaisri A. Factors affecting community pharmacy customers' decision to use personal health records via smartphone. TJPS. 2016;40(Supplement Issue):163-167. [ FREE Full text ]
• Knoerl R, Dudley WN, Smith G, Bridges C, Kanzawa-Lee G, Smith EML. Pilot testing a web-based system for the assessment and management of chemotherapy-induced peripheral neuropathy. Comput Inform Nurs. 2017;35(4):201-211. [ CrossRef ] [ Medline ]
• Myall M, May CR, Grimmett C, May CM, Calman L, Richardson A, et al. RESTORE: an exploratory trial of a web-based intervention to enhance self-management of cancer-related fatigue: findings from a qualitative process evaluation. BMC Med Inform Decis Mak. 2015;15:94. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Akdur G, Aydin MN, Akdur G. Adoption of mobile health apps in dietetic practice: case study of diyetkolik. JMIR Mhealth Uhealth. 2020;8(10):e16911. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Meijer E, Korst JS, Oosting KG, Heemskerk E, Hermsen S, Willemsen MC, et al. "At least someone thinks i'm doing well": a real-world evaluation of the quit-smoking app StopCoach for lower socio-economic status smokers. Addict Sci Clin Pract. 2021;16(1):48. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Bardosh KL, Murray M, Khaemba AM, Smillie K, Lester R. Operationalizing mHealth to improve patient care: a qualitative implementation science evaluation of the WelTel texting intervention in Canada and Kenya. Global Health. 2017;13(1):87. [ FREE Full text ] [ CrossRef ] [ Medline ]
• McCreesh-Toselli S, Torline J, Gouse H, Robbins RN, Mellins CA, Remien RH, et al. Staff perceptions of preimplementation barriers and facilitators to a mobile health antiretroviral therapy adherence counseling intervention in South Africa: qualitative study. JMIR Mhealth Uhealth. 2021;9(4):e23280. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Hale-Gallardo JL, Kreider CM, Jia H, Castaneda G, Freytes IM, Ripley DCC, et al. Telerehabilitation for rural veterans: a qualitative assessment of barriers and facilitators to implementation. J Multidiscip Healthc. 2020;13:559-570. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Peracca SB, Jackson GL, Lamkin RP, Mohr DC, Zhao M, Lachica O, et al. Implementing teledermatology for rural veterans: an evaluation using the RE-AIM framework. Telemed J E Health. 2021;27(2):218-226. [ CrossRef ] [ Medline ]
• Harry ML, Truitt AR, Saman DM, Henzler-Buckingham HA, Allen CI, Walton KM, et al. Barriers and facilitators to implementing cancer prevention clinical decision support in primary care: a qualitative study. BMC Health Serv Res. 2019;19(1):534. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Haverhals LM, Sayre G, Helfrich CD, Battaglia C, Aron D, Stevenson LD, et al. E-consult implementation: lessons learned using consolidated framework for implementation research. Am J Manag Care. 2015;21(12):e640-e647. [ FREE Full text ] [ Medline ]
• Liddy C, Bello A, Cook J, Drimer N, Pilon MD, Farrell G, et al. Supporting the spread and scale-up of electronic consultation across Canada: cross-sectional analysis. BMJ Open. 2019;9(5):e028888. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Levinson AJ, Ayers S, Butler L, Papaioannou A, Marr S, Sztramko R. Barriers and facilitators to implementing web-based dementia caregiver education from the clinician's perspective: qualitative study. JMIR Aging. 2020;3(2):e21264. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Ndlovu K, Mauco KL, Keetile M, Kadimo K, Senyatso RY, Ntebela D, et al. Acceptance of the district health information system version 2 platform for malaria case-based surveillance by health care workers in Botswana: web-based survey. JMIR Form Res. 2022;6(3):e32722. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Adeoye-Olatunde OA, Curran GM, Jaynes HA, Hillman LA, Sangasubana N, Chewning BA, et al. Preparing for the spread of Patient-Reported Outcome (PRO) data collection from primary care to community pharmacy: a mixed-methods study. Implement Sci Commun. 2022;3(1):29. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Wang SL, Lin HI. Integrating TTF and IDT to evaluate user intention of big data analytics in mobile cloud healthcare system. Behav Inf Technol. 2019;38(9):974-985. [ CrossRef ]
• Mei YY, Marquard J, Jacelon C, DeFeo AL. Designing and evaluating an electronic patient falls reporting system: perspectives for the implementation of health information technology in long-term residential care facilities. Int J Med Inform. 2013;82(11):e294-e306. [ CrossRef ] [ Medline ]
• Gray CS, Chau E, Tahsin F, Harvey S, Loganathan M, McKinstry B, et al. Assessing the implementation and effectiveness of the electronic patient-reported outcome tool for older adults with complex care needs: mixed methods study. J Med Internet Res. 2021;23(12):e29071. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Bodenheimer T, Sinsky C. From triple to quadruple aim: care of the patient requires care of the provider. Ann Fam Med. 2014;12(6):573-576. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Lau F, Hagens S, Muttitt S. A proposed benefits evaluation framework for health information systems in Canada. Healthc Q. 2007;10(1):112-116. [ Medline ]
• Damschroder LJ, Reardon CM, Widerquist MAO, Lowery J. Conceptualizing outcomes for use with the Consolidated Framework for Implementation Research (CFIR): the CFIR outcomes addendum. Implement Sci. 2022;17(1):7. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Damschroder LJ, Reardon CM, Widerquist MAO, Lowery J. The updated consolidated framework for implementation research based on user feedback. Implement Sci. 2022;17(1):75. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Schünemann HJ, Reinap M, Piggott T, Laidmäe E, Köhler K, Pōld M, et al. The ecosystem of health decision making: from fragmentation to synergy. Lancet Public Health. 2022;7(4):e378-e390. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Hung D, Gray C, Martinez M, Schmittdiel J, Harrison MI. Acceptance of lean redesigns in primary care: a contextual analysis. Health Care Manage Rev. 2017;42(3):203-212. [ CrossRef ] [ Medline ]
• Squires JE, Aloisio LD, Grimshaw JM, Bashir K, Dorrance K, Coughlin M, et al. Attributes of context relevant to healthcare professionals' use of research evidence in clinical practice: a multi-study analysis. Implement Sci. 2019;14(1):52. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Wensing M, Grol R. Knowledge translation in health: how implementation science could contribute more. BMC Med. 2019;17(1):88. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Deverka PA, Lavallee DC, Desai PJ, Esmail LC, Ramsey SD, Veenstra DL, et al. Stakeholder participation in comparative effectiveness research: defining a framework for effective engagement. J Comp Eff Res. 2012;1(2):181-194. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Haddaway NR, Kohl C, Rebelo da Silva N, Schiemann J, Spök A, Stewart R, et al. A framework for stakeholder engagement during systematic reviews and maps in environmental management. Environ Evid. 2017;6(1):11. [ FREE Full text ] [ CrossRef ]
• Rangachari P, Mushiana SS, Herbert K. A scoping review of applications of the Consolidated Framework for Implementation Research (CFIR) to telehealth service implementation initiatives. BMC Health Serv Res. 2022;22(1):1450. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Theory, model, and framework comparison and selection tool (T-CaST). ImpSciX. Birken SA. The North Carolina Translational and Clinical Sciences (NC TraCS) Institute at The University of North Carolina at Chapel Hill C; 2023. URL: https://impsci.tracs.unc.edu/tcast/ [accessed 2023-02-16]
• University of Colorado. URL: https://dissemination-implementation.org/tool/ [accessed 2023-02-16]
• Phillips AC, Lewis LK, McEvoy MP, Galipeau J, Glasziou P, Moher D, et al. Development and validation of the guideline for reporting evidence-based practice educational interventions and teaching (GREET). BMC Med Educ. 2016;16(1):237. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Hoffmann TC, Glasziou PP, Boutron I, Milne R, Perera R, Moher D, et al. Better reporting of interventions: Template for Intervention Description and Replication (TIDieR) checklist and guide. BMJ. 2014;348:g1687. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Krick T, Huter K, Seibert K, Domhoff D, Wolf-Ostermann K. Measuring the effectiveness of digital nursing technologies: development of a comprehensive digital nursing technology outcome framework based on a scoping review. BMC Health Serv Res. 2020;20(1):243. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Lau F, Kuziemsky C, editors. Handbook of eHealth Evaluation: An Evidence-based Approach. Victoria (BC). University of Victoria; 2017.

Abbreviations

Edited by A Mavragani; submitted 26.07.23; peer-reviewed by A Finucane, T Rapley; comments to author 18.09.23; revised version received 10.11.23; accepted 27.12.23; published 05.02.24.

©Geneviève Rouleau, Kelly Wu, Karishini Ramamoorthi, Cherish Boxall, Rebecca H Liu, Shelagh Maloney, Jennifer Zelmer, Ted Scott, Darren Larsen, Harindra C Wijeysundera, Daniela Ziegler, Sacha Bhatia, Vanessa Kishimoto, Carolyn Steele Gray, Laura Desveaux. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 05.02.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Review Article
• Open access
• Published: 26 February 2021

Applications of digital health for public health responses to COVID-19: a systematic scoping review of artificial intelligence, telehealth and related technologies

• Dinesh Visva Gunasekeran   ORCID: orcid.org/0000-0002-8502-2334 1 , 2   na1 ,
• Rachel Marjorie Wei Wen Tseng 1   na1 ,
• Yih-Chung Tham 1 , 3 &
• Tien Yin Wong   ORCID: orcid.org/0000-0002-8448-1264 1 , 2 , 3  

npj Digital Medicine volume  4 , Article number:  40 ( 2021 ) Cite this article

21k Accesses

139 Citations

51 Altmetric

Metrics details

• Infectious diseases
• Public health

The coronavirus disease 2019 (COVID-19) pandemic has overwhelmed healthcare services, faced with the twin challenges in acutely meeting the medical needs of patients with COVID-19 while continuing essential services for non-COVID-19 illnesses. The need to re-invent, re-organize and transform healthcare and co-ordinate clinical services at a population level is urgent as countries that controlled initial outbreaks start to experience resurgences. A wide range of digital health solutions have been proposed, although the extent of successful real-world applications of these technologies is unclear. This study aims to review applications of artificial intelligence (AI), telehealth, and other relevant digital health solutions for public health responses in the healthcare operating environment amidst the COVID-19 pandemic. A systematic scoping review was performed to identify potentially relevant reports. Key findings include a large body of evidence for various clinical and operational applications of telehealth (40.1%, n  = 99/247). Although a large quantity of reports investigated applications of artificial intelligence (AI) (44.9%, n  = 111/247) and big data analytics (36.0%, n  = 89/247), weaknesses in study design limit generalizability and translation, highlighting the need for more pragmatic real-world investigations. There were also few descriptions of applications for the internet of things (IoT) (2.0%, n  = 5/247), digital platforms for communication (DC) (10.9%, 27/247), digital solutions for data management (DM) (1.6%, n  = 4/247), and digital structural screening (DS) (8.9%, n  = 22/247); representing gaps and opportunities for digital public health. Finally, the performance of digital health technology for operational applications related to population surveillance and points of entry have not been adequately evaluated.

