essay on technology in healthcare

Health Care Comes Home: The Human Factors (2011)

Chapter: 7 conclusions and recommendations.

7 Conclusions and Recommendations

Health care is moving into the home increasingly often and involving a mixture of people, a variety of tasks, and a broad diversity of devices and technologies; it is also occurring in a range of residential environments. The factors driving this migration include the rising costs of providing health care; the growing numbers of older adults; the increasing prevalence of chronic disease; improved survival rates of various diseases, injuries, and other conditions (including those of fragile newborns); large numbers of veterans returning from war with serious injuries; and a wide range of technological innovations. The health care that results varies considerably in its safety, effectiveness, and efficiency, as well as its quality and cost.

The committee was charged with examining this major trend in health care delivery and resulting challenges from only one of many perspectives: the study of human factors. From the outset it was clear that the dramatic and evolving change in health care practice and policies presents a broad array of opportunities and problems. Consequently the committee endeavored to maintain focus specifically on how using the human factors approach can provide solutions that support maximizing the safety and quality of health care delivered in the home while empowering both care recipients and caregivers in the effort.

The conclusions and recommendations presented below reflect the most critical steps that the committee thinks should be taken to improve the state of health care in the home, based on the literature reviewed in this report examined through a human factors lens. They are organized into four areas: (1) health care technologies, including medical devices and health information technologies involved in health care in the home; (2)

caregivers and care recipients; (3) residential environments for health care; and (4) knowledge gaps that require additional research and development. Although many issues related to home health care could not be addressed, applications of human factors principles, knowledge, and research methods in these areas could make home health care safer and more effective and also contribute to reducing costs. The committee chose not to prioritize the recommendations, as they focus on various aspects of health care in the home and are of comparable importance to the different constituencies affected.

HEALTH CARE TECHNOLOGIES

Health care technologies include medical devices that are used in the home as well as information technologies related to home-based health care. The four recommendations in this area concern (1) regulating technologies for health care consumers, (2) developing guidance on the structure and usability of health information technologies, (3) developing guidance and standards for medical device labeling, and (4) improving adverse event reporting systems for medical devices. The adoption of these recommendations would improve the usability and effectiveness of technology systems and devices, support users in understanding and learning to use them, and improve feedback to government and industry that could be used to further improve technology for home care.

Ensuring the safety of emerging technologies is a challenge, in part because it is not always clear which federal agency has regulatory authority and what regulations must be met. Currently, the U.S. Food and Drug Administration (FDA) has responsibility for devices, and the Office of the National Coordinator for Health Information Technology (ONC) has similar authority with respect to health information technology. However, the dividing line between medical devices and health information technology is blurring, and many new systems and applications are being developed that are a combination of the two, although regulatory oversight has remained divided. Because regulatory responsibility for them is unclear, these products may fall into the gap.

The committee did not find a preponderance of evidence that knowledge is lacking for the design of safe and effective devices and technologies for use in the home. Rather than discovering an inadequate evidence base, we were troubled by the insufficient attention directed at the development of devices that account, necessarily and properly, for users who are inadequately trained or not trained at all. Yet these new users often must

rely on equipment without ready knowledge about limitations, maintenance requirements, and problems with adaptation to their particular home settings.

The increased prominence of the use of technology in the health care arena poses predictable challenges for many lay users, especially people with low health literacy, cognitive impairment, or limited technology experience. For example, remote health care management may be more effective when it is supported by technology, and various electronic health care (“e-health”) applications have been developed for this purpose. With the spectrum of caregivers ranging from individuals caring for themselves or other family members to highly experienced professional caregivers, computer-based care management systems could offer varying levels of guidance, reminding, and alerting, depending on the sophistication of the operator and the criticality of the message. However, if these technologies or applications are difficult to understand or use, they may be ignored or misused, with potentially deleterious effects on care recipient health and safety. Applying existing accessibility and usability guidelines and employing user-centered design and validation methods in the development of health technology products designed for use in the home would help ensure that they are safe and effective for their targeted user populations. In this effort, it is important to recognize how the line between medical devices and health information technologies has become blurred while regulatory oversight has remained distinct, and it is not always clear into which domain a product falls.

Recommendation 1. The U.S. Food and Drug Administration and the Office of the National Coordinator for Health Information Technology should collaborate to regulate, certify, and monitor health care applications and systems that integrate medical devices and health information technologies. As part of the certification process, the agencies should require evidence that manufacturers have followed existing accessibility and usability guidelines and have applied user-centered design and validation methods during development of the product.

Guidance and Standards

Developers of information technologies related to home-based health care, as yet, have inadequate or incomplete guidance regarding product content, structure, accessibility, and usability to inform innovation or evolution of personal health records or of care recipient access to information in electronic health records.

The ONC, in the initial announcement of its health information technology certification program, stated that requirements would be forthcom-

ing with respect both to personal health records and to care recipient access to information in electronic health records (e.g., patient portals). Despite the importance of these requirements, there is still no guidance on the content of information that should be provided to patients or minimum standards for accessibility, functionality, and usability of that information in electronic or nonelectronic formats.

Consequently, some portals have been constructed based on the continuity of care record. However, recent research has shown that records and portals based on this model are neither understandable nor interpretable by laypersons, even by those with a college education. The lack of guidance in this area makes it difficult for developers of personal health records and patient portals to design systems that fully address the needs of consumers.

Recommendation 2. The Office of the National Coordinator for Health Information Technology, in collaboration with the National Institute of Standards and Technology and the Agency for Healthcare Research and Quality, should establish design guidelines and standards, based on existing accessibility and usability guidelines, for content, accessibility, functionality, and usability of consumer health information technologies related to home-based health care.

The committee found a serious lack of adequate standards and guidance for the labeling of medical devices. Furthermore, we found that the approval processes of the FDA for changing these materials are burdensome and inflexible.

Just as many medical devices currently in use by laypersons in the home were originally designed and approved for use only by professionals in formal health care facilities, the instructions for use and training materials were not designed for lay users, either. The committee recognizes that lack of instructional materials for lay users adds to the level of risk involved when devices are used by populations for whom they were not intended.

Ironically, the FDA’s current premarket review and approval processes inadvertently discourage manufacturers from selectively revising or developing supplemental instructional and training materials, when they become aware that instructional and training materials need to be developed or revised for lay users of devices already approved and marketed. Changing the instructions for use (which were approved with the device) requires manufacturers to submit the device along with revised instructions to the FDA for another 510(k) premarket notification review. Since manufacturers can find these reviews complicated, time-consuming, and expensive, this requirement serves as a disincentive to appropriate revisions of instructional or training materials.

Furthermore, little guidance is currently available on design of user

training methods and materials for medical devices. Even the recently released human factors standard on medical device design (Association for the Advancement of Medical Instrumentation, 2009), while reasonably comprehensive, does not cover the topic of training or training materials. Both FDA guidance and existing standards that do specifically address the design of labeling and ensuing instructions for use fail to account for up-to-date findings from research on instructional systems design. In addition, despite recognition that requirements for user training, training materials, and instructions for use are different for lay and professional users of medical equipment, these differences are not reflected in current standards.

Recommendation 3. The U.S. Food and Drug Administration (FDA) should promote development (by standards development organizations, such as the International Electrotechnical Commission, the International Organization for Standardization, the American National Standards Institute, and the Association for the Advancement of Medical Instrumentation) of new standards based on the most recent human factors research for the labeling of and ensuing instructional materials for medical devices designed for home use by lay users. The FDA should also tailor and streamline its approval processes to facilitate and encourage regular improvements of these materials by manufacturers.

Adverse Event Reporting Systems

The committee notes that the FDA’s adverse event reporting systems, used to report problems with medical devices, are not user-friendly, especially for lay users, who generally are not aware of the systems, unaware that they can use them to report problems, and uneducated about how to do so. In order to promote safe use of medical devices in the home and rectify design problems that put care recipients at risk, it is necessary that the FDA conduct more effective postmarket surveillance of medical devices to complement its premarket approval process. The most important elements of their primarily passive surveillance system are the current adverse event reporting mechanisms, including Maude and MedSun. Entry of incident data by health care providers and consumers is not straightforward, and the system does not elicit data that could be useful to designers as they develop updated versions of products or new ones that are similar to existing devices. The reporting systems and their importance need to be widely promoted to a broad range of users, especially lay users.

Recommendation 4. The U.S. Food and Drug Administration should improve its adverse event reporting systems to be easier to use, to collect data that are more useful for identifying the root causes of events

related to interactions with the device operator, and to develop and promote a more convenient way for lay users as well as professionals to report problems with medical devices.

CAREGIVERS IN THE HOME

Health care is provided in the home by formal caregivers (health care professionals), informal caregivers (family and friends), and individuals who self-administer care; each type of caregiver faces unique issues. Properly preparing individuals to provide care at home depends on targeting efforts appropriately to the background, experience, and knowledge of the caregivers. To date, however, home health care services suffer from being organized primarily around regulations and payments designed for inpatient or outpatient acute care settings. Little attention has been given to how different the roles are for formal caregivers when delivering services in the home or to the specific types of training necessary for appropriate, high-quality practice in this environment.

Health care administration in the home commonly involves interaction among formal caregivers and informal caregivers who share daily responsibility for a person receiving care. But few formal caregivers are given adequate training on how to work with informal caregivers and involve them effectively in health decision making, use of medical or adaptive technologies, or best practices to be used for evaluating and supporting the needs of caregivers.

It is also important to recognize that the majority of long-term care provided to older adults and individuals with disabilities relies on family members, friends, or the individual alone. Many informal caregivers take on these responsibilities without necessary education or support. These individuals may be poorly prepared and emotionally overwhelmed and, as a result, experience stress and burden that can lead to their own morbidity. The committee is aware that informational and training materials and tested programs already exist to assist informal caregivers in understanding the many details of providing health care in the home and to ease their burden and enhance the quality of life of both caregiver and care recipient. However, tested materials and education, support, and skill enhancement programs have not been adequately disseminated or integrated into standard care practices.