Similar content being viewed by others

Digital technologies in the public-health response to COVID-19

COVID-19: a gray swan’s impact on the adoption of novel medical technologies

The potential use of digital health technologies in the African context: a systematic review of evidence from Ethiopia

Introduction.

The coronavirus disease 2019 (COVID-19) pandemic has crippled both economies and health systems, killing more than 1 million people, with threats of resurgence even as many nations control initial outbreaks 1 , 2 . Many health systems are overwhelmed 3 , 4 , with this trend being more pronounced in front-line emergency services and mental health services 5 , 6 , 7 . Conservative modelling has indicated that certain health systems are particularly vulnerable, including many developing countries in Asia with limited healthcare capacity, along with shortages of beds in hospitals and intensive care units (ICUs) in African countries 8 , 9 . Health systems need to rapidly re-organize resources and restructure clinical services at a population level to minimize the risk of healthcare-associated transmission, as well as meet public health requirements for continued surveillance, risk mitigation, and containment 2 , 4 .

Digital health technologies, such as telehealth, artificial intelligence (AI) and big data predictive analytics, offer substantial promise to mitigate the effects of COVID-19 by enhancing population-level public health responses. Some of these digital solutions have already been piloted and deployed to address the challenges of COVID-19 10 , 11 . However, while there have been exciting isolated reports of real-world development and validation of these digital solutions, recent literature has also highlighted significant challenges in deployment and scale-up, and limitations of clinical trials that are of varied quality and design 12 , 13 . Therefore, it is presently unclear what digital health solutions, if any, have been successfully deployed and applied in the public health responses to the COVID-19 pandemic.

This manuscript is a systemic review of digital health applications for population-level public health responses during the first 6 months of the pandemic. We used a scoping review approach to map out the range and nature of evidence, in order to answer our fundamental question: “What forms of digital health had been applied for public health responses to COVID-19?”.

We retrieved an initial 1904 unique records by the search. All titles and abstract information available in the database were reviewed during screening, and 1559 reports were excluded. 345 full-text reports were then assessed for eligibility. The 345 reports identified on screening originated from over 15 countries and regions (Supplemental Fig. 1 ). After full-text articles were assessed for eligibility, 247 reports were included in this scoping review for data charting and analysis (Screening flow diagram in Supplemental Fig. 2 ). The study design and other key features of the included articles are described in Table 1 . Only 20 articles (8.1%) investigated patient and/or provider acceptance of these technologies, whereby 17 studies focused primarily on acceptance while three cross-sectional studies had included assessment of acceptance. The technology domains that were most frequently described for responses to COVID-19 were AI (44.9%, n  = 111/247), telehealth (40.1%, n  = 99/247), and big data (36.0%, n  = 89/247).

The complete spectrum of CAs and OAs of these digital health technology domains in the context of COVID-19 are detailed in Table 2 . These are further visualized in the form of a Spider diagrams (Fig. 1 ) and detailed matrix cross-tabular table (Supplemental Fig. 3 ) to map the evidence for these digital health solutions, indicating the percentage of reports that have topical coverage of each clinical and/or operational application within each technology domain.

Spider diagram of ( a ) clinical applications and ( b ) operational applications for the digital health technology domains described in COVID-19. Scale for the radial axes of this chart are standardized at 10 units per layer.

Despite a large number of reports describing promising AI and big data applications in the pandemic 13 , we found that minimal investigations for patient and/or provider acceptance have been reported. This is despite numerous reports of these tools being widely applied for surveillance or interpretation of chest imaging scans for operational efficiencies in overloaded healthcare services. Furthermore, few investigations of IoT solutions have been reported despite a large number of these solutions being deployed at the population level for the monitoring of high risk patients under quarantine, such as returning travelers or contacts of confirmed cases. That said, there have been a surprisingly large number of reports regarding applications of DCs such as digital messaging communications platforms, national/organizational websites for information dissemination (1-way), or online health communities (OHCs) that facilitate discussions (2-way). These categories of reports are further detailed in Table 3 using the report assessment criteria defined in the methodology section (screening reports).

There was a paucity of reports describing the performance of digital health technologies applied at points of entry and national laboratories. The varying quality of study design and methods of analyses are further depicted in the form of a bubble plot in Fig. 2 using the same report assessment criteria, highlighting the shortage of investigations for DM, IoT, DS and DC, as well as methodological limitations despite the large number of reported studies for AI and big data applications. Notably, there was a surprising prominence of reports about DCs (Fig. 2 ) for triage 14 , 15 , 16 , co-ordination 16 , 17 , 18 , and public health communication addressing misinformation, resource availability, and evolving guidelines 19 , 20 , 21 , 22 .

The scales for “Translational relevance” and “Strength of evidence” are applied based on study design, participant recruitment and follow-up as described in the Methodology section.

Our systematic scoping review provides an overview of digital health technology that were used for clinical and/or operational applications for population-level public health responses in the first 6 months of the COVID-19 pandemic. Our findings build on recent editorial and perspective articles that provide a subjective narrative overview of various digital health topics that could be potentially applied in public health responses to COVID-19 10 , 11 , 23 . The systematic and pragmatic approach of this scoping review provides a map of existing reports at this critical juncture of the pandemic as countries develop population health strategies for safe re-opening. We believe that this serves as a crucial reference for public healthcare systems regarding potential impact and relevance of different digital technologies to prioritise resources and efforts to address the challenges presented by COVID-19. In addition, we highlight significant gaps in the literature that can be addressed through the conduct of research concurrently with the deployment of these solutions.

The need for rapid adoption of digital health technology has been suggested and driven by the unprecedented scale in the impact of the COVID-19 pandemic due to an increasingly connected global ecosystem with mass travel, urban overcrowding, and information from social and digital media 11 . These factors did not feature as prominently in previous major infectious disease outbreaks such as severe acute respiratory distress syndrome (SARS) and middle east respiratory syndrome (MERS) 24 , 25 . In particular, the deluge of misinformation during this pandemic has drowned out official information, in what has been dubbed an “infodemic” by WHO 26 . Coupled with evolving recommendations as scientists gradually uncover more information about this virus 23 , the infodemic has needlessly fueled growing paranoia and anxiety among the public 7 , as well as confusion for patients with chronic diseases who seek to continue the care for their medical problems 27 . In this regard, DC is at the forefront to address the infodemic and provide transparent information and updates, as reflected in the prominence of relevant reports (Fig. 2 ).

While there are a range of digital health technology and the maturity of some (e.g., AI) has paved the way for the digitization of clinical and operational responses to contain the pandemic 28 , there remain significant challenges and gaps in adoption, scale-up and integration into healthcare systems, even in developed countries 29 . For example, there continues to be ethical concerns with population-level deployment of these tools, particularly in the case of surveillance technologies without individual consent, presenting new ethical and privacy concerns that need to be addressed 30 , 31 . Moreover, although vulnerable regions with limited health system capacity are likely to benefit the most from scalable digital tools 32 , many have barriers to technology implementation illustrated in earlier technology reports 33 . These regions will require concerted support and public health coordination for the year ahead 23 , at least until a safe and effective vaccine or treatment is readily available. Without this, limiting the human toll and addressing infectious reservoirs will remain a formidable challenge, potentially crippling these fragile health systems.

The scoping review approach provided for a detailed account of the spectrum of relevant literature at this critical juncture of the pandemic. Gaps in the literature that have been identified in this review include assessments of digital health technologies for operational applications at points of entry and for population surveillance (Fig. 1 ). Furthermore, although there was a large quantity of reports investigating applications of AI and big data, limitations in study design curb generalizability and translation. These results indicate that there is a pressing need for more investigations of IoT, DC, DM, and DS digital health technologies, as well as underscore the need for better quality studies of digital health such as AI and big data applications using prospective, pragmatic study designs (Fig. 2 ) 34 .

The strengths of this review include its timeliness in the context of the ongoing pandemic, systematic article inclusion and data extraction, as well as the scoping review approach for an in-depth analysis of the literature. Added benefits of this review include an a priori protocol and involvement of stakeholders with relevant experience developing digital health and deployment in clinical services before and during COVID-19 35 . We have specifically included in this review only digital technologies with applications for population-level public health responses during COVID-19. Our search strategy was limited to reports that self-identified with relevant search terms (Supplementary note 1 ) selected to improve the yield of reports about digital technologies with relevance to population-level public health responses to COVID-19 given the timeliness of this topic We have not included other digital technologies such as those regarding fitness trackers, augmented reality (AR) or virtual reality (VR) digital health tools 29 .

The limited number of studies investigating patient and provider acceptance of these tools (<10% of reports) also highlights the need for greater participatory research involving stakeholders to increase the likelihood of sustained adoption beyond the pandemic 26 , 36 . This is advocated on the basis of a growing body of evidence surrounding the complexity of digital health solutions due to their interactions with operational and interpersonal aspects of clinical care beyond the target condition(s) 30 , 36 , 37 , 38 . Therefore, digital health solutions may need to be evaluated as clinical care pathway interventions rather than isolated tools, in order to achieve holistic assessment and inform successful implementation 39 .

In conclusion, our study provides a rapid scoping review of digital health applications described in the first six months of the pandemic, highlighting potential applications and gaps in the literature for the consideration of clinicians, administrators, and researchers. More studies investigating specific applications of digital health to develop relevant scalable public health responses are highlighted, in particular, the pressing need for researchers to formally evaluate digital health applications for population surveillance and points of entry. Finally, there is a general need for better methodological design in the investigation of digital health applications prospectively using pragmatic approaches to better inform public health responses. The use of participatory approaches in the deployment and assessment of these tools will also yield crucial insights to enable sustained adoption during and beyond the pandemic.