Recommendation 5. Relevant professional practice and advocacy groups should develop appropriate certification, credentialing, and/or training standards that will prepare formal caregivers to provide care in the home, develop appropriate informational and training materials

for informal caregivers, and provide guidance for all caregivers to work effectively with other people involved.

RESIDENTIAL ENVIRONMENTS FOR HEALTH CARE

Health care is administered in a variety of nonclinical environments, but the most common one, particularly for individuals who need the greatest level and intensity of health care services, is the home. The two recommendations in this area encourage (1) modifications to existing housing and (2) accessible and universal design of new housing. The implementation of these recommendations would be a good start on an effort to improve the safety and ease of practicing health care in the home. It could improve the health and safety of many care recipients and their caregivers and could facilitate adherence to good health maintenance and treatment practices. Ideally, improvements to housing design would take place in the context of communities that provide transportation, social networking and exercise opportunities, and access to health care and other services.

Safety and Modification of Existing Housing

The committee found poor appreciation of the importance of modifying homes to remove health hazards and barriers to self-management and health care practice and, furthermore, that financial support from federal assistance agencies for home modifications is very limited. The general connection between housing characteristics and health is well established. For example, improving housing conditions to enhance basic sanitation has long been part of a public health response to acute illness. But the characteristics of the home can present significant barriers to autonomy or self-care management and present risk factors for poor health, injury, compromised well-being, and greater dependence on others. Conversely, physical characteristics of homes can enhance resident safety and ability to participate in daily self-care and to utilize effectively health care technologies that are designed to enhance health and well-being.

Home modifications based on professional home assessments can increase functioning, contribute to reducing accidents such as falls, assist caregivers, and enable chronically ill persons and people with disabilities to stay in the community. Such changes are also associated with facilitating hospital discharges, decreasing readmissions, reducing hazards in the home, and improving care coordination. Familiar modifications include installation of such items as grab bars, handrails, stair lifts, increased lighting, and health monitoring equipment as well as reduction of such hazards as broken fixtures and others caused by insufficient home maintenance.

Deciding on which home modifications have highest priority in a given

setting depends on an appropriate assessment of circumstances and the environment. A number of home assessment instruments and programs have been validated and proven to be effective to meet this need. But even if needed modifications are properly identified and prioritized, inadequate funding, gaps in services, and lack of coordination between the health and housing service sectors have resulted in a poorly integrated system that is difficult to access. Even when accessed, progress in making home modifications available has been hampered by this lack of coordination and inadequate reimbursement or financial mechanisms, especially for those who cannot afford them.

Recommendation 6. Federal agencies, including the U.S. Department of Health and Human Services and the Centers for Medicare & Medicaid Services, along with the U.S. Department of Housing and Urban Development and the U.S. Department of Energy, should collaborate to facilitate adequate and appropriate access to health- and safety-related home modifications, especially for those who cannot afford them. The goal should be to enable persons whose homes contain obstacles, hazards, or features that pose a home safety concern, limit self-care management, or hinder the delivery of needed services to obtain home assessments, home modifications, and training in their use.

Accessibility and Universal Design of New Housing

Almost all existing housing in the United States presents problems for conducting health-related activities because physical features limit independent functioning, impede caregiving, and contribute to such accidents as falls. In spite of the fact that a large and growing number of persons, including children, adults, veterans, and older adults, have disabilities and chronic conditions, new housing continues to be built that does not account for their needs (current or future). Although existing homes can be modified to some extent to address some of the limitations, a proactive, preventive, and effective approach would be to plan to address potential problems in the design phase of new and renovated housing, before construction.

Some housing is already required to be built with basic accessibility features that facilitate practice of health care in the home as a result of the Fair Housing Act Amendments of 1998. And 17 states and 30 cities have passed what are called “visitability” codes, which currently apply to 30,000 homes. Some localities offer tax credits, such as Pittsburgh through an ordinance, to encourage installing visitability features in new and renovated housing. The policy in Pittsburgh was impetus for the Pennsylvania Residential VisitAbility Design Tax Credit Act signed into law on October 28, 2006, which offers property owners a tax credit for new construction

and rehabilitation. The Act paves the way for municipalities to provide tax credits to citizens by requiring that such governing bodies administer the tax credit (Self-Determination Housing Project of Pennsylvania, Inc., n.d.).

Visitability, rather than full accessibility, is characterized by such limited features as an accessible entry into the home, appropriately wide doorways and one accessible bathroom. Both the International Code Council, which focuses on building codes, and the American National Standards Institute, which establishes technical standards, including ones associated with accessibility, have endorsed voluntary accessibility standards. These standards facilitate more jurisdictions to pass such visitability codes and encourage legislative consistency throughout the country. To date, however, the federal government has not taken leadership to promote compliance with such standards in housing construction, even for housing for which it provides financial support.

Universal design, a broader and more comprehensive approach than visitability, is intended to suit the needs of persons of all ages, sizes, and abilities, including individuals with a wide range of health conditions and activity limitations. Steps toward universal design in renovation could include such features as anti-scald faucet valve devices, nonslip flooring, lever handles on doors, and a bedroom on the main floor. Such features can help persons and their caregivers carry out everyday tasks and reduce the incidence of serious and costly accidents (e.g., falls, burns). In the long run, implementing universal design in more homes will result in housing that suits the long-term needs of more residents, provides more housing choices for persons with chronic conditions and disabilities, and causes less forced relocation of residents to more costly settings, such as nursing homes.

Issues related to housing accessibility have been acknowledged at the federal level. For example, visitability and universal design are in accord with the objectives of the Safety of Seniors Act (Public Law No. 110-202, passed in 2008). In addition, implementation of the Olmstead decision (in which the U.S. Supreme Court ruled that the Americans with Disabilities Act may require states to provide community-based services rather than institutional placements for individuals with disabilities) requires affordable and accessible housing in the community.

Visitability, accessibility, and universal design of housing all are important to support the practice of health care in the home, but they are not broadly implemented and incentives for doing so are few.

Recommendation 7. Federal agencies, such as the U.S. Department of Housing and Urban Development, the U.S. Department of Veterans Affairs, and the Federal Housing Administration, should take a lead role, along with states and local municipalities, to develop strategies that promote and facilitate increased housing visitability, accessibil-

ity, and universal design in all segments of the market. This might include tax and other financial incentives, local zoning ordinances, model building codes, new products and designs, and related policies that are developed as appropriate with standards-setting organizations (e.g., the International Code Council, the International Electrotechnical Commission, the International Organization for Standardization, and the American National Standards Institute).

RESEARCH AND DEVELOPMENT

In our review of the research literature, the committee learned that there is ample foundational knowledge to apply a human factors lens to home health care, particularly as improvements are considered to make health care safe and effective in the home. However, much of what is known is not being translated effectively into practice, neither in design of equipment and information technology or in the effective targeting and provision of services to all those in need. Consequently, the four recommendations that follow support research and development to address knowledge and communication gaps and facilitate provision of high-quality health care in the home. Specifically, the committee recommends (1) research to enhance coordination among all the people who play a role in health care practice in the home, (2) development of a database of medical devices in order to facilitate device prescription, (3) improved surveys of the people involved in health care in the home and their residential environments, and (4) development of tools for assessing the tasks associated with home-based health care.

Health Care Teamwork and Coordination

Frail elders, adults with disabilities, disabled veterans, and children with special health care needs all require coordination of the care services that they receive in the home. Home-based health care often involves a large number of elements, including multiple care providers, support services, agencies, and complex and dynamic benefit regulations, which are rarely coordinated. However, coordinating those elements has a positive effect on care recipient outcomes and costs of care. When successful, care coordination connects caregivers, improves communication among caregivers and care recipients and ensures that receivers of care obtain appropriate services and resources.

To ensure safe, effective, and efficient care, everyone involved must collaborate as a team with shared objectives. Well-trained primary health care teams that execute customized plans of care are a key element of coordinated care; teamwork and communication among all actors are also

essential to successful care coordination and the delivery of high-quality care. Key factors that influence the smooth functioning of a team include a shared understanding of goals, common information (such as a shared medication list), knowledge of available resources, and allocation and coordination of tasks conducted by each team member.

Barriers to coordination include insufficient resources available to (a) help people who need health care at home to identify and establish connections to appropriate sources of care, (b) facilitate communication and coordination among caregivers involved in home-based health care, and (c) facilitate communication among the people receiving and the people providing health care in the home.

The application of systems analysis techniques, such as task analysis, can help identify problems in care coordination systems and identify potential intervention strategies. Human factors research in the areas of communication, cognitive aiding and decision support, high-fidelity simulation training techniques, and the integration of telehealth technologies could also inform improvements in care coordination.

Recommendation 8 . The Agency for Healthcare Research and Quality should support human factors–based research on the identified barriers to coordination of health care services delivered in the home and support user-centered development and evaluation of programs that may overcome these barriers.

Medical Device Database

It is the responsibility of physicians to prescribe medical devices, but in many cases little information is readily available to guide them in determining the best match between the devices available and a particular care recipient. No resource exists for medical devices, in contrast to the analogous situation in the area of assistive and rehabilitation technologies, for which annotated databases (such as AbleData) are available to assist the provider in determining the most appropriate one of several candidate devices for a given care recipient. Although specialists are apt to receive information about devices specific to the area of their practice, this is much less likely in the case of family and general practitioners, who often are responsible for selecting, recommending, or prescribing the most appropriate device for use at home.

Recommendation 9. The U.S. Food and Drug Administration, in collaboration with device manufacturers, should establish a medical device database for physicians and other providers, including pharmacists, to use when selecting appropriate devices to prescribe or recommend

for people receiving or self-administering health care in the home. Using task analysis and other human factors approaches to populate the medical device database will ensure that it contains information on characteristics of the devices and implications for appropriate care recipient and device operator populations.

Characterizing Caregivers, Care Recipients, and Home Environments

As delivery of health care in the home becomes more common, more coherent strategies and effective policies are needed to support the workforce of individuals who provide this care. Developing these will require a comprehensive understanding of the number and attributes of individuals engaged in health care in the home as well as the context in which care is delivered. Data and data analysis are lacking to accomplish this objective.

National data regarding the numbers of individuals engaged in health care delivery in the home—that is, both formal and informal caregivers—are sparse, and the estimates that do exist vary widely. Although the Bureau of Labor Statistics publishes estimates of the number of workers employed in the home setting for some health care classifications, they do not include all relevant health care workers. For example, data on workers employed directly by care recipients and their families are notably absent. Likewise, national estimates of the number of informal caregivers are obtained from surveys that use different methodological approaches and return significantly different results.

Although numerous national surveys have been designed to answer a broad range of questions regarding health care delivery in the home, with rare exceptions such surveys reflect the relatively limited perspective of the sponsoring agency. For example,

• The Medicare Current Beneficiary Survey (administered by the Centers for Medicare & Medicaid Services) and the Health and Retirement Survey (administered by the National Institute on Aging) are primarily geared toward understanding the health, health services use, and/or economic well-being of older adults and provide no information regarding working-age adults or children or information about home or neighborhood environments.
• The Behavioral Risk Factors Surveillance Survey (administered by the Centers for Disease Control and Prevention, CDC), the National Health Interview Survey (administered by the CDC), and the National Children’s Study (administered by the U.S. Department of Health and Human Services and the U.S. Environmental Protection Agency) all collect information on health characteristics, with limited or no information about the housing context.
• The American Housing Survey (administered by the U.S. Department of Housing and Urban Development) collects detailed information regarding housing, but it does not include questions regarding the health status of residents and does not collect adequate information about home modifications and features on an ongoing basis.

Consequently, although multiple federal agencies collect data on the sociodemographic and health characteristics of populations and on the nation’s housing stock, none of these surveys collects data necessary to link the home, its residents, and the presence of any caregivers, thus limiting understanding of health care delivered in the home. Furthermore, information is altogether lacking about health and functioning of populations linked to the physical, social, and cultural environments in which they live. Finally, in regard to individuals providing care, information is lacking regarding their education, training, competencies, and credentialing, as well as appropriate knowledge about their working conditions in the home.

Better coordination across government agencies that sponsor such surveys and more attention to information about health care that occurs in the home could greatly improve the utility of survey findings for understanding the prevalence and nature of health care delivery in the home.

Recommendation 10. Federal health agencies should coordinate data collection efforts to capture comprehensive information on elements relevant to health care in the home, either in a single survey or through effective use of common elements across surveys. The surveys should collect data on the sociodemographic and health characteristics of individuals receiving care in the home, the sociodemographic attributes of formal and informal caregivers and the nature of the caregiving they provide, and the attributes of the residential settings in which the care recipients live.

Tools for Assessing Home Health Care Tasks and Operators

Persons caring for themselves or others at home as well as formal caregivers vary considerably in their skills, abilities, attitudes, experience, and other characteristics, such as age, culture/ethnicity, and health literacy. In turn, designers of health-related devices and technology systems used in the home are often naïve about the diversity of the user population. They need high-quality information and guidance to better understand user capabilities relative to the task demands of the health-related device or technology that they are developing.

In this environment, valid and reliable tools are needed to match users with tasks and technologies. At this time, health care providers lack the

tools needed to assess whether particular individuals would be able to perform specific health care tasks at home, and medical device and system designers lack information on the demands associated with health-related tasks performed at home and the human capabilities needed to perform them successfully.

Whether used to assess the characteristics of formal or informal caregivers or persons engaged in self-care, task analysis can be used to develop point-of-care tools for use by consumers and caregivers alike in locations where such tasks are encouraged or prescribed. The tools could facilitate identification of potential mismatches between the characteristics, abilities, experiences, and attitudes that an individual brings to a task and the demands associated with the task. Used in ambulatory care settings, at hospital discharge or other transitions of care, and in the home by caregivers or individuals and family members themselves, these tools could enable assessment of prospective task performer’s capabilities in relation to the demands of the task. The tools might range in complexity from brief screening checklists for clinicians to comprehensive assessment batteries that permit nuanced study and tracking of home-based health care tasks by administrators and researchers. The results are likely to help identify types of needed interventions and support aids that would enhance the abilities of individuals to perform health care tasks in home settings safely, effectively, and efficiently.

Recommendation 11. The Agency for Healthcare Research and Quality should collaborate, as necessary, with the National Institute for Disability and Rehabilitation Research, the National Institutes of Health, the U.S. Department of Veterans Affairs, the National Science Foundation, the U.S. Department of Defense, and the Centers for Medicare & Medicaid Services to support development of assessment tools customized for home-based health care, designed to analyze the demands of tasks associated with home-based health care, the operator capabilities required to carry them out, and the relevant capabilities of specific individuals.

Association for the Advancement of Medical Instrumentation. (2009). ANSI/AAMI HE75:2009: Human factors engineering: Design of medical devices. Available: http://www.aami.org/publications/standards/HE75_Ch16_Access_Board.pdf [April 2011].

Self-Determination Housing Project of Pennsylvania, Inc. (n.d.) Promoting visitability in Pennsylvania. Available: http://www.sdhp.org/promoting_visitability_in_pennsy.htm [March 30, 2011].

In the United States, health care devices, technologies, and practices are rapidly moving into the home. The factors driving this migration include the costs of health care, the growing numbers of older adults, the increasing prevalence of chronic conditions and diseases and improved survival rates for people with those conditions and diseases, and a wide range of technological innovations. The health care that results varies considerably in its safety, effectiveness, and efficiency, as well as in its quality and cost.

Health Care Comes Home reviews the state of current knowledge and practice about many aspects of health care in residential settings and explores the short- and long-term effects of emerging trends and technologies. By evaluating existing systems, the book identifies design problems and imbalances between technological system demands and the capabilities of users. Health Care Comes Home recommends critical steps to improve health care in the home. The book's recommendations cover the regulation of health care technologies, proper training and preparation for people who provide in-home care, and how existing housing can be modified and new accessible housing can be better designed for residential health care. The book also identifies knowledge gaps in the field and how these can be addressed through research and development initiatives.

Health Care Comes Home lays the foundation for the integration of human health factors with the design and implementation of home health care devices, technologies, and practices. The book describes ways in which the Agency for Healthcare Research and Quality (AHRQ), the U.S. Food and Drug Administration (FDA), and federal housing agencies can collaborate to improve the quality of health care at home. It is also a valuable resource for residential health care providers and caregivers.

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These smart technologies are transforming healthcare

VR technology enables doctors to work remotely. Image:  Rawpixel.com/roungroat.

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In turn, people have become more accepting of remote healthcare services and telemedicine, with Ernest Young reporting that 54% of patients with chronic diseases now accept remote healthcare.

That’s a welcome trend: research shows that 30% of hospital visits from patients with common chronic conditions are in fact unnecessary, tie-up resources, and cost the industry upwards of $8.3 billion per year. For patients, the online approach means better and safer access, less wasted time, and lower costs.

The ability to see a doctor regardless of location has helped democratize healthcare access for many people in underserved areas.

Solutions for emergency response

Much like connectivity sits at the core of remote healthcare, it can drive up the efficacy of emergency response during the “golden hour”, the time when effective medical intervention can mean the difference between life and death.

Historically, it’s been impossible to share data between ambulances, A&E departments, and experts in a way that enables a real-time response.

A 5G-powered remote emergency channel that links to a command centre gives doctors equipped with VR glasses the same view as if they were actually inside the ambulance. Doctors receive data on a patient’s vital signs in real-time on a large screen in the command centre, including the patient's ECG, ultrasound image, blood pressure, heart rate, oxygen saturation, and temperature.

The patient's medical history can be quickly established, doctors can guide paramedics in the ambulance, and patients can be admitted to hospital immediately after arrival with their details and condition known. This isn’t something for the future – many hospitals in China are already using this solution.

The application of “precision medicine” to save and improve lives relies on good-quality, easily-accessible data on everything from our DNA to lifestyle and environmental factors. The opposite to a one-size-fits-all healthcare system, it has vast, untapped potential to transform the treatment and prediction of rare diseases—and disease in general.

But there is no global governance framework for such data and no common data portal. This is a problem that contributes to the premature deaths of hundreds of millions of rare-disease patients worldwide.

The World Economic Forum’s Breaking Barriers to Health Data Governance initiative is focused on creating, testing and growing a framework to support effective and responsible access – across borders – to sensitive health data for the treatment and diagnosis of rare diseases.

The data will be shared via a “federated data system”: a decentralized approach that allows different institutions to access each other’s data without that data ever leaving the organization it originated from. This is done via an application programming interface and strikes a balance between simply pooling data (posing security concerns) and limiting access completely.

The project is a collaboration between entities in the UK (Genomics England), Australia (Australian Genomics Health Alliance), Canada (Genomics4RD), and the US (Intermountain Healthcare).

Speed and precision with AI

Alongside remote technologies and 5G, AI is emerging as a key technology in the tech-powered healthcare armory. It’s been instrumental, for example, along with the rapid rollout of COVID-19 vaccines , the large-scale virtual screening for potential drugs and shortening the simulation time from one month to less than one day.

Equally, AI can offset a shortage of specialists, such as ultrasound experts who can interpret echocardiograms to diagnose heart disease. A single expert can diagnose just 40 cases per day, which for patients translates into a waiting time of nearly one week. By training algorithms in small-sample data for 10 heart conditions, we’ve developed the B Ultrasound solution that can speed up the diagnosis process by between five to 10 times.