We conducted a systematic review in accordance with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines extension for scoping reviews (PRISMA-ScR). The review was pre-registered in open science framework (OSF, registration number: osf.io/8nbgj). To be included in the review, papers needed to provide original descriptions of clinical and/or operational applications of digital health technology or solutions in the context of COVID-19 for population-level public health responses. All English-language peer-reviewed reports and pre-prints published within the first 6 months of the pandemic are included. Pre-prints are included due to the extremely current nature of this topic. The completed PRISMA-ScR checklist is included (Supplemental Table 1 ).

Search strategy and selection criteria

To identify potentially relevant reports, databases were searched from the time of the initial announcement from WHO regarding a cluster of cases of pneumonia in Wuhan on 31 Dec 2019 40 , to 1 July 2020. The search was conducted on 2 July 2020 and exported to Microsoft excel for screening and charting. Electronic bibliographic databases of published research in Pubmed including MEDLINE, IEE explore, and databases for research pre-prints including medrXiv (health sciences), arXiv (engineering), and bioRxiv (biology), given the cross-disciplinary nature of the search topic involving both health sciences and information systems. The search strategies were drafted and refined through study team discussion. Search terms selected for the literature search include the digital health technology domains and the target application context of the pandemic using Boolean operators (OR/AND). The final detailed search strategy for Pubmed is included in this publication (Supplementary note 1 ).

Randomized-controlled trials, cohort studies, case–control studies, case series, descriptions of technology solutions or case reports of digital health technology and solutions for clinical and/or operational applications in COVID-19 are included. Reports were excluded if they did not fit into the conceptual framework of clinical and/or operational applications for public health responses applied to this study, such as descriptions of digital health for residency training or continuing medical education. Editorials, perspective articles, narrative or other reviews without original data, and study protocols are also excluded.

Screening reports

Study selection was determined by review of available information from study title and abstract in the indexed database for relevance to digital health clinical and/or operational applications for public health responses in the context of COVID-19. Data charting was completed based on all accessible information in the study manuscript. A standardized study screening manual, including a data charting form along with an explanation and elaboration document in the form of a coding manual (Supplementary note 2 ) was developed by the study team by group consensus.

The report quality assessment criteria used in this study were extrapolated from distillation of the oxford center for evidence-based medicine (OCEBM) construct 41 , to facilitate greater granularity and relevance to translation for this review. This was done with an aim to provide practical information for decision makers to inform ongoing responses to the pandemic and identify gaps in the literature for researchers looking to evaluate ongoing applications of digital health technologies. Studies are thereby categorized based on the strength of evidence, ranging from case reports to the ideal randomized-controlled trial (RCT) methodology, as well as the translational relevance depending whether prospective or retrospective data was used, and whether an intention-to-treat approach to evaluate the technology “as offered” was adopted to reduce bias and missing data 39 , 42 , 43 .

The coding manual (Supplementary note 2 ) details how digital health technology or solutions described in these reports were characterized based on technology domains 10 , including artificial intelligence (AI), big data analytics, internet of things (IoT), telehealth, digital platforms for communication (DC), digital solutions for data management (DM), and digital structural screening (DS). The coding of clinical applications (CAs) were indicated based on clinical priorities for patients with COVID-19 such as detection, triage, developing tests/treatment, as well as continuing care for patients with non-COVID ailments 44 , 45 . Finally, the coding of potential relevance to operational applications (OAs) were indicated based on descriptions of the 9-pillars of country-level public health responses as recommended by the WHO 46 .

Data charting and analysis

To increase consistency of study screening among reviewers, reviewer 1 (DG) piloted the study screening manual for database search and study selection based on title/abstract information available in the databases. Subsequently, reviewer 2 (RT) independently cross-checked study selection for 10% of all articles identified in the database search, using a computer-generated random sequence ( www.randomizer.org ). Both reviewers then discussed results, and amended the screening manual before the data charting step.

Subsequently, both reviewer 1 and 2 independently piloted the study screening manual for evaluating the eligibility of 10% of all identified full-text reports using a computer-generated random sequence ( www.randomizer.org ), along with complete data charting for the included articles. Both reviewers then discussed results and amended the screening manual. Finally, reviewers 1 and 2 independently completed assessment of the remaining full-text reports for eligibility along with data charting for all included reports.

Any disagreements on study selection and data charting during pilot testing were resolved by consensus, or otherwise tie-breaker by reviewer 3 (YT) if needed. Where interrater agreement was low (Cohen’s kappa coefficient < 0.8), a repeat sampling of 10% of all relevant reports was conducted with disagreements resolved by consensus. Data from the coding of included studies were analysed quantitatively whereby missing data were handled by pairwise deletion without imputation. We grouped studies by technology domains and summarized the clinical and operational applications described to identify key trends in the literature and knowledge gaps for future research. All findings are synthesized using a narrative review approach. Key results are summarized using Spider diagrams, matrix cross-tabular table of the published clinical/operational applications as well as a bubble plot of the reports depicting the strength of evidence and translational relevance for each technology domain.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

All included reports from which data was generated and/or analysed in this systematic review are included in the published article and Supplemental Information .

World Health Organisation (WHO). Coronavirus disease (COVID-2019) situation reports. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports/ (2020).

Fontanarosa, P. B. & Bauchner, H. COVID-19-looking beyond tomorrow for health care and society. JAMA 323 , 1907–1908 (2020).

Article   CAS   Google Scholar  

Legido-Quigley, H. et al. Are high-performing health systems resilient against the COVID-19 epidemic? Lancet 395 , 848–850 (2020).

Walensky, R. P., Rio, Del & From, C. Mitigation to containment of the COVID-19 pandemic: putting the SARS-CoV-2 genie back in the bottle. JAMA 323 , 1889–1890 (2020).

Uppal, A. et al. Critical care and emergency department response at the epicenter of the COVID-19 pandemic. Health Aff (Millwood) 39 , 1443–1449 (2020).

Article   Google Scholar  

Rajkumar, R. P. COVID-19 and mental health: a review of the existing literature. Asian J. Psychiatr. 52 , 102066 (2020).

Chew, A. M. K. et al. Digital health solutions for mental health disorders during COVID-19. Front. Psychiatry 11 , 582007 (2020).

Barasa, E., Ouma, P. O. & Okiro, E. A. Assessing the hospital surge capacity of the Kenyan health system in the face of the COVID-19 pandemic. PLoS ONE 15 , e0236308 (2020).

Verma, V. R., Saini, A., Gandhi, S., Dash, U. & Koya, M. S. F. Projecting Demand-Supply Gap of Hospital Capacity in India in the face of COVID-19 pandemic using Age-Structured Deterministic SEIR model. medRxiv 2020.05.14.20100537. Preprint at https://doi.org/10.1101/2020.05.14.20100537 (2020).

Ting, D. S. W., Carin, L., Dzau, V. & Wong, T. Y. Digital technology and COVID-19. Nat. Med . 26 , 459–461 (2020).

Rasmussen, S. A., Khoury, M. J. & Del Rio, C. Precision public health as a key tool in the COVID-19 response. JAMA 324 , 933–934 (2020).

Piovani, D., Pansieri, C., Peyrin-Biroulet, L., Danese, S. & Bonovas, S. A snapshot of the ongoing clinical research on COVID-19. F1000Res. 9 , 373 (2020).

Murray, C. J. L., Alamro, N. M. S., Hwang, H. & Lee, U. Digital public health and COVID-19. Lancet. Public Health 5 , e469–e470 (2020).

PubMed   Google Scholar  

Annis, T. et al. Rapid implementation of a COVID-19 remote patient monitoring program. J. Am. Med Inf. Assoc. 27 , 1326–1330 (2020).

Espinoza, J., Crown, K. & Kulkarni, O. A guide to chatbots for COVID-19 screening at pediatric health care facilities. JMIR Public Health Surveill. 6 , e18808 (2020).

Judson, T. J. et al. Rapid design and implementation of an integrated patient self-triage and self-scheduling tool for COVID-19. J. Am. Med Inf. Assoc. 27 , 860–866 (2020).

Perez-Alba, E., Nuzzolo-Shihadeh, L., Espinosa-Mora, J. E. & Camacho-Ortiz, A. Use of self-administered surveys through QR code and same center telemedicine in a walk-in clinic in the era of COVID-19. J. Am. Med Inf. Assoc. 27 , 985–986 (2020).

Menni, C. et al. Real-time tracking of self-reported symptoms to predict potential COVID-19. Nat. Med. 26 , 1037–1040 (2020).

Yuan, E. J. et al. Where to buy face masks? Survey of applications using Taiwan’s open data in the time of coronavirus disease 2019. J. Chin. Med Assoc. 83 , 557–560 (2020).

Lu, Y. & Zhang, L. Social media WeChat infers the development trend of COVID-19. J. Infect. 81 , e82–e83 (2020).

Zamberg, I. et al. A mobile health platform to disseminate validated institutional measurements during the COVID-19 outbreak: utilization-focused evaluation study. JMIR Public Health Surveill. 6 , e18668 (2020).

Hua, J. & Shaw, R. Corona Virus (COVID-19) "Infodemic" and Emerging Issues through a Data Lens: the case of China. Int. J. Environ. Res. Public Health 17 , 2309 (2020).

Whitelaw, S., Mamas, M. A., Topol, E., Van & Spall, H. G. C. Applications of digital technology in COVID-19 pandemic planning and response. Lancet Digit Health 2 , e435–e440 (2020).

Peeri, N. C. et al. The SARS, MERS and novel coronavirus (COVID-19) epidemics, the newest and biggest global health threats: what lessons have we learned?. Int. J. Epidemiol 49 , 717–726 (2020).

Leung, G. M. & Leung, K. Crowdsourcing data to mitigate epidemics. Lancet Digit Health 2 , e156–e157 (2020).

Li, L. W., Chew, A. M. K. & Gunasekeran, D. V. Digital health for patients with chronic pain during the COVID-19 pandemic. Br. J. Anaesth. 125 , 657–660 (2020).

Rosenbaum, L. The untold toll—the pandemic’s effects on patients without Covid-19. N. Engl. J. Med. 382 , 2368–2371 (2020).