Proactive healthcare with wearables

In addition to B Ultrasound, since 2018 we’ve been working with more than 80 hospitals in China on the world's largest heart-health research project. With the consent of the research subjects, we’ve collected anonymized data from nearly 3.1 million people. Our smart wearable devices can collect signals from users in real-time, identify abnormal heart rhythms with AI, and upload the results to Huawei Research. Cloud AI then pushes information about high-risk people to the remote medical management platform of the hospitals we’re working with, so that healthcare workers can take appropriate measures.

For physicians, wearables and apps overcome the challenges of data acquisition and analysis, with AI able to analyze massive data through continuous monitoring. Over the past two and a half years, more than 10,000 people were screened for suspected atrial fibrillation (AF) – an abnormally fast heartbeat. More than 4,400 people were diagnosed with AF, with an accuracy of 94%.

Currently, Huawei's smart wearable devices can also monitor blood oxygen and generate electrocardiograms. We expect to release a medical-grade blood pressure watch by the end of the year.

Smart hospital management

To maximize the effectiveness of the technologies to empower doctors, and improve the patient experience, hospital management must also be smart.

Open, connected digital platforms can achieve the real-time visual management of operations and resources in hospitals, from patient flow and doctor workloads to bed occupancy and medical device use. This in turn can enable hospital management to plan and improve resource use and make decisions based on full datasets, underpinning overall healthcare performance and outcomes.

Over the past 18 months, I’ve witnessed the dedication and bravery of healthcare workers, as I think much of the world’s population has. However, with a chronic shortage of medical staff unfolding and with half the world’s population still lacking access to basic healthcare services , we need to develop solutions if we are to move forward as an equitable society where everyone has room to fulfill their potential.

In healthcare, technology companies can make a real difference – and I believe that’s something worth committing to.

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The rise of health technology

July 11, 2021 When it comes to the digital transformation of healthcare, one area deserves particular attention: health technology. It continues to push the boundaries of how healthcare is delivered and could drive breakthroughs in how we understand disease. Explore a special collection  to understand its evolving role in the life-sciences sector, or go deeper with articles from it to discover:

• How digital tools can help improve well-being, address sleep loss, and support mental-health needs
• What medtech and healthcare players more broadly can do to capture more value from digital health
• How it could help companies build new business or reinvent themselves
• What executives think could lie ahead for digital therapeutics and healthcare innovation

Leap to the future of healthcare: Reinvent through business building

How the medtech industry can capture value from digital health

Moving digital health forward: Lessons on business building

Telehealth: A quarter-trillion-dollar post-COVID-19 reality?

Using digital tech to support employees’ mental health and resilience

Sleep on it: Addressing the sleep-loss epidemic through technology

Digital therapeutics and pharma: A blueprint for success from Sanofi’s Bozidar Jovicevic

‘Test and listen’: Microsoft’s Jim Weinstein on healthcare tech

Essay on the Impact of Technology on Health Care

Technology has grown to become an integral part of health. Healthcare organizations in different parts of the world are using technology to monitor their patients’ progress while others are using technology to store patients’ data (Bonato 37). Patient outcomes have improved due to technology, and health organizations that sought profits have significantly increased their income because of technology. It is no doubt that technology has influenced medical services in varied ways. Therefore, it would be fair to conclude that technology has positively affected healthcare.

First, technology has improved access to medical information and data (Mettler 33). One of the most significant advantages triggered by technology is the ability to store and access patient data. Medical professionals can now track patients’ progress by retrieving data from anywhere. At the same time, the internet has allowed doctors to share medical information rapidly amongst themselves, an instance that leads to more efficient patient care.

Second, technology has allowed clinicians to gather big data in a limited time (Chen et al. 72). Digital technology allows instant data collection for professionals engaged in epidemiological studies, clinical trials, and those in research. The collection of data, in this case, allows for meta-analysis and permits healthcare organizations to stay on top of cutting edge technological trends.

In addition to allowing quick access to medical data and big data technology has improved medical communication (Free et al. 54). Communication is a critical part of healthcare; nurses and doctors must communicate in real-time, and technology allows this instance to happen. Also, healthcare professionals can today make their videos, webinars and use online platforms to communicate with other professionals in different parts of the globe.

Technology has revolutionized how health care services are rendered. But apart from improving healthcare, critics argue that technology has increased or added extra jobs for medical professionals (de Belvis et al. 11). Physicians need to have excellent clinical skills and knowledge of the human body. Today, they are forced to have knowledge of both the human body and technology, which makes it challenging for others. Technology has also improved access to data, and this has allowed physicians to study and understand patients’ medical history. Nevertheless, these instances have opened doors to unethical activities such as computer hacking (de Belvis et al. 13). Today patients risk losing their medical information, including their social security numbers, address and other critical information.

Despite the improvements that have come with adopting technology, there is always the possibility that digital technological gadgets might fail. If makers of a given technology do not have a sustainable business process or a good track record, their technologies might fail. Many people, including patients and doctors who solely rely on technology, might be affected when it does. Apart from equipment failure, technology has created the space for laziness within hospitals.

Doctors and patients heavily rely on medical technology for problem-solving. In like manner, medical technologies that use machine learning have removed decision-making in different hospitals; today, medical tools are solving people’s problems. Technology has been great for our hospitals, but the speed at which different hospitals are adapting to technological processes is alarming. Technology often fails, and when it does, health care may be significantly affected. Doctors and patients who use technology may be forced to go back to traditional methods of health care services.

Bonato, P. “Advances in Wearable Technology and Its Medical Applications.”  2010 Annual International Conference of The IEEE Engineering in Medicine and Biology , 2010, pp. 33-45.

Chen, Min et al. “Disease Prediction by Machine Learning Over Big Data from Healthcare Communities.”  IEEE Access , vol. 5, 2017, pp. 69-79.

De Belvis, Antonio Giulio et al. “The Financial Crisis in Italy: Implications for The Healthcare Sector.”  Health Policy , vol. 106, no. 1, 2012, pp. 10-16.

Free, Caroline et al. “The Effectiveness of M-Health Technologies for Improving Health and Health Services: A Systematic Review Protocol.”  BMC Research Notes , vol. 3, no. 1, 2010, pp. 42-78.

Mettler, Matthias. “Blockchain Technology in Healthcare: The Revolution Starts Here.”  2016 IEEE 18Th International Conference On E-Health Networking, Applications and Services (Healthcom) , 2016, pp. 23-78.

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The Information Technology in Medicine Essay

Modern healthcare, being primarily focused on providing quality patient care, cannot exist apart from information technology. For this reason, medical employees are now obliged to learn how to beneficially use technology as well as practice proper communication with patients on the subject of its use in the process of treatment. The discipline, which will be analyzed in the course of the paper, is aimed at defining major tools necessary for providing quality healthcare to the community. Thus, the most significant insight acquired during the course is the high necessity of learning how to convey the importance of information technology to the patients in the simplest way possible.

To begin with, it is necessary to dwell upon the notions that should be processed by the students throughout the course. The subject’s primary goal was to introduce the most common technologies now used in healthcare and their significance to the sphere. Another part of the course was dedicated to the nuance of proper communication with patients using both technology and interpersonal communication.

Before the course enrollment, I considered information technology to be more of an arbitrary helping tool rather than part and parcel of medical practice. However, by the end of the course, I have discovered that the proper use of technology tools can potentially help save hundreds of human lives. Researchers claim that such tools as the Clinical Decision Support System (CDSS) prevent doctors from making false diagnoses due to cognitive overload (Ancker et al., 2017). Besides helping medicals focus more on the treatment process, it is also a key tool for the precise statistical data, crucial for the further development of healthcare across the state.

Another crucial aspect obtained during the course is the ability to assess personal strengths and weaknesses when it comes to cooperation with information systems. As a result, I have discovered that my major strength is the ability to adapt to technology use quite quickly. This benefit also concerns almost all young specialists who are used to information technology from an early age. Such knowledge puts more adolescent specialists at a serious advantage in terms of healthcare system development. However, the skills of fast learning do not concern some patients who are to be educated on the use of technology tools during their treatment process.

According to the researchers, effective patient education is the key to successful treatment due to the patient’s willingness to collaborate (Jimenez & Lewis, 2018). Hence, I believe my major weakness to be the ability to communicate with patients in a way beneficial for their desire to be educated. Despite various already existing methods on patient education, there is still a strong need to develop further research on the topic in order to make it more productive.

In my opinion, the development of healthcare informatics is now rapidly moving towards its zenith due to the beneficial environment. The strategies of further researches are now being planned for the next decades. However, automatized systems of clinical history have introduced the issue of privacy for both patients and medical employees (Iyengar et al., 2018). For this reason, I believe sustaining privacy while maintaining technological advancements in the medical sphere to be one of the most significant topics for further investigation. Moreover, the field of healthcare informatics develops too fast for educators to adapt to the process.

As a result, many specialized educational establishments fail to provide proper students’ preparation on the subject (Ashrafi et al., 2019). Hence, another subject of further investigation should be the methods in which students could be informed of the information technologies used in the field.

Speaking of my personal evaluation of the competencies aligned to the course, the progress is definitely visible by the end of the course, but there are still a lot of details requiring reconsideration. My understanding of the outlines introduced in the course syllabus, including the ability to analyze major programs and methods of computer-human interaction critically is clear and exhaustive. However, in order to maintain the obtained knowledge and skills, there should be more practical tasks, which may help build on the progress. The most valuable output I realized throughout the course is the fact that subjects concerning medicine and technology require constant and comprehensive learning in order to remain relevant in the field.

Taking everything into consideration, it may be concluded that the course of healthcare informatics is an integral part of medical education in the context of the 21st century. In the following reflection of the course, some of its major constituents and outputs were introduced and analyzed. When it comes to personal knowledge and skills gained, the most significant discoveries are the necessity to continually improve on the subject in order to realize how to convey the information to the patients. Even the most advanced technology may be of no help if the patients are not willing to collaborate. Further research on the subject includes more methods for patient education and examination of privacy maintenance.

Ancker, J. S., Edwards, A., Nosal, S., Hauser, D., Mauer, E., & Kaushal, R. (2017). Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system. BMC medical informatics and decision making , 17 (1), 36.

Ashrafi, N., Kuilboer, J. P., Joshi, C., Ran, I., & Pande, P. (2019). Health informatics in the classroom: An empirical study to investigate higher education’s response to healthcare transformation. Journal of Information Systems Education , 25 (4), 5.

Iyengar, A., Kundu, A., & Pallis, G. (2018). Healthcare informatics and privacy. IEEE Internet Computing , 22 (2), 29-31.

Jimenez, Y. A., & Lewis, S. J. (2018). Radiation therapy patient education using VERT: a combination of technology with human care. Journal of medical radiation sciences , 65 (2), 158-162.

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Home — Essay Samples — Life — Improve — The Importance of Using Technology to Improve Healthcare

The Importance of Using Technology to Improve Healthcare

• Categories: Decision Making Improve Patient

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Published: May 24, 2022

Words: 1272 | Pages: 3 | 7 min read

Table of contents

Introduction, communication, reduces medical inaccuracies, improve patient safety, support decision making, works cited.

• Asghar, N. Z., Sheikh, Z. H., Ilyas, A., Khan, I. A., & Imran, A. (2014). Role of health information technology in reducing medical errors. Journal of Medical Systems, 38(12), 1-5.
• Dhillon, I. S., & Schuurmans, D. (Eds.). (2019). Machine Learning: Foundations. Adaptive Computation and Machine Learning series. MIT Press.
• Kaminski, J., & Nesterova, Y. (2020). The role of technology in improving healthcare system performance. Central European Journal of Operations Research, 28(1), 273-291.
• Kemp, P., & Williams, C. (Eds.). (2020). Health Systems in Low- and Middle-Income Countries: An Economic and Policy Perspective. Oxford University Press.
• Kumar, A. (2020). Healthcare Information Systems: Challenges of the New Age. Springer.
• Lungu, I., Lupu, V. V., & Vladescu, C. (2021). The impact of technology on healthcare: Evidence from a panel of Eastern European countries. Technological Forecasting and Social Change , 170, 120849.
• Schwab, K. (2017). The Fourth Industrial Revolution. Crown Business.
• Seidel, R. (Ed.). (2017). Handbook of Research on Entrepreneurship and Aging. Edward Elgar Publishing.
• Tavares, J., & Oliveira, T. (2019). Electronic health record patient portals: A systematic review of impact on care outcomes and access. BMC Medical Informatics and Decision Making, 19(1), 1-14.
• World Health Organization. (2021). Digital health. Retrieved from https://www.who.int/health-topics/digital-health#tab=tab_1

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Research ethics and artificial intelligence for global health: perspectives from the global forum on bioethics in research

• James Shaw 1 , 13 ,
• Joseph Ali 2 , 3 ,
• Caesar A. Atuire 4 , 5 ,
• Phaik Yeong Cheah 6 ,
• Armando Guio Español 7 ,
• Judy Wawira Gichoya 8 ,
• Adrienne Hunt 9 ,
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BMC Medical Ethics volume  25 , Article number:  46 ( 2024 ) Cite this article

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The ethical governance of Artificial Intelligence (AI) in health care and public health continues to be an urgent issue for attention in policy, research, and practice. In this paper we report on central themes related to challenges and strategies for promoting ethics in research involving AI in global health, arising from the Global Forum on Bioethics in Research (GFBR), held in Cape Town, South Africa in November 2022.

The GFBR is an annual meeting organized by the World Health Organization and supported by the Wellcome Trust, the US National Institutes of Health, the UK Medical Research Council (MRC) and the South African MRC. The forum aims to bring together ethicists, researchers, policymakers, research ethics committee members and other actors to engage with challenges and opportunities specifically related to research ethics. In 2022 the focus of the GFBR was “Ethics of AI in Global Health Research”. The forum consisted of 6 case study presentations, 16 governance presentations, and a series of small group and large group discussions. A total of 87 participants attended the forum from 31 countries around the world, representing disciplines of bioethics, AI, health policy, health professional practice, research funding, and bioinformatics. In this paper, we highlight central insights arising from GFBR 2022.

We describe the significance of four thematic insights arising from the forum: (1) Appropriateness of building AI, (2) Transferability of AI systems, (3) Accountability for AI decision-making and outcomes, and (4) Individual consent. We then describe eight recommendations for governance leaders to enhance the ethical governance of AI in global health research, addressing issues such as AI impact assessments, environmental values, and fair partnerships.

Conclusions

The 2022 Global Forum on Bioethics in Research illustrated several innovations in ethical governance of AI for global health research, as well as several areas in need of urgent attention internationally. This summary is intended to inform international and domestic efforts to strengthen research ethics and support the evolution of governance leadership to meet the demands of AI in global health research.

Peer Review reports

Introduction

The ethical governance of Artificial Intelligence (AI) in health care and public health continues to be an urgent issue for attention in policy, research, and practice [ 1 , 2 , 3 ]. Beyond the growing number of AI applications being implemented in health care, capabilities of AI models such as Large Language Models (LLMs) expand the potential reach and significance of AI technologies across health-related fields [ 4 , 5 ]. Discussion about effective, ethical governance of AI technologies has spanned a range of governance approaches, including government regulation, organizational decision-making, professional self-regulation, and research ethics review [ 6 , 7 , 8 ]. In this paper, we report on central themes related to challenges and strategies for promoting ethics in research involving AI in global health research, arising from the Global Forum on Bioethics in Research (GFBR), held in Cape Town, South Africa in November 2022. Although applications of AI for research, health care, and public health are diverse and advancing rapidly, the insights generated at the forum remain highly relevant from a global health perspective. After summarizing important context for work in this domain, we highlight categories of ethical issues emphasized at the forum for attention from a research ethics perspective internationally. We then outline strategies proposed for research, innovation, and governance to support more ethical AI for global health.

In this paper, we adopt the definition of AI systems provided by the Organization for Economic Cooperation and Development (OECD) as our starting point. Their definition states that an AI system is “a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments. AI systems are designed to operate with varying levels of autonomy” [ 9 ]. The conceptualization of an algorithm as helping to constitute an AI system, along with hardware, other elements of software, and a particular context of use, illustrates the wide variety of ways in which AI can be applied. We have found it useful to differentiate applications of AI in research as those classified as “AI systems for discovery” and “AI systems for intervention”. An AI system for discovery is one that is intended to generate new knowledge, for example in drug discovery or public health research in which researchers are seeking potential targets for intervention, innovation, or further research. An AI system for intervention is one that directly contributes to enacting an intervention in a particular context, for example informing decision-making at the point of care or assisting with accuracy in a surgical procedure.

The mandate of the GFBR is to take a broad view of what constitutes research and its regulation in global health, with special attention to bioethics in Low- and Middle- Income Countries. AI as a group of technologies demands such a broad view. AI development for health occurs in a variety of environments, including universities and academic health sciences centers where research ethics review remains an important element of the governance of science and innovation internationally [ 10 , 11 ]. In these settings, research ethics committees (RECs; also known by different names such as Institutional Review Boards or IRBs) make decisions about the ethical appropriateness of projects proposed by researchers and other institutional members, ultimately determining whether a given project is allowed to proceed on ethical grounds [ 12 ].

However, research involving AI for health also takes place in large corporations and smaller scale start-ups, which in some jurisdictions fall outside the scope of research ethics regulation. In the domain of AI, the question of what constitutes research also becomes blurred. For example, is the development of an algorithm itself considered a part of the research process? Or only when that algorithm is tested under the formal constraints of a systematic research methodology? In this paper we take an inclusive view, in which AI development is included in the definition of research activity and within scope for our inquiry, regardless of the setting in which it takes place. This broad perspective characterizes the approach to “research ethics” we take in this paper, extending beyond the work of RECs to include the ethical analysis of the wide range of activities that constitute research as the generation of new knowledge and intervention in the world.

Ethical governance of AI in global health

The ethical governance of AI for global health has been widely discussed in recent years. The World Health Organization (WHO) released its guidelines on ethics and governance of AI for health in 2021, endorsing a set of six ethical principles and exploring the relevance of those principles through a variety of use cases. The WHO guidelines also provided an overview of AI governance, defining governance as covering “a range of steering and rule-making functions of governments and other decision-makers, including international health agencies, for the achievement of national health policy objectives conducive to universal health coverage.” (p. 81) The report usefully provided a series of recommendations related to governance of seven domains pertaining to AI for health: data, benefit sharing, the private sector, the public sector, regulation, policy observatories/model legislation, and global governance. The report acknowledges that much work is yet to be done to advance international cooperation on AI governance, especially related to prioritizing voices from Low- and Middle-Income Countries (LMICs) in global dialogue.

One important point emphasized in the WHO report that reinforces the broader literature on global governance of AI is the distribution of responsibility across a wide range of actors in the AI ecosystem. This is especially important to highlight when focused on research for global health, which is specifically about work that transcends national borders. Alami et al. (2020) discussed the unique risks raised by AI research in global health, ranging from the unavailability of data in many LMICs required to train locally relevant AI models to the capacity of health systems to absorb new AI technologies that demand the use of resources from elsewhere in the system. These observations illustrate the need to identify the unique issues posed by AI research for global health specifically, and the strategies that can be employed by all those implicated in AI governance to promote ethically responsible use of AI in global health research.

RECs and the regulation of research involving AI

RECs represent an important element of the governance of AI for global health research, and thus warrant further commentary as background to our paper. Despite the importance of RECs, foundational questions have been raised about their capabilities to accurately understand and address ethical issues raised by studies involving AI. Rahimzadeh et al. (2023) outlined how RECs in the United States are under-prepared to align with recent federal policy requiring that RECs review data sharing and management plans with attention to the unique ethical issues raised in AI research for health [ 13 ]. Similar research in South Africa identified variability in understanding of existing regulations and ethical issues associated with health-related big data sharing and management among research ethics committee members [ 14 , 15 ]. The effort to address harms accruing to groups or communities as opposed to individuals whose data are included in AI research has also been identified as a unique challenge for RECs [ 16 , 17 ]. Doerr and Meeder (2022) suggested that current regulatory frameworks for research ethics might actually prevent RECs from adequately addressing such issues, as they are deemed out of scope of REC review [ 16 ]. Furthermore, research in the United Kingdom and Canada has suggested that researchers using AI methods for health tend to distinguish between ethical issues and social impact of their research, adopting an overly narrow view of what constitutes ethical issues in their work [ 18 ].