Whitelaw, S., Mamas, M. A., Topol, E. & Van Spall, H.G.C. Applications of digital technology in COVID-19 pandemic planning and response. Lancet Digit Health 2 , e435–e440 (2020).

Meskó, B., Drobni, Z., Bényei, É., Gergely, B. & Győrffy, Z. Digital health is a cultural transformation of traditional healthcare. Mhealth 3 , 38 (2017).

Sustained suppression. Nat. Biomed. Eng. 4 , 479–480 (2020).

Cohen, I. G., Gostin, L. O. & Weitzner, D. J. Digital smartphone tracking for COVID-19: public health and civil liberties in tension. JAMA 323 , 2371–2372 (2020).

Mallapaty, S. Scientists fear coronavirus spread in countries least able to contain it. Nature 578 , 348 (2020).

Gunasekeran, D. V. & Wong, T. Y. Artificial intelligence in ophthalmology in 2020: a technology on the Cusp for translation and implementation. Asia Pac. J. Ophthalmol. (Philos.) 9 , 61–66 (2020).

Evangelista, L., Steinhubl, S. R. & Topol, E. J. Digital health care for older adults. Lancet 393 , 1493 (2019).

Feldmann, J., Puhan, M. A. & Mütsch, M. Characteristics of stakeholder involvement in systematic and rapid reviews: a methodological review in the area of health services research. BMJ Open 9 , e024587 (2020).

Gunasekeran, D. V. Technology and chronic disease management. Lancet Diabetes Endocrinol. 6 , 91 (2018).

Greenhalgh, T., Wherton, J., Shaw, S. & Morrison, C. Video consultations for covid-19. BMJ 368 , m998 (2020).

Greenhalgh, T. et al. Analysing the role of complexity in explaining the fortunes of technology programmes: empirical application of the NASSS framework. BMC Med 16 , 66 (2018).

Topol, E. J. High-performance medicine: the convergence of human and artificial intelligence. Nat. Med 25 , 44–56 (2019).

World Health Organization (WHO). Timeline of WHO’s response to COVID-19. https://www.who.int/news-room/detail/29-06-2020-covidtimeline (2020).

OCEBM Levels of Evidence Working Group* “The Oxford Levels of Evidence 2”. Oxford Centre for Evidence-Based Medicine. https://www.cebm.ox.ac.uk/resources/levels-of-evidence/ocebm-levels-of-evidence (2020).

Little, R. J. et al. The prevention and treatment of missing data in clinical trials. N. Engl. J. Med . 367 , 1355–1360 (2012).

McCoy, C. E. Understanding the intention-to-treat principle in randomized controlled trials. West J. Emerg. Med. 18 , 1075–1078 (2017).

Olivia, Li,J. P. et al. Preparedness among ophthalmologists: during and beyond the COVID-19 pandemic. Ophthalmology 127 , 569–572 (2020).

Heymann, D. L. et al. COVID-19: what is next for public health? Lancet 395 , 542–545 (2020).

World Health Organisation (WHO). COVID‑19 strategic preparedness and response: operational planning guidance to support country preparedness and response. https://www.who.int/publications/i/item/draft-operational-planning-guidance-for-un-country-teams (2020).

Download references

Author information

These authors contributed equally: Dinesh Visva Gunasekeran, Rachel Marjorie Wei Wen Tseng.

Authors and Affiliations

Singapore Eye Research Institute (SERI), Singapore National Eye Center (SNEC), Singapore, Singapore

Dinesh Visva Gunasekeran, Rachel Marjorie Wei Wen Tseng, Yih-Chung Tham & Tien Yin Wong

Yong Loo Lin School of Medicine, National University of Singapore (NUS), Singapore, Singapore

Dinesh Visva Gunasekeran & Tien Yin Wong

Duke-NUS Medical School, Singapore, Singapore

Yih-Chung Tham & Tien Yin Wong

You can also search for this author in PubMed   Google Scholar

Contributions

All authors D.G., R.T., Y.T., and T.W. conceptualised the manuscript, researched its contents, wrote the manuscript, edited all revisions, and approved the final version. Authors D.G. and R.T. are co-first authors that contributed equally to the manuscript.

Corresponding author

Correspondence to Tien Yin Wong .

Ethics declarations

Competing interests.

D.G. reports equity investment in digital health solutions AskDr, Doctorbell (acquired by Mobile Health), VISRE, and Shyfts, and appointments as physician leader (telemedicine) in Raffles Medical Group and senior lecturer (medical innovation) at the National University of Singapore. T.W. holds patents of deep learning systems for detection of eye diseases. T.W. is the deputy group chief executive officer (research and education) of Singapore Health Services, a consultant & advisory board for Allergan, Bayer, Boehringer-Ingelheim, Genentech, Merck, Novartis, Oxurion (formerly ThromboGenics), Roche, and co-founder of plano and EyRiS. The remaining authors R.T. and Y.T. declare no competing financial interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary information, reporting summary, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Gunasekeran, D.V., Tseng, R.M.W.W., Tham, YC. et al. Applications of digital health for public health responses to COVID-19: a systematic scoping review of artificial intelligence, telehealth and related technologies. npj Digit. Med. 4 , 40 (2021). https://doi.org/10.1038/s41746-021-00412-9

Download citation

Received : 07 October 2020

Accepted : 15 January 2021

Published : 26 February 2021

DOI : https://doi.org/10.1038/s41746-021-00412-9

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

New evidence on the real role of digital economy in influencing public health efficiency.

• Xiongfei Zhao
• Shansong Wu

Scientific Reports (2024)

Climate change characteristics and population health impact factors using deep neural network and hyperautomation mechanism

• Hairui Zhang

The Journal of Supercomputing (2024)

A model of purchase intention of complementary and alternative medicines: the role of social media influencers’ endorsements

• Gizem Gülpınar
• Mehmet Barlas Uzun
• Mahmood Basil A. Al-Rawi

BMC Complementary Medicine and Therapies (2023)

Non-contrast CT synthesis using patch-based cycle-consistent generative adversarial network (Cycle-GAN) for radiomics and deep learning in the era of COVID-19

• Reza Kalantar
• Sumeet Hindocha
• Matthew D. Blackledge

Scientific Reports (2023)

A systematic literature review on deep learning approaches for pneumonia detection using chest X-ray images

• Shagun Sharma
• Kalpna Guleria

Multimedia Tools and Applications (2023)

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Digital Health Competencies Among Health Care Professionals: Systematic Review

Affiliations.

• 1 Department of Medical Sciences, University of Udine, Udine, Italy.
• 2 School of Physiotherapy, University of Verona, Verona, Italy.
• PMID: 35980735
• PMCID: PMC9437781
• DOI: 10.2196/36414

Background: Digitalization is not fully implemented in clinical practice, and several factors have been identified as possible barriers, including the competencies of health care professionals. However, no summary of the available evidence has been provided to date to depict digital health competencies that have been investigated among health care professionals, the tools used in assessing such competencies, and the effective interventions to improve them.

Objective: This review aims to summarize digital health competencies investigated to date and the tools used to assess them among health care professionals.

Methods: A systematic review based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist was performed. The MEDLINE, Cumulative Index to Nursing and Allied Health Literature, PsycINFO, and Scopus databases were accessed up to September 4, 2021. Studies assessing digital health competencies with quantitative designs, targeting health care professionals, and written in English were included. The methodological quality of included studies was evaluated using the Joanna Briggs Institute tools.

Results: A total of 26 studies, published from 1999 to 2021, met the inclusion criteria, and the majority were cross sectional in design, while only 2 were experimental study designs. Most studies were assessed with moderate to low methodological quality; 4 categories and 9 subcategories of investigated digital health competencies have been identified. The most investigated category was "Self-rated competencies," followed by "Psychological and emotional aspects toward digital technologies," "Use of digital technologies," and "Knowledge about digital technologies." In 35% (9/26) of the studies, a previously validated tool was used to measure the competencies assessed, while others developed ad hoc questionnaires.

Conclusions: Mainly descriptive studies with issues regarding methodology quality have been produced to date investigating 4 main categories of digital health competencies mostly with nonvalidated tools. Competencies investigated might be considered while designing curricula for undergraduate, postgraduate, and continuing education processes, whereas the methodological lacks detected might be addressed with future research. There is a need to expand research on psychological and emotional elements and the ability to use digital technology to self-learn and teach others.

Trial registration: PROSPERO International Prospective Register of Systematic Reviews CRD42021282775; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=282775.

Keywords: competencies; digital health; digital technology; eHealth; eHealth competencies; eHealth literacy; health care professionals; health care workers; health literacy; review; systematic review.

©Jessica Longhini, Giacomo Rossettini, Alvisa Palese. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 18.08.2022.

Publication types

• Systematic Review
• Health Personnel*

Rethinking digital transformation of healthcare: The role of technology and institutions in service innovation: Dissertation

Research output : Thesis › Dissertation › Collection of Articles

• digitalization
• service innovation
• innovation challenges
• institutionalisation
• healthcare renewal

Access to Document

• https://publications.vtt.fi/pdf/science/2018/S176.pdf

Fingerprint

• Healthcare Keyphrases 100%
• Service Innovation Keyphrases 100%
• Digital Transformation Keyphrases 100%
• Role of Technology Keyphrases 100%
• Role of Institutions Keyphrases 100%
• transformations INIS 100%
• productivity INIS 100%
• Theses Social Sciences 100%

T1 - Rethinking digital transformation of healthcare

T2 - The role of technology and institutions in service innovation: Dissertation

AU - Wallin, Arto

N2 - Due to the mounting prevalence of chronic diseases, increasing demand for expensive treatments and the growing old-age dependency ratio, there is a pressing need to augment the productivity and quality of health and elderly care. Although the potential of digital technologies is widely acknowledged, focusing on technological innovations and incremental improvements originatingfrom the healthcare system does not appear to provide the desired results. Therefore, there is a need for innovation that breaks established rules and practices and enables systemic transformation in healthcare.This article-based doctoral thesis builds on four published studies employing abductive case research strategy: a dialogue between theory and empirical analysis. The first two studies were conducted under the framework of European innovation programmes. They explore how digitallyenhanced services improve service productivity in the elderly care setting, and provide insights intoinnovation challenges experienced during a three-year collaborative innovation project. The latter two studies focus on start-ups operating under a start-up business accelerator programme. They increase understanding of the institutional constraints experienced by entrepreneurs when developing innovations that diverge from the prevailing rules of healthcare, and of the ways in which they attempt to change the rules hindering the adoption of innovations.The thesis contributes to service research by constructing a more profound understanding of the mechanisms that advance, hinder, enable and constrain service innovation in the field of healthcare. In particular, the thesis contributes to integrating the perspective of institutional entrepreneurship in service innovation, highlighting the importance of actions that contribute tobreaking prevailing 'rules of the game' (i.e. institutions) and creating new ones. In addition, the thesis depicts how digitalization reveals the pervasive role of technology in innovation. Jointly, these contributions advance the synthesis view on service innovation – a view that highlights the importance of both technological and service aspects in innovation.The policy and managerial implications of the thesis suggest that, in addition to a complex set of institutions that guide innovation in the field of healthcare, the development context may also have a notable impact on innovation. The institutional structures of collaborative innovation programmes should encourage collaboration outside project boundaries, in order to foster theactors' awareness of the institutional and market environment. Exposing innovation to institutional forces makes it easier to comprehend the necessary institutional change and to develop ways of justifying the change to actors that are vital for its support. The institutional perspective should be more tightly linked to the practice of innovation.