The challenges for RECs in adequately addressing ethical issues in AI research for health care and public health exceed a straightforward survey of ethical considerations. As Ferretti et al. (2021) contend, some capabilities of RECs adequately cover certain issues in AI-based health research, such as the common occurrence of conflicts of interest where researchers who accept funds from commercial technology providers are implicitly incentivized to produce results that align with commercial interests [ 12 ]. However, some features of REC review require reform to adequately meet ethical needs. Ferretti et al. outlined weaknesses of RECs that are longstanding and those that are novel to AI-related projects, proposing a series of directions for development that are regulatory, procedural, and complementary to REC functionality. The work required on a global scale to update the REC function in response to the demands of research involving AI is substantial.

These issues take greater urgency in the context of global health [ 19 ]. Teixeira da Silva (2022) described the global practice of “ethics dumping”, where researchers from high income countries bring ethically contentious practices to RECs in low-income countries as a strategy to gain approval and move projects forward [ 20 ]. Although not yet systematically documented in AI research for health, risk of ethics dumping in AI research is high. Evidence is already emerging of practices of “health data colonialism”, in which AI researchers and developers from large organizations in high-income countries acquire data to build algorithms in LMICs to avoid stricter regulations [ 21 ]. This specific practice is part of a larger collection of practices that characterize health data colonialism, involving the broader exploitation of data and the populations they represent primarily for commercial gain [ 21 , 22 ]. As an additional complication, AI algorithms trained on data from high-income contexts are unlikely to apply in straightforward ways to LMIC settings [ 21 , 23 ]. In the context of global health, there is widespread acknowledgement about the need to not only enhance the knowledge base of REC members about AI-based methods internationally, but to acknowledge the broader shifts required to encourage their capabilities to more fully address these and other ethical issues associated with AI research for health [ 8 ].

Although RECs are an important part of the story of the ethical governance of AI for global health research, they are not the only part. The responsibilities of supra-national entities such as the World Health Organization, national governments, organizational leaders, commercial AI technology providers, health care professionals, and other groups continue to be worked out internationally. In this context of ongoing work, examining issues that demand attention and strategies to address them remains an urgent and valuable task.

The GFBR is an annual meeting organized by the World Health Organization and supported by the Wellcome Trust, the US National Institutes of Health, the UK Medical Research Council (MRC) and the South African MRC. The forum aims to bring together ethicists, researchers, policymakers, REC members and other actors to engage with challenges and opportunities specifically related to research ethics. Each year the GFBR meeting includes a series of case studies and keynotes presented in plenary format to an audience of approximately 100 people who have applied and been competitively selected to attend, along with small-group breakout discussions to advance thinking on related issues. The specific topic of the forum changes each year, with past topics including ethical issues in research with people living with mental health conditions (2021), genome editing (2019), and biobanking/data sharing (2018). The forum is intended to remain grounded in the practical challenges of engaging in research ethics, with special interest in low resource settings from a global health perspective. A post-meeting fellowship scheme is open to all LMIC participants, providing a unique opportunity to apply for funding to further explore and address the ethical challenges that are identified during the meeting.

In 2022, the focus of the GFBR was “Ethics of AI in Global Health Research”. The forum consisted of 6 case study presentations (both short and long form) reporting on specific initiatives related to research ethics and AI for health, and 16 governance presentations (both short and long form) reporting on actual approaches to governing AI in different country settings. A keynote presentation from Professor Effy Vayena addressed the topic of the broader context for AI ethics in a rapidly evolving field. A total of 87 participants attended the forum from 31 countries around the world, representing disciplines of bioethics, AI, health policy, health professional practice, research funding, and bioinformatics. The 2-day forum addressed a wide range of themes. The conference report provides a detailed overview of each of the specific topics addressed while a policy paper outlines the cross-cutting themes (both documents are available at the GFBR website: https://www.gfbr.global/past-meetings/16th-forum-cape-town-south-africa-29-30-november-2022/ ). As opposed to providing a detailed summary in this paper, we aim to briefly highlight central issues raised, solutions proposed, and the challenges facing the research ethics community in the years to come.

In this way, our primary aim in this paper is to present a synthesis of the challenges and opportunities raised at the GFBR meeting and in the planning process, followed by our reflections as a group of authors on their significance for governance leaders in the coming years. We acknowledge that the views represented at the meeting and in our results are a partial representation of the universe of views on this topic; however, the GFBR leadership invested a great deal of resources in convening a deeply diverse and thoughtful group of researchers and practitioners working on themes of bioethics related to AI for global health including those based in LMICs. We contend that it remains rare to convene such a strong group for an extended time and believe that many of the challenges and opportunities raised demand attention for more ethical futures of AI for health. Nonetheless, our results are primarily descriptive and are thus not explicitly grounded in a normative argument. We make effort in the Discussion section to contextualize our results by describing their significance and connecting them to broader efforts to reform global health research and practice.

Uniquely important ethical issues for AI in global health research

Presentations and group dialogue over the course of the forum raised several issues for consideration, and here we describe four overarching themes for the ethical governance of AI in global health research. Brief descriptions of each issue can be found in Table  1 . Reports referred to throughout the paper are available at the GFBR website provided above.

The first overarching thematic issue relates to the appropriateness of building AI technologies in response to health-related challenges in the first place. Case study presentations referred to initiatives where AI technologies were highly appropriate, such as in ear shape biometric identification to more accurately link electronic health care records to individual patients in Zambia (Alinani Simukanga). Although important ethical issues were raised with respect to privacy, trust, and community engagement in this initiative, the AI-based solution was appropriately matched to the challenge of accurately linking electronic records to specific patient identities. In contrast, forum participants raised questions about the appropriateness of an initiative using AI to improve the quality of handwashing practices in an acute care hospital in India (Niyoshi Shah), which led to gaming the algorithm. Overall, participants acknowledged the dangers of techno-solutionism, in which AI researchers and developers treat AI technologies as the most obvious solutions to problems that in actuality demand much more complex strategies to address [ 24 ]. However, forum participants agreed that RECs in different contexts have differing degrees of power to raise issues of the appropriateness of an AI-based intervention.

The second overarching thematic issue related to whether and how AI-based systems transfer from one national health context to another. One central issue raised by a number of case study presentations related to the challenges of validating an algorithm with data collected in a local environment. For example, one case study presentation described a project that would involve the collection of personally identifiable data for sensitive group identities, such as tribe, clan, or religion, in the jurisdictions involved (South Africa, Nigeria, Tanzania, Uganda and the US; Gakii Masunga). Doing so would enable the team to ensure that those groups were adequately represented in the dataset to ensure the resulting algorithm was not biased against specific community groups when deployed in that context. However, some members of these communities might desire to be represented in the dataset, whereas others might not, illustrating the need to balance autonomy and inclusivity. It was also widely recognized that collecting these data is an immense challenge, particularly when historically oppressive practices have led to a low-trust environment for international organizations and the technologies they produce. It is important to note that in some countries such as South Africa and Rwanda, it is illegal to collect information such as race and tribal identities, re-emphasizing the importance for cultural awareness and avoiding “one size fits all” solutions.

The third overarching thematic issue is related to understanding accountabilities for both the impacts of AI technologies and governance decision-making regarding their use. Where global health research involving AI leads to longer-term harms that might fall outside the usual scope of issues considered by a REC, who is to be held accountable, and how? This question was raised as one that requires much further attention, with law being mixed internationally regarding the mechanisms available to hold researchers, innovators, and their institutions accountable over the longer term. However, it was recognized in breakout group discussion that many jurisdictions are developing strong data protection regimes related specifically to international collaboration for research involving health data. For example, Kenya’s Data Protection Act requires that any internationally funded projects have a local principal investigator who will hold accountability for how data are shared and used [ 25 ]. The issue of research partnerships with commercial entities was raised by many participants in the context of accountability, pointing toward the urgent need for clear principles related to strategies for engagement with commercial technology companies in global health research.

The fourth and final overarching thematic issue raised here is that of consent. The issue of consent was framed by the widely shared recognition that models of individual, explicit consent might not produce a supportive environment for AI innovation that relies on the secondary uses of health-related datasets to build AI algorithms. Given this recognition, approaches such as community oversight of health data uses were suggested as a potential solution. However, the details of implementing such community oversight mechanisms require much further attention, particularly given the unique perspectives on health data in different country settings in global health research. Furthermore, some uses of health data do continue to require consent. One case study of South Africa, Nigeria, Kenya, Ethiopia and Uganda suggested that when health data are shared across borders, individual consent remains necessary when data is transferred from certain countries (Nezerith Cengiz). Broader clarity is necessary to support the ethical governance of health data uses for AI in global health research.

Recommendations for ethical governance of AI in global health research

Dialogue at the forum led to a range of suggestions for promoting ethical conduct of AI research for global health, related to the various roles of actors involved in the governance of AI research broadly defined. The strategies are written for actors we refer to as “governance leaders”, those people distributed throughout the AI for global health research ecosystem who are responsible for ensuring the ethical and socially responsible conduct of global health research involving AI (including researchers themselves). These include RECs, government regulators, health care leaders, health professionals, corporate social accountability officers, and others. Enacting these strategies would bolster the ethical governance of AI for global health more generally, enabling multiple actors to fulfill their roles related to governing research and development activities carried out across multiple organizations, including universities, academic health sciences centers, start-ups, and technology corporations. Specific suggestions are summarized in Table  2 .

First, forum participants suggested that governance leaders including RECs, should remain up to date on recent advances in the regulation of AI for health. Regulation of AI for health advances rapidly and takes on different forms in jurisdictions around the world. RECs play an important role in governance, but only a partial role; it was deemed important for RECs to acknowledge how they fit within a broader governance ecosystem in order to more effectively address the issues within their scope. Not only RECs but organizational leaders responsible for procurement, researchers, and commercial actors should all commit to efforts to remain up to date about the relevant approaches to regulating AI for health care and public health in jurisdictions internationally. In this way, governance can more adequately remain up to date with advances in regulation.

Second, forum participants suggested that governance leaders should focus on ethical governance of health data as a basis for ethical global health AI research. Health data are considered the foundation of AI development, being used to train AI algorithms for various uses [ 26 ]. By focusing on ethical governance of health data generation, sharing, and use, multiple actors will help to build an ethical foundation for AI development among global health researchers.

Third, forum participants believed that governance processes should incorporate AI impact assessments where appropriate. An AI impact assessment is the process of evaluating the potential effects, both positive and negative, of implementing an AI algorithm on individuals, society, and various stakeholders, generally over time frames specified in advance of implementation [ 27 ]. Although not all types of AI research in global health would warrant an AI impact assessment, this is especially relevant for those studies aiming to implement an AI system for intervention into health care or public health. Organizations such as RECs can use AI impact assessments to boost understanding of potential harms at the outset of a research project, encouraging researchers to more deeply consider potential harms in the development of their study.

Fourth, forum participants suggested that governance decisions should incorporate the use of environmental impact assessments, or at least the incorporation of environment values when assessing the potential impact of an AI system. An environmental impact assessment involves evaluating and anticipating the potential environmental effects of a proposed project to inform ethical decision-making that supports sustainability [ 28 ]. Although a relatively new consideration in research ethics conversations [ 29 ], the environmental impact of building technologies is a crucial consideration for the public health commitment to environmental sustainability. Governance leaders can use environmental impact assessments to boost understanding of potential environmental harms linked to AI research projects in global health over both the shorter and longer terms.

Fifth, forum participants suggested that governance leaders should require stronger transparency in the development of AI algorithms in global health research. Transparency was considered essential in the design and development of AI algorithms for global health to ensure ethical and accountable decision-making throughout the process. Furthermore, whether and how researchers have considered the unique contexts into which such algorithms may be deployed can be surfaced through stronger transparency, for example in describing what primary considerations were made at the outset of the project and which stakeholders were consulted along the way. Sharing information about data provenance and methods used in AI development will also enhance the trustworthiness of the AI-based research process.

Sixth, forum participants suggested that governance leaders can encourage or require community engagement at various points throughout an AI project. It was considered that engaging patients and communities is crucial in AI algorithm development to ensure that the technology aligns with community needs and values. However, participants acknowledged that this is not a straightforward process. Effective community engagement requires lengthy commitments to meeting with and hearing from diverse communities in a given setting, and demands a particular set of skills in communication and dialogue that are not possessed by all researchers. Encouraging AI researchers to begin this process early and build long-term partnerships with community members is a promising strategy to deepen community engagement in AI research for global health. One notable recommendation was that research funders have an opportunity to incentivize and enable community engagement with funds dedicated to these activities in AI research in global health.

Seventh, forum participants suggested that governance leaders can encourage researchers to build strong, fair partnerships between institutions and individuals across country settings. In a context of longstanding imbalances in geopolitical and economic power, fair partnerships in global health demand a priori commitments to share benefits related to advances in medical technologies, knowledge, and financial gains. Although enforcement of this point might be beyond the remit of RECs, commentary will encourage researchers to consider stronger, fairer partnerships in global health in the longer term.

Eighth, it became evident that it is necessary to explore new forms of regulatory experimentation given the complexity of regulating a technology of this nature. In addition, the health sector has a series of particularities that make it especially complicated to generate rules that have not been previously tested. Several participants highlighted the desire to promote spaces for experimentation such as regulatory sandboxes or innovation hubs in health. These spaces can have several benefits for addressing issues surrounding the regulation of AI in the health sector, such as: (i) increasing the capacities and knowledge of health authorities about this technology; (ii) identifying the major problems surrounding AI regulation in the health sector; (iii) establishing possibilities for exchange and learning with other authorities; (iv) promoting innovation and entrepreneurship in AI in health; and (vi) identifying the need to regulate AI in this sector and update other existing regulations.

Ninth and finally, forum participants believed that the capabilities of governance leaders need to evolve to better incorporate expertise related to AI in ways that make sense within a given jurisdiction. With respect to RECs, for example, it might not make sense for every REC to recruit a member with expertise in AI methods. Rather, it will make more sense in some jurisdictions to consult with members of the scientific community with expertise in AI when research protocols are submitted that demand such expertise. Furthermore, RECs and other approaches to research governance in jurisdictions around the world will need to evolve in order to adopt the suggestions outlined above, developing processes that apply specifically to the ethical governance of research using AI methods in global health.

Research involving the development and implementation of AI technologies continues to grow in global health, posing important challenges for ethical governance of AI in global health research around the world. In this paper we have summarized insights from the 2022 GFBR, focused specifically on issues in research ethics related to AI for global health research. We summarized four thematic challenges for governance related to AI in global health research and nine suggestions arising from presentations and dialogue at the forum. In this brief discussion section, we present an overarching observation about power imbalances that frames efforts to evolve the role of governance in global health research, and then outline two important opportunity areas as the field develops to meet the challenges of AI in global health research.

Dialogue about power is not unfamiliar in global health, especially given recent contributions exploring what it would mean to de-colonize global health research, funding, and practice [ 30 , 31 ]. Discussions of research ethics applied to AI research in global health contexts are deeply infused with power imbalances. The existing context of global health is one in which high-income countries primarily located in the “Global North” charitably invest in projects taking place primarily in the “Global South” while recouping knowledge, financial, and reputational benefits [ 32 ]. With respect to AI development in particular, recent examples of digital colonialism frame dialogue about global partnerships, raising attention to the role of large commercial entities and global financial capitalism in global health research [ 21 , 22 ]. Furthermore, the power of governance organizations such as RECs to intervene in the process of AI research in global health varies widely around the world, depending on the authorities assigned to them by domestic research governance policies. These observations frame the challenges outlined in our paper, highlighting the difficulties associated with making meaningful change in this field.

Despite these overarching challenges of the global health research context, there are clear strategies for progress in this domain. Firstly, AI innovation is rapidly evolving, which means approaches to the governance of AI for health are rapidly evolving too. Such rapid evolution presents an important opportunity for governance leaders to clarify their vision and influence over AI innovation in global health research, boosting the expertise, structure, and functionality required to meet the demands of research involving AI. Secondly, the research ethics community has strong international ties, linked to a global scholarly community that is committed to sharing insights and best practices around the world. This global community can be leveraged to coordinate efforts to produce advances in the capabilities and authorities of governance leaders to meaningfully govern AI research for global health given the challenges summarized in our paper.

Limitations

Our paper includes two specific limitations that we address explicitly here. First, it is still early in the lifetime of the development of applications of AI for use in global health, and as such, the global community has had limited opportunity to learn from experience. For example, there were many fewer case studies, which detail experiences with the actual implementation of an AI technology, submitted to GFBR 2022 for consideration than was expected. In contrast, there were many more governance reports submitted, which detail the processes and outputs of governance processes that anticipate the development and dissemination of AI technologies. This observation represents both a success and a challenge. It is a success that so many groups are engaging in anticipatory governance of AI technologies, exploring evidence of their likely impacts and governing technologies in novel and well-designed ways. It is a challenge that there is little experience to build upon of the successful implementation of AI technologies in ways that have limited harms while promoting innovation. Further experience with AI technologies in global health will contribute to revising and enhancing the challenges and recommendations we have outlined in our paper.

Second, global trends in the politics and economics of AI technologies are evolving rapidly. Although some nations are advancing detailed policy approaches to regulating AI more generally, including for uses in health care and public health, the impacts of corporate investments in AI and political responses related to governance remain to be seen. The excitement around large language models (LLMs) and large multimodal models (LMMs) has drawn deeper attention to the challenges of regulating AI in any general sense, opening dialogue about health sector-specific regulations. The direction of this global dialogue, strongly linked to high-profile corporate actors and multi-national governance institutions, will strongly influence the development of boundaries around what is possible for the ethical governance of AI for global health. We have written this paper at a point when these developments are proceeding rapidly, and as such, we acknowledge that our recommendations will need updating as the broader field evolves.

Ultimately, coordination and collaboration between many stakeholders in the research ethics ecosystem will be necessary to strengthen the ethical governance of AI in global health research. The 2022 GFBR illustrated several innovations in ethical governance of AI for global health research, as well as several areas in need of urgent attention internationally. This summary is intended to inform international and domestic efforts to strengthen research ethics and support the evolution of governance leadership to meet the demands of AI in global health research.

Data availability

All data and materials analyzed to produce this paper are available on the GFBR website: https://www.gfbr.global/past-meetings/16th-forum-cape-town-south-africa-29-30-november-2022/ .

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Acknowledgements

We would like to acknowledge the outstanding contributions of the attendees of GFBR 2022 in Cape Town, South Africa. This paper is authored by members of the GFBR 2022 Planning Committee. We would like to acknowledge additional members Tamra Lysaght, National University of Singapore, and Niresh Bhagwandin, South African Medical Research Council, for their input during the planning stages and as reviewers of the applications to attend the Forum.

This work was supported by Wellcome [222525/Z/21/Z], the US National Institutes of Health, the UK Medical Research Council (part of UK Research and Innovation), and the South African Medical Research Council through funding to the Global Forum on Bioethics in Research.