AB - Due to the mounting prevalence of chronic diseases, increasing demand for expensive treatments and the growing old-age dependency ratio, there is a pressing need to augment the productivity and quality of health and elderly care. Although the potential of digital technologies is widely acknowledged, focusing on technological innovations and incremental improvements originatingfrom the healthcare system does not appear to provide the desired results. Therefore, there is a need for innovation that breaks established rules and practices and enables systemic transformation in healthcare.This article-based doctoral thesis builds on four published studies employing abductive case research strategy: a dialogue between theory and empirical analysis. The first two studies were conducted under the framework of European innovation programmes. They explore how digitallyenhanced services improve service productivity in the elderly care setting, and provide insights intoinnovation challenges experienced during a three-year collaborative innovation project. The latter two studies focus on start-ups operating under a start-up business accelerator programme. They increase understanding of the institutional constraints experienced by entrepreneurs when developing innovations that diverge from the prevailing rules of healthcare, and of the ways in which they attempt to change the rules hindering the adoption of innovations.The thesis contributes to service research by constructing a more profound understanding of the mechanisms that advance, hinder, enable and constrain service innovation in the field of healthcare. In particular, the thesis contributes to integrating the perspective of institutional entrepreneurship in service innovation, highlighting the importance of actions that contribute tobreaking prevailing 'rules of the game' (i.e. institutions) and creating new ones. In addition, the thesis depicts how digitalization reveals the pervasive role of technology in innovation. Jointly, these contributions advance the synthesis view on service innovation – a view that highlights the importance of both technological and service aspects in innovation.The policy and managerial implications of the thesis suggest that, in addition to a complex set of institutions that guide innovation in the field of healthcare, the development context may also have a notable impact on innovation. The institutional structures of collaborative innovation programmes should encourage collaboration outside project boundaries, in order to foster theactors' awareness of the institutional and market environment. Exposing innovation to institutional forces makes it easier to comprehend the necessary institutional change and to develop ways of justifying the change to actors that are vital for its support. The institutional perspective should be more tightly linked to the practice of innovation.

KW - digitalization

KW - service innovation

KW - innovation challenges

KW - institutionalisation

KW - healthcare renewal

M3 - Dissertation

SN - 978-952-60-8019-2

SN - 978-951-38-8638-7

T3 - VTT Science

PB - Aalto University

Get the week's top news shaping the future of medicine

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• J Glob Health

Technology and Universal Health Coverage: Examining the role of digital health

David wilson.

1 The World Bank, Washington, DC, USA

Aziz Sheikh

2 Global Health Academy/Usher Institute, Center for Medical Informatics, College of Medicine and Veterinary Medicine, Chair of Primary Care Research and Development/Deanery of Molecular, Genetic and Population Health Sciences, University of Edinburgh, Edinburgh, UK

Marelize Görgens

Katherine ward.

While there is tremendous promise to leverage technology for UHC, it will require smart, context-specific policies and programming with ample flexibility to adapt as needs and opportunities change – and with robust safeguards to protect privacy, data security, and equity. The health sector, by its very nature of being data intensive, lends itself to the use of technology for analytics to improve health outcomes, respond to public health crises, and efficiently and equitably allocate resources. The first imperative in considering the use of digital health to expand UHC is to remember that digital health is a means to an end, and only one of the available means. Efforts leveraging digital health to move along that path to universality have taken many forms: to increase the number of people reached, to provide enhanced service coverage, and to reduce the financial burdens on individuals in need of health care. Making use of digital health interventions is an evolving process, not a one-time decision point. It is context specific and needs a clear vision to move from pilot interventions to scaled implementation. Technology can be a key tool in achieving UHC but its use has to be strategic, judicious, and cognizant of issues around privacy and patient rights.

UNDERSTANDING UHC AND TECHNOLOGY

What is universal health coverage.

Universal health coverage is a central component of the Sustainable Development Goals (SDGs). Target 3.8 of SDG 3 states:

“Achieve universal health coverage, including financial risk protection access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for all” [ 1 ].

Universal health coverage requires a fundamental shift in how we define and provide health care: moving from a disease-specific approach concentrated on counting the number of services provided to a people-centered approach. This approach must efficiently and equitably provide predictive, pre-emptive, personalized and participatory health care that enables people to live healthy, productive lives in which clients are active participants in their own care: empowered to better determine their own health outcomes and able to understand, control, protect, and leverage their own health information. Global health is about people and the systems needed to support people to live health lives. It is a systems and delivery problem, not just a disease problem. Medicines and other interventions to prevent and reverse diseases only have value if they reach the people who need them and do so in a way that ensures they are properly and sustainably used.

This entails substantial, concrete progress on three main fronts:

• Providing all people with access to services;
• Providing the full spectrum of essential, quality health services; and
• Protecting people from overwhelming financial consequences of paying for health.

In short, this means reaching more people, with more (and more effective) services, while also reducing the financial burdens on patients – a structure often explained using Figure 1 [ 2 ].

The Three Action Lines for Realizing UHC. Adapted from World Health Organization, by permission of World Health Organization [ 2 ].

What does “technology in health” mean?

Technology. Big Data. Artificial Intelligence. Digital health. These are a few of the terms frequently used to describe this rapidly evolving set of tools that are often hailed as the answer to many health needs and challenges. But the first step to making use of them is to understand what they are and how they relate to one another. If we do not really know what the tools are, we cannot make smart decisions about the best ways to use them.

Artificial Intelligence (AI) is any task that would be considered intelligent if done by a human [ 3 ]. AI systems typically demonstrate behaviors associated with human intelligence such as planning, learning and reasoning. Its four core technologies are the three areas of Big Data analytics (computer vision and image recognition; natural language processing; and machine learning to cluster, predict, and classify) along with smart devices and robots. AI adapts through progressive learning algorithms to analyze more and deeper data, with increasing accuracy to get the most out of the health data we have. This is particularly relevant because health systems generate vast amounts of data. By 2020, the volume of health data was expected to exceed 2314 exabytes, with 2.5 trillion megabytes added daily; one report found that the expected compound annual growth rate of health care data between 2018 and 2025 will be 36 percent [ 4 ]. Currently, 30% of all global data are health data and 80% of health data are unstructured [ 5 , 6 ]. The McKinsey Global Institute Digitization Index has found that despite AI’s potential, uptake in the health sector is slow compared to other sectors, even in industrialized nations in Europe and the in the United States ( Figure 2 ) [ 7 , 8 ].

McKinsey Global Institute Digitization Index [ 7 , 8 ].

AI can be used to answer a number of types of questions:

• What has happened? (descriptive)
• Why it happened? (diagnostic)
• What will happen? (predictive)
• What can we do? (prescriptive)

With machine learning analytics and thinking, AI can also be used to make recommendations about what to do based on the answers to those questions.

While AI has existed for decades, its potential has increased exponentially in recent years because of dramatic improvements in computational power, the rise of Big Data (rapid digitization and vast amounts of data now available, and the rise of fast and cheap data storage). The estimated amount of space necessary to store a million trillion megabytes has dropped from a data center equal to the size of Lithuania (2010 estimate) to the size of a tennis ball (2019 estimate). The rise of 5G networks will further facilitate data use, allowing such networks to move more data and move data faster. This will enable wider use of tools relying on videos, imaging, and continual patient monitoring using sensors [ 9 ]. One caveat that must be kept in mind is that AI is only as good as the data it uses. As a result, realizing the potential of AI also depends on making the requisite structural improvements in data collection.

Another common term is digital health which includes eHealth, mHealth, diagnostic innovations, the Internet of Things, and AI. In short, as defined by the World Health Organization in its 2019 Digital Health Guideline, digital health is the use of digital technologies, employing routine and innovative forms of information and communications technology to address health needs [ 10 ]. It is a more precise term than “technology” with a more clearly defined meaning and relevance for the topic at hand and will be used throughout the remainder of this article.

USING DIGITAL HEALTH TO EXPAND EACH SIDE OF THE CUBE: APPLYING DIGITAL HEALTH TO UHC

The health sector is knowledge-intensive, dependent on data and analytics to improve health outcomes, respond to public health crises, and efficiently and equitably allocate resources [ 11 ]. The first imperative in considering the use of digital health to expand UHC is to remember that digital health is a means to an end, and only one of the available means. The focus should always first be on identifying the question that needs to be answered; only after that has been done should attention turn to determining how technology can be used to meet to answer that question [ 12 ]. It is also important to keep in mind that the focus should always remain on ensuring real benefits for real people in the health services value chain, with potential beneficiary groups including: individuals/patients; families; communities; clinicians/service providers; payers; regulators/policy-makers; and producers (eg, of medicines and equipment).

As indicated above, in the context of UHC the three overarching questions are:

• How to reach more people (risk stratification and targeting)? (benefits individuals, families, communities, clinicians and regulators);
• How to improve service quality (including new services)? (benefits individuals, families, communities, clinicians, payers, regulators and producers);
• How to strengthen financial risk protection (reducing financial burdens on patients while also ensuring a sustainable financing structure)? (benefits individuals, families, clinicians, payers, and regulators).

This entails finding smart, evidence-informed interventions for the following steps and determining what role, if any, digital interventions can and should play in those responses:

• Targeting the right areas (location services): finding those most in need and better locating services
• Making the right predictions (predictive analytics): identifying who will need help, and supporting patients as well as front-line and hospital-based health workers with remote diagnostic tools, monitoring, and consultative support
• Creating the right access (financial inclusion): eg, mobile payments/credits
• Ensuring the right recipients are reached (digital verification)
• Ensuring the right payments are made
• Providing the right job aids
• Making the right choices (data-driven choices)
• Delivering the right services
• Using the right provider (match need with expertise)
• Delivering the right value (technology solutions) (improve cost-effectiveness of health impact with available funds)
• Making the right measurement (performance innovation)
• Supporting the right innovation (accelerated R&D)

To this end, digital health, and particularly AI, can also help answer questions such as those presented in Table 1 .

Examples of questions digital health, and particularly AI, can help answer

These questions are clearly relevant to accelerating progress towards UHC. Digital health can also be used to improve health impacts in underserved settings by increasing capacity and efficiency by responsibly shifting tasks to individuals with less, but adequate, training ( task-shifting ); equipping individuals with the knowledge and tools to manage their own health ( self-management ); and using technology to address underlying risk factors, improve early detection, and identify cost-effective, high-impact measures and adjust resources accordingly ( improving population-level outcomes ) [ 10 ].

EXPERIENCES AND INSIGHTS FROM THE FIELD

Examples from the field.

Digital health tools can be used in many ways to address a wide variety of health needs related to expanding UHC. These can include simple, single-tool interventions to achieve immediate, discreet successes. Or it can entail working over the longer-term to build integrated systems for broader impact. The bottom line is that using digital health to accelerate UHC is a process not an action point. Moreover, as some of the later examples below demonstrate, sustained success also requires attention to the building blocks that create a supportive environment for digital health. The following examples are organized according to the three action pillars of UHC described above: expanding access to more people, providing more (and better) services that meet people’s real health needs as part of a people-centered approach, and reducing financing burdens on individuals (reducing financial stress). It also includes a brief discussion of the area of digital identification, which often has cross-cutting impacts. It concludes with a few examples of more complex programs involving multiple forms and phases using digital health tools to provide insights into the complexities of more systematically integrating digital health into ongoing programming. Figure 3 and Table 2 summarize the interventions discussed and the groups that benefit from each.

What helps and how it helps: examples from the field.

Digital health and UHC in action: examples of expanding the cube

HCV – Hepatitis C virus

Reaching more people

Expanding access to care to more people is fundamental to any universal health coverage plan that aims to indeed be universal. Efforts leveraging digital health to move along that path to universality have taken many forms. The examples below illustrate some of the many promising paths that have been explored.

In Pakistan, to reach underserved communities with quality care, DoctHERS & Sehat Kehani use an app and web portal to provide online telecare, connecting over 40 000 patients in remote areas to qualified home-based female doctors who were not practicing [ 13 ]. Frontline health workers visit communities with app-enabled devices and also work in telemedicine centers, providing patients with two ways to tap into the systems, which now has over 20 eHealth telemedicine hubs and has almost 1 million beneficiaries [ 14 , 15 ].

In Colombia, a World Bank project is using machine learning to optimize resource management for the Hepatitis C virus (HCV) to better identify those likely to contract HCV but not be diagnosed and who will need a particular combination of medicines. Early results show machine learning can make these predictions with a high degree of confidence, although further progress has been slowed due to data access issues. A similar program in Brazil is using Big Data techniques to assess viral hepatitis treatment in the public health system. This project involves multi-step assessment integrating government databases, identifying the municipalities with the highest vulnerability for both detection and treatment of cases, and using machine learning to identify patients with vulnerabilities not covered in existing protocols who could benefit from screening.

In Vietnam, with support from PATH and Novartis, the government’s Communities for Healthy Hearts initiative in Hanoi works to reach people where they are with hypertension screening services provided in locations such as markets, nail salons, and coffee shops. Preliminary results indicate significant improvements in the number of people screened and the number of people receiving treatment, with 82% of people diagnosed with hypertension through the initiative currently under treatment, compared to 13.6% of hypertension patients managed at care facilities before the initiative began [ 9 ].

Overstretched health care facilities in China have led officials to promote greater use of telemedicine. The resulting programs include Ping An Good Doctor, an online platform established in 2015 to provide online consultations and appointment bookings. Within two years, it had almost 200 million registered users and over 9000 doctors serving those users [ 9 ].

To identify and reach high-risk patients in Estonia, the World Bank is also conducting an innovative data analysis that uses data analytics and incorporates medical diagnoses and socio-economic data on individuals to better identify patients who were harder to find using traditional economic prediction methods. In Costa Rica, it is using machine learning algorithms to identify patients at high risk of developing diabetes and other chronic health issues such as hypertension, so health brigades can use the risk profiles to take preventive action. In Yemen, it has been able to use GIS to estimate access to health facilities and how that access has changed over time—an example of a situation in which digital health interventions may be the only option as conflict precludes data collection through more traditional methods such as census taking.

Providing more (and better) services

Examples of health interventions leveraging digital health to provide enhanced services cover a myriad of needs employing a wide variety of types of digital health tools, of which the following is only a small sample.

Near real-time data sharing through mTRAC, an SMS-based health reporting program in Uganda to track service quality, drug access, and disease patterns in Uganda, is being used by over 53 000 health workers and was credited with halving response time to disease outbreaks. In 2015, it allowed the ministry of health and district response teams to respond to a typhoid outbreak within hours of its emergence [ 16 ].

AxisMed Brazil has successfully used personal devices provided to chronic patients to track and transmit biometric data to help medical professionals better oversee their treatment plans. Under the intervention, over 80% of the monitored patients have adhered to their treatment plans and emergency department visits have reduced by two-thirds [ 9 , 11 , 17 ].

The e-Health records system and portal in Estonia ensures that every person in the country has an online medical history and integrates data from across health care providers into a single record for each patient. The patient can use it to track their care and receive general health advice, while health care providers can access records to better understand the totality of their patient’s health profile. The electronic health record system pools health data from multiple sources into a single record for each patient using X-Road, a central information exchange layer, that uses blockchain technology to protect data privacy and integrity [ 9 , 11 ].

In Ethiopia, in just 12 months, the Federal Ministry of Health (FMOH), with support from Zenysis, integrated health data from more than 10 disconnected systems into a single platform: the Ethiopian Data Analytics Platform. The FMOH then used the platform to optimize its immunization program and to conduct data analytics that led it to allocate over $100 million to maternal and child health programs [ 9 ].

In the Indian state of Telagana, the government adopted Microsoft Cloud and became the first state to use AI for eye care screening for children [ 18 ]. And in Tanzania, an SMS system to report drug stock-outs to a GIS-enabled command center was able to rapidly halve drug stock outs [ 19 ].

In a practice that could be a game-changer for managing chronic diseases, the Right to Care group in South Africa has installed 5 pharmacy dispensing units in shopping malls and hospitals in Johannesburg. The units connect users to pharmacy assistance to remote consultations before dispensing prescribed medicines — in a process that takes only a few minutes in total. In a country that provides free treatment to 4.2 million people living with HIV and regularly sees people waiting hours to have their prescriptions filled, this offers tremendous promise to slash waiting times at clinics, relieving pressure on patients and the health care system [ 20 , 21 ].

Drones and 3D printers have also been explored as ways to boost physical reach where limitations in the transportation infrastructure pose barriers to effective access to critical services and supplies. To improve access to blood and medical supplies at hospitals in the southern and western sections of the country, the government of Rwanda has successfully partnered with Zipline, a California-based robotics firm, to deliver these items, reducing the delivery time from four hours or more to about 30 minutes [ 22 , 23 ]. More recently expanded to Ghana as well, Zipline’s work in the two countries now delivers over 170 different vaccines, blood products, and medications to 25000 health facilities in the two countries, serving 22 million people [ 24 ]. This example also embodies the concept of leapfrogging, where countries without well-established pre-existing technologies can use emerging technologies to meet a concrete health need without having to invest in the previous, and often more expensive technology (in this case road improvements) [ 9 ]. Inspired by the Zipline example, Nepal’s National Innovation Centre is piloting the Medical Drone Project to deliver medicine packages and laboratory samples to and from remote health posts; and the humanitarian NGO Field Ready deployed a 3D printer in the village of Bhotecaur to print otoscopes on site and at lower cost for immediate use when poor roads, financial constraints and slow bureaucracy made it difficult to access these basic devices in remote facilities [ 25 , 26 ].

Big Data analytics show promise as well. For example, in South Africa, the National Health Laboratory Service and National Department of Health, with support from the World Bank and Boston University, used Big Data analytics to boost HIV service coverage and quality by identifying and targeting support to underperforming facilities [ 27 ].

Finally, health technology assessments can provide decision-makers with quality information to help determine how best to spend available funds—effectively increasing the services available without budget increases. For example, Thailand was an early pioneer among its peers in using of insights from HTAs to develop its universal coverage benefit package. More recently, Turkey formalized HTAs for hospital services to bolster affordability and efficiency [ 27 ].

Reducing the financial burden on individuals (out-of-pocket expenses and cost-sharing)

Reducing the financial burdens on individuals in need of health care is the critical third pillar in the architecture of universal health coverage. In addition to the programs described below, digital health tools such as data analytics and data-driven efficiency assessment modelling tools can be used to improve service targeting and reduce waste. This can reduce some service delivery costs and free up additional money in existing budgets which in turn can be used to create mechanisms to further reduce financial barriers to access.

Connecting people to health financing. In Kenya, 40% of Kenyans who need care cannot afford treatment, nearly 50% of health care expenditures are out-of-pocket expenses, and the traditional, paper-based insurance and voucher systems to help people afford care are slow, unreliable, and not trusted. M-Tiba provides a mobile-based health insurance wallet (Kenya) that users can access to send, save, and spend funds to pay instantly through M-Pesa for medical treatment at over 350 facilities. The health wallet can be used for health savings, health insurance coverage funds and for free health benefits provided by the government or donors, giving those in need immediate, secure access to funds. The program has almost 1 million users, with over 4000 new users per day and has made over US$1 million in medical pay-outs since its launch in 2016 [ 28 ]. The National Hospital Insurance Fund has also partnered with M-Tiba to provide 2000 households with health care insurance and CarePay and Safaricom activated “send money” features allowing people to send health care funds from their M-Pesa accounts to the M-Tiba accounts of others including relatives and household staff [ 29 , 30 ]. The underlying M-Pesa system reaches 93% of Kenyans. In Pakistan, Easypaisa, launched in 2009, is now the world’s third-largest mobile bank, serving over 18 million people [ 31 ]. Keys to success for the Kenya and Pakistan programs have included: streamlined enrolment, governments use the service to disburse payments to individuals, and reduced regulatory requirements by regulating the service as a money transfer service rather than a bank—thus making access easier for the unbanked who are unable to access traditional banking because of regulatory requirements [ 32 - 34 ].

Increasing impact of available health financing support funds for individuals by decreasing fraud. In Zambia, a World Bank team has shown that machine learning approaches, particularly Random Forest, outperformed other fraud detection methods and increased the cost-effectiveness of verification [ 35 ].

Digital identification for health

As countries move toward universal coverage, many new insurance schemes are emerging. Integrating an ID system into these schemes can increase efficiencies and expand inclusion [ 11 ]. These ID programs can help “make the invisible visible” providing identification to those who lack it so they can access social benefits including health care, which, in turn, can reduce the financial burdens they face. To this end, the World Bank Group’s Identification for Development (ID4D) initiative, with support from the Bill & Melinda Gates Foundation, the Omidyar Network and the Australian Government, launched the Mission Billion Challenge in November 2018, to spur efforts to provide IDs to the estimated one billion people globally who currently lack them [ 36 ].

In the context of UHC, digital identification systems have been shown to produce benefits across the three main pillars of action to achieve UHC described above, fostering gains in patient management and treatment, insurance and benefits programs, and data collection for planning and population-level health improvement. Foundational ID systems have received particular attention in this regard. In Estonia, the digital ID card, which is mandatory for all citizens and legal residents over 15 years of age, and the automatic registration at birth of all citizens in the Population Register have been linked to the Estonian Health Information System to expand health care coverage. This includes guaranteeing all children are covered by the national compulsory health insurance program from birth [ 11 ]. In Botswana, linking the national Omang ID card system to the country’s flagship antiretroviral therapy program and related services for diseases such as tuberculosis has given patients easier access to needed support [ 11 ].

In Thailand, the BORA identification system, established in 1984, has been integral in operationalizing the country’s Universal Coverage Scheme (UCS), which guarantees subsidized health care to all citizens. The national ID registers all Thai citizens, eligible migrants, refugees, and stateless people and gives them a unique personal identification number (PIC). Leveraging this system, the UCS reportedly reduced the uninsured portion of the population from 29% to under 5% in under two years [ 9 ]. It also ensures that individuals are automatically moved from one portion of the insurance rolls to another when changes in their work status change the way they are covered. Similarly, in South Korea, the National ID card and ID numbers are integrated into the health care system, supporting the National Health Insurance program, which provides universal health care services, covering close to 97% of the population [ 11 , 37 ]. And technologies from India’s Aadhaar national ID number system, which had issued over 1.19 billion unique ID numbers by the end of 2017, are being used to track the performance of health workers and improve the process of identifying insurance beneficiaries [ 9 , 11 ].

Complex interventions: combining multiple digital health tools in multi-phased responses tied to an overarching goal

In the longer run to more fully leverage the power of digital health requires more complex interventions. These multistage processes often entail building from small, discreet interventions, learning from what did not work, committing to flexible persistence, and integrating the digital health components with many other components that are essential to producing the desired health outcomes. The following two examples offer quick views of different experiences in this regard.

Addressing a need can also entail using multiple types of digital health tools. For example, a World Bank team in Peru is using Big Data (data analytics), GIS, and machine learning to support government decision making to refocus the health system, starting in Lima Province, in light of changing disease burdens. Still in its early stages, the Health Networks Transformation initiative is charged with improving the health of those who lack access to health services, while also improving the governance, efficiency, and equity of the health system: addressing growing needs related to chronic conditions and NCDs, reducing service fragmentation across small facilities, and creating referral pathways to ensure people receive the care they need. In a three-component, integrated initiative, the team has already used large-scale HIS data and analytics to produce a first-round of a strategic map to optimize the network facilities based on current usage and gaps and predictions of future demand. It has also been able to produce preliminary assessments using GIS tools (eg, OpenStreetMap and Waze) and local health data to determine the optimal locations for health facilities to maximize services to meet identified health needs while minimizing costs. Finally, it is using Big Data and machine learning techniques to improve the targeting of care by using machine learning to identify high-performing health facilities and their attributes. While early results are promising, they are preliminary. They have also required the use of a gradual, iterative approach to allow for consensus and trust building, to approach data challenges in terms of both access and quality, and to allow decision-makers to set benchmarks for what the initiative should achieve and how to prioritize various service goals.

As the case study from Pakistan in Box 1 demonstrates, moving from reactive interventions addressing an immediate need to a more systematic program that more fully leverages the potential of digital health to extend and improve health services and outcomes is an iterative process requiring perseverance and attention to a number of other factors beyond the digital health tools.

Phased progress towards a systemized use of digital health: multiple rounds of innovation and multiple keys to success – a case study from Punjab

The widely lauded digital health program in the Punjab region of Pakistan did not start out as a grand plan. Its origins lay largely in an unsuccessful attempt to counteract a dengue outbreak in 2011. It began as a straightforward effort using a single intervention to address a single challenge is a small area: providing health inspectors with smartphones loaded with a special app to use in inspecting health care facilities in a single district. The plan came about too late to succeed. However, with leadership from Shehbaz Sharif, the chief minister of Punjab, the failure became a building block to create a plan for future success: leading to the development of a system, leveraging World Bank technical assistance and financing from its Innovation Fund, in advance of the 2012 dengue season to monitor prevention activities and identify hotspots across Punjab. With funding secured, the government expanded its small, existing IT department to hire software developers and other technical staff including highly capable computer scientists and a manager to develop custom-tailored apps, and partnered with a local university to develop Data Plug, a specialized support platform. The resulting apps were then loaded onto smartphones and distributed to inspectors; and the program was credited with helping to slow the spread of dengue in 2012 in the region. Then, building on success and innovating again for broader impact, further collaborative innovation between the provincial government and the World Bank explored further potential gains, conducting a randomized control trial that expanded the project to half of Punjab’s districts and showed a large increase in attendance at facilities that were monitored by smartphone-equipped inspectors.

Insights from the trial were used to build again: this time to develop a broader Punjab Management Reform Program, launched in November 2013 targeting five departments including health. A new app for vaccinators distributed on smartphones in 2014 required vaccinators to use the app to check in and upload data at several designated locations during the work day. Vaccinator attendance increased from 36% to over 80% in just four months. But the locations covered remained static, focused on places easiest for the vaccinators to reach. This led to another round of technical partnership and innovation to produce a mapping system (using GIS and machine learning) that divided the province into polygons representing each neighborhood and provided real-time reporting to indicate when a neighborhood was not receiving enough visits so health officials could quickly redeploy vaccinators to rectify the problem. Result: the percentage of polygons reached by vaccinators rose from 25% when the program was launched in 2014 to 88% by May 2016 and the percentage of fully immunized children rose from 62% to 81%, with 95% of children fully vaccinated against polio. By 2017, health inspectors were using the updated application and a counterpart app to monitor over 3000 facilities across the province to check staff attendance, medicine availability, equipment conditions, and facility maintenance, with the uploaded information linked to a dashboard the health department could use to track the performance of both hospitals and inspectors. Pressure on inspectors to inflate results also decreased since reporting became instantaneous and automated, and transparency improved with the Punjab government creating an open government website ( http://open.punjab.gov.pk ) that made data from multiple departments and projects including the vaccination program available to the public.

Lessons learned: getting the technology right is critical but not enough. Success also depended on: (i) building positive relationships between IT teams and other staff and users so they could collaborate to “co-create” solutions; (ii) political leadership; (iii) increased institutional capacity; (iv) providing incentives for people to use the intervention; and (v) creating transparency [ 38 , 39 ].

Insights from the field: key principles learned

The following insights, gained from experience implementing digital health interventions to date, provide learned key principles that should be leveraged as we strive to move towards smarter, systems-oriented decision-making on how to use and integrate digital interventions into planning and programming to achieve UHC.

From low-hanging fruit to moonshots

As seen in the Punjab example, making use of digital health interventions is an evolving process, not a one-time decision point. As needs and capabilities evolve, so do the options for effective use of digital interventions. Uses that would have been impossible at first, become attainable as capabilities improve, allowing decision makers-to move from using simple interventions to solve easy “low-hanging fruit” challenges to eventually taking on larger “moon-shot” challenges such as a fully integrated, eHealth system to manage universal health care as is under way in countries such as South Korea, Thailand, and Estonia.

This also ties into another important key need going forward: a sustained press to ensure movement from piloting to systematic reform. Using data to reshape health systems can and should be a key pillar of this effort.

Distinguishing between hype and substance: informed technology selection

To date, health decision-makers have lacked resources they can turn to for evidence-based methods that can be used to determine which digital health intervention (if any) is well-suited to meet a particular need in their situation. Efforts are under way to help countries better tackle these challenges, although much more work remains to be done. The World Health Organization’s 2019 Guideline Recommendations on Digital Interventions for Health System Strengthening is the first such guideline by the organization and is designed to help decision-makers make informed decisions about which digital health interventions to invest in [ 10 ]. The US Agency for International Development’s Center for Innovation and Impact is also partnering with the Bill & Melinda Gates Foundation and The Rockefeller foundation to explore use of AI in global health and to share its findings with stakeholders around the globe through various means including the report Artificial Intelligence in Global Health [ 40 ]. The long-term goal must be to create reliable, evidence-based methods for such decision-making that can be adapted to take into consideration the needs, priorities, strengths, and limitations faced in a particular context at a particular time.

Moving beyond a use-case mentality: creating AI-enabled health delivery systems and analytics

At first, a health system’s use of digital health interventions is often episodic: identifying a particular tool that can address a discreet, immediate need within a particular health intervention area, such as a smartphone app to share HIV prevention messages with at-risk youth in a particular area or community. However, to fully leverage the potential of digital interventions to further the goal of people-centered, sustainable, and equitable UHC, use of digital interventions should, as capabilities develop, shift to developing broader delivery systems and analytics that are fully AI-enabled. Developments in South Korea and Estonia provide examples of varying paths towards this goal.

Identify and be clear about likelihood of improved impact and the likely amount of gain

Determining that a particular digital intervention would have an impact is not enough. Those conducting such assessments should be careful to make sure their evaluations reflect the effects of size and variance. This is a widespread issue, but particularly true of “nudge” interventions that would have a marginal impact on outcomes. It is also important to note that analysts too often tell decision-makers that x “works” rather than stating that doing x may improve outcomes fractionally.

Better understanding v. better action: bridging the gap

There are still many cases in which there is a failure to translate insights about what is happening and why it is happening into concrete policies and programs that actually improve health outcomes.

Digital context

Digital health interventions should only be used when they have been properly vetted and determined to be well suited to addressing a given need. But even in those conditions, they will be hard pressed to succeed unless the necessary support environment exists. For example, as noted in the report of the Working Group on Digital Health of the Broadband Commission for Sustainable Development, The Promise of Digital Health: Addressing Non-communicable Diseases to Accelerate Universal Health Coverage in LMICs , to maximize impact, such interventions must be supported by foundational building blocks including: vision and leadership, regulations and policies, communications infrastructure and common platforms, interoperability frameworks (to allow different data sources and systems to connect with each other), partnerships involving a variety of stakeholders, and sustainable financing models [ 9 ].

Country context

As the examples above demonstrate, countries have different needs, different strengths, different priorities, and different limitations. In addition, as seen in the multi-year, multi-phase project in Pakistan/Punjab, those factors can also change for a given community over time. To succeed, digital health interventions must reflect and adapt to those realities. Efforts to develop stakeholder understanding, trust, and inclusion are also critical as are effective measures to address social and economic inclusion needs.

Data: access, quantity, quality and ability to share

The existence of sufficient, relevant, reliable data should not be taken for granted, even in countries that have made significant progress in establishing eHealth records. Moreover, even when quality data exist and privacy standards have been protected, the data owners may want to establish a sense of trust in the proposed intervention plan before agreeing to share the data.

Privacy and control of data

As touched upon above, privacy is and should be a primary concern in establishing programs and processes that protect individuals’ rights to privacy and enable them to have greater control over the use of their data. Legal and regulatory frameworks are a critical component of effective systems to protect data privacy. Much work remains to be done, but some progress is being made. For example, the 2014 African Union Convention on Cyber-Security and Personal Data Protection sets standards that can be used to both establish domestic frameworks and to harmonize those frameworks to strengthen data process across the region [ 9 ]. The World Bank’s Principles on Identification for Sustainable Development also offer guidance on data protection, security, and privacy in contexts involving unique IDs and health systems [ 41 ].

Blockchain technology may prove useful in addressing certain types of privacy concerns. For example, Estonia uses blockchain to ensure data privacy and integrity in its national e-Health system. Furthermore, as noted in a report by the Broadband Commission, blockchain may be particularly useful in safeguarding health data privacy in UHC settings, which, by definition, entail connecting large amounts of health data from different sources. That said, use of blockchain in health is still generally in early stages of development because the legal and regulatory framework, technical capacities, and incentive structures are still under development. In the interim, the use of personal identification codes in countries with digital health IDs such as Thailand and Estonia has helped to reduce some of the risk [ 9 ].

While these insights and examples from the field do not provide a perfect roadmap for every country working to leverage technology to achieve UHC, they do provide important road signs to help countries to stay on course and avoid numerous detours that would slow progress. It is also important to remember that success ultimately also depends on creating a supportive enabling environment including the legal, logistical, and infrastructure frameworks on which digital health tools and UHC depend. However, as noted in this chapter, the promise is real and substantial. And given the scale of the challenge of achieving effective and robust UHC for all people in all countries—a challenge which the COVID-19 pandemic has both heightened and highlighted—these opportunities must be seized and realized.

Acknowledgments

Disclaimer: All authors contributed in their individual capacities and the views that are expressed in this commentary do not necessarily reflect the views of their respective organizations.

Funding: Funded by Deloitte Consulting US, Brandeis University Heller School of Social Policy, and The World Bank.

Authorship declaration: DW, MG and KW conceived and drafted the manuscript including revisions; AS contributed to the revisions. All authors read and approved the final transcript.

Competing interest: The authors completed the ICMJE Declaration of Interest Form (available upon request from the corresponding author), and declare no conflicts of interest.

• Arts & Culture
• Business and Economy
• Campus Life
• Education & Society
• Environment
• Heritage/Tradition
• International
• Law & Politics
• Podcast: Listen in, Michigan
• Research News
• Science & Technology
• President’s Message
• Editor’s Blog
• Climate Blue
• Health Yourself
• Talking About Words
• Talking About Movies
• Talking About Books
• Up In Your Business
• Alumni Books
• Alumni Memories
• Alumni Notes
• X (Twitter)
• update your information

Office of the VP for Communications – Keeping alumni and friends connected to U-M

Too much screen time? U-M pioneers digital wellness program for youths

Sixth grader Sera Bergman at Scarlett Middle School. (Image credit: Fernanda Pires.)

‘Just one more’

Sixth grader Sera Bergman confesses she spends a significant amount of time watching reels — and enjoys it, like most kids her age. Once she starts scrolling through the short videos, stopping is challenging.

“When I am in the car, I think I will just watch a couple of YouTube shorts before I get somewhere,” said Bergman, who attends Scarlett Middle School in Ann Arbor. “But then when I get out of the car, I’ll be like, ‘Just one more.’ It is super addictive. When creating games and social media apps, designers find ways to make us addicted to them.”

Addiction, cyberbullying, eating disorders, anxiety, and other mental health issues caused by problematic digital practices and an increase in screen time are some of the themes of a new and unique University of Michigan interprofessional Peer-to-Peer Digital Wellness class.

This semester, U-M students and scholars launched an interprofessional course in partnership with sixth graders from Ann Arbor Public Schools to provide classroom and real-world engagement about digital wellness.

“Evidence suggests the COVID-19 pandemic has intensified mental health issues and shifted social engagement to digital platforms,” said Liz Kolb, clinical professor at U-M’s Marsal Family School of Education. “With an increasing reliance on screens as primary tools of learning, entertainment and socialization, there is a critical need to educate students about digital wellness.

Students participated in the Digital Wellness Symposium at U-M North Quad. (Image credit: Niki Williams.)

“Enhancing digital wellness, encompassing online engagement activities and emotional experiences, is crucial for students’ emotional, intellectual, and social well-being.”

The current digital wellness program evolved from the digital citizenship curriculum designed by Kolb. The curriculum she launched at Scarlett Middle School began with a focus on bullying, privacy, and online safety. As the concerns of parents, teachers, and scholars around the country have mounted, the new digital wellness program has shifted toward a broader conversation with kids: “What impact are these devices having on me?”

The program is a collaboration between the Marsal Family School of Education, School of Information, and School of Social Work. The U-M student mentors are undergraduate and graduate students from these schools taking a digital wellness course.

“Most education around digital device use for young people has focused on safety lectures and lists of ‘do’s and don’ts’ coming from adults and authority figures,” Kolb said. “These approaches do not often work at helping young people understand the impact of their device on their individual mental and physical health, and rarely cause young people to change habits.

“This course takes a different approach, giving young people — both college and middle school students—scientific information about what happens to our bodies when using screens, both the benefits and harms.”

Screen test

This first class includes 52 sixth graders from Scarlett, Tappan, and Clague middle schools and 11 U-M students.

Scarlett Middle School students. (Image credit: Fernanda Pires.)

Besides getting internship credit for the class and seeing digital wellness as an area of interest after graduation, master’s student Wanting Qian, majoring in education studies, decided to take this course for its interdisciplinarity.

“This course is co-taught by the schools of Social Work, Education, and Information, and I want to understand how these three aspects work together,” she said. “I also needed hands-on experience to put theory into practice.”

Qian’s studies are concentrated on design and technologies for learning across cultures and contexts, and she has no doubt that this experience will benefit her future career.

“First, the understanding of trauma-informed practice,” she said. “This is a concept and theory every teacher should be aware of and integrate into their teaching, considering students’ prior experiences and personalities, and being culturally responsive.

“Second, technology is rapidly developing in today’s world. In addition to investing in new technology, we must critically examine how it impacts our lives and what we should do when facing negative influences, especially for the younger generation.”

Near-peer approach

Muneer Khalid of the U-M Center for Research on Learning and Teaching has been working closely with Kolb and her colleagues Kristin Fontichiaro, clinical professor of information, and Beth Sherman, clinical associate professor of social work, to develop and support the new class.

According to the researchers, it has been surprising to see what the sixth graders and college students have in common regarding their device use and mental health struggles. They hope many schools throughout Michigan and the United States can replicate this digital wellness program.

Muneer Khalid of the U-M Center for Research on Learning and Teaching. (Image credit: Fernanda Pires.)

“Students of all ages have been able to share stories, engage in conversation, and debate solutions to their challenges,” Kolb said. “This near-peer approach seems to be leading to more long-term change of habit or, at the very least, an understanding of how individual feelings and emotions are impacted through screen time.

“This project has had a lot of joy, which feels different from the shame often associated with school-related talks/lectures on digital safety and citizenship. Engaging with digital devices in a healthy way should feel good.”

For sixth grader Oliver Thomas, who attends Scarlett Middle School, balance has been one of the program’s big takeaways.

“I learned that technology isn’t a really bad thing,” he said. “It can be bad in some cases, so you just have to monitor it. We learned that social media, for example, can lead to higher anxiety and depression rates. So, I have to be smarter about how much I use social media, if at all. We should try to put it off for as long as we can. But if we decide to use it, we should be smart and put a time limit on it.”

(Lead image: Students participated in the Digital Wellness Symposium at U-M North Quad. Image credit: Niki Williams.)

Leave a comment: Cancel reply

Please enable JavaScript to submit this form.

• Philanthropy
• Podcast: "Listen in, Michigan"
• Science and Technology

• U-M Privacy Policy
• Giving to U-M
• Publications & Resources
• U-M Alumni Association
• Update Your Information
• Unsubscribe
• Podcast: “Listen in, Michigan”

Upcoming Dissertation Defenses

News from the School

From public servant to public health student

Exploring the intersection of health, mindfulness, and climate change

Conference aims to help experts foster health equity

Building solidarity to face global injustice