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Shaw, J., Ali, J., Atuire, C.A. et al. Research ethics and artificial intelligence for global health: perspectives from the global forum on bioethics in research. BMC Med Ethics 25 , 46 (2024). https://doi.org/10.1186/s12910-024-01044-w

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Technology as a Tool for Improving Patient Safety

Introduction .

In the past several decades, technological advances have opened new possibilities for improving patient safety. Using technology to digitize healthcare processes has the potential to increase standardization and efficiency of clinical workflows and to reduce errors and cost across all healthcare settings. 1 However, if technological approaches are designed or implemented poorly, the burden on clinicians can increase. For example, overburdened clinicians can experience alert fatigue and fail to respond to notifications. This can lead to more medical errors. As a testament to the significance of this topic in recent years, several government agencies [(e.g. the Agency for Healthcare Research and Quality (AHRQ) and the Centers for Medicare and Medicaid services (CMS)] have developed resources to help healthcare organizations integrate technology, such as the Safety Assurance Factors for EHR Resilience (SAFER) guides developed by the Office of the National Coordinator for Health Information Technology (ONC). 2,3,4  However, there is some evidence that these resources have not been widely used.5 Recently, the Centers for Medicare & Medicaid Services (CMS) started requiring hospitals to use the SAFER guides as part of the FY 2022 Hospital Inpatient Prospective Payment Systems (IPPS), which should raise awareness and uptake of the guides. 6

During 2022, research into technological approaches was a major theme of articles on PSNet. Researchers reviewed all relevant articles on PSNet and consulted with Dr. A Jay Holmgren, PhD, and Dr. Susan McBride, PhD, subject matter experts in health IT and its role in patient safety. Key topics and themes are highlighted below.  

Clinical Decision Support  

The most prominent focus in the 2022 research on technology, based on the number of articles published on PSNet, was related to clinical decision support (CDS) tools. CDS provides clinicians, patients, and other individuals with relevant data (e.g. patient-specific information), purposefully filtered and delivered through a variety of formats and channels, to improve and enhance care. 7   

Computerized Patient Order Entry  

One of the main applications of CDS is in computerized patient order entry (CPOE), which is the process used by clinicians to enter and send treatment instructions via a computer application. 8 While the change from paper to electronic order entry itself can reduce errors (e.g., due to unclear handwriting or manual copy errors), research in 2022 showed that there is room for improvement in order entry systems, as well as some promising novel approaches. 

Two studies looked at the frequency of and reasons for medication errors in the absence of CDS and CPOE and demonstrated that there was a clear patient safety need. One study found that most medication errors occurred during the ordering or prescribing stage, and both this study and the other study found that the most common medication error was incorrect dose. Ongoing research, such as the AHRQ Medication Safety Measure Development project, aims to develop and validate measure specifications for wrong-patient, wrong-dose, wrong-medication, wrong-route, and wrong-frequency medication orders within EHR systems, in order to better understand and capture health IT safety events.9 Errors of this type could be avoided or at least reduced through the use of effective CPOE and CDS systems. However, even when CPOE and CDS are in place, errors can still occur and even be caused by the systems themselves. One study reviewed duplicate medication orders and found that 20% of duplicate orders resulted from technological issues, including alerts being overridden, alerts not firing, and automation issues (e.g., prefilled fields). A case study last year Illustrated one of the technological issues, in this case a manual keystroke error, that can lead to a safety event. A pharmacist mistakenly set the start date for a medication to the following year rather than the following day , which the CPOE system failed to flag. The authors recommended various alerts and coding changes in the system to prevent this particular error in the future.  

There were also studies in 2022 that showed successful outcomes of well-implemented CPOE systems. One in-depth pre-post, mixed-methods study showed that a fully implemented CPOE system significantly reduced specific serious and commonly occurring prescribing and procedural errors. The authors also presented evidence that it was cost-effective and detailed implementation lessons learned drawn from the qualitative data collected for the study. A specific CPOE function that demonstrated statistically significant improvement in 2022 was automatic deprescribing of medication orders and communication of the relevant information to pharmacies. Deprescribing is the planned and supervised process of dose reduction or stopping of a medication that is no longer beneficial or could be causing harm. That study showed an immediate and sustained 78% increase in successful discontinuations after implementation of the software. A second study on the same functionality determined that currently only one third to one half of medications are e-prescribed, and the study proposed that e-prescribing should be expanded to increase the impact of the deprescribing software. It should be noted, however, that the systems were not perfect and that a small percentage of medications were unintentionally cancelled. Finally, an algorithm to detect patients in need of follow-up after test results was developed and implemented in another study . The algorithm showed some process improvements, but outcome measures were not reported. 

Usability  

Usability of CDS systems was a large focus of research in 2022. Poorly designed systems that do not fit into existing workflows lead to frustrated users and increase the potential for errors. For example, if users are required to enter data in multiple places or prompted to enter data that are not available to them, they could find ways to work around the system or even cease to use it, increasing the potential for patient safety errors. The documentation burden is already very high on U.S. clinicians, 10 so it is important that novel technological approaches do not add to this burden but, if possible, alleviate it by offering a high level of usability and interoperability.  

One study used human-factored design in creating a CDS to diagnose pulmonary embolism in the Emergency Department and then surveyed clinician users about their experiences using the tool. Despite respondents giving the tool high usability ratings and reporting that the CDS was valuable, actual use of the tool was low. Based on the feedback from users, the authors proposed some changes to increase uptake, but both users and authors mentioned the challenges that arise when trying to change the existing workflow of clinicians without increasing their burden. Another study gathered qualitative feedback from clinicians on a theoretical CDS system for diagnosing neurological issues in the Emergency Department. In this study too, many clinicians saw the potential value in the CDS tool but had concerns about workflow integration and whether it would impact their ability to make clinical decisions. Finally, one study developed a dashboard to display various risk factors for multiple hospital-acquired infections and gathered feedback from users. The users generally found the dashboard useful and easy to learn, and they also provided valuable feedback on color scales, location, and types of data displayed. All of these studies show that attention to end user needs and preferences is necessary for successful implementation of CDS.  However, the recent market consolidation in Electronic Health Record vendors may have an impact on the amount of user feedback gathered and integrated into CDS systems. Larger vendors may have more resources to devote to improving the usability and design of CDS, or their near monopolies in the market may not provide an incentive to innovate further. 11 More research is needed as this trend continues.  

Alerts and Alarms 

Alerts and alarms are an important part of most CDS systems, as they can prompt clinicians with important and timely information during the treatment process. However, these alerts and alarms must be accurate and useful to elicit an appropriate response. The tradeoff between increased safety due to alerts and clinician alert fatigue is an important balance to strike. 12

Many studies in 2022 looked at clinician responses to medication-related alerts, including override and modification rates. Several of the studies found a high alert override rate but questioned the validity of using override rates alone as a marker of CDS effectiveness and usability. For example, one study looked at drug allergy alerts and found that although 44.8% of alerts were overridden, only 9.3% of those were inappropriately overridden, and very few overrides led to an adverse allergic reaction. A study on “do not give” alerts found that clinicians modified their orders to comply with alert recommendations after 78% of alerts but only cancelled orders after 26% of alerts. A scoping review looked at drug-drug interaction alerts and found similar results, including high override rates and the need for more data on why alerts are overridden. These findings are supported by another study that found that the underlying drug value sets triggering drug-drug interaction alerts are often inconsistent, leading to many inappropriate alerts that are then appropriately overridden by clinicians. These studies suggest that while a certain number of overrides should be expected, the underlying criteria for alert systems should be designed and regularly reviewed with specificity and sensitivity in mind. This will increase the frequency of appropriate alerts that foster indicated clinical action and reduce alert fatigue. 

There also seems to be variability in the effectiveness of alert systems across sites. One study looked at an alert to add an item to the problem list if a clinician placed an order for a medication that was not indicated based on the patient’s chart. The study found about 90% accuracy in alerts across two sites but a wide difference in the frequency of appropriate action between the sites (83% and 47%). This suggests that contextual factors at each site, such as culture and organizational processes, may impact success as much as the technology itself.  

A different study looked at the psychology of dismissing alerts using log data and found that dismissing alerts becomes habitual and that the habit is self-reinforcing over time. Furthermore, nearly three quarters of alerts were dismissed within 3 seconds. This indicates how challenging it can be to change or disrupt alert habits once they are formed. 

Artificial Intelligence and Machine Learning  

In recent years, one of the largest areas of burgeoning technology in healthcare has been artificial intelligence (AI) and machine learning. AI and machine learning use algorithms to absorb large amounts of historical and real-time data and then predict outcomes and recommend treatment options as new data are entered by clinicians. Research in 2022 showed that these techniques are starting to be integrated into EHR and CDS systems, but challenges remain. A full discussion of this topic is beyond the scope of this review. Here we limit the discussion to several patient-safety-focused resources posted on PSNet in 2022.  

One of the promising aspects of AI is its ability to improve CDS processes and clinician workflow overall. For example, one study last year looked at using machine learning to improve and filter CDS alerts. They found that the software could reduce alert volume by 54% while maintaining high precision. Reducing alert volume has the potential to alleviate alert fatigue and habitual overriding. Another topic explored in a scoping review was the use of AI to reduce adverse drug events. While only a few studies reviewed implementation in a clinical setting (most evaluated algorithm technical performance), several promising uses were found for AI systems that predict risk of an adverse drug event, which would facilitate early detection and mitigate negative effects.  

Despite enthusiasm for and promising applications of AI, implementation is slow. One of the challenges facing implementation is the variable quality of the systems. For example, a commonly used sepsis detection model was recently found to have very low sensitivity. 13 Algorithms also drift over time as new data are integrated, and this can affect performance, particularly during and after large disturbances like the COVID-19 pandemic. 14 There is also emerging research about the impact of AI algorithms on racial and ethnic biases in healthcare; at the time of publication of this essay, an AHRQ EPC was conducting a review of evidence on the topic. 15  These examples highlight the fact that AI is not a “set it and forget it” application; it requires monitoring and customization from a dedicated resource to ensure that the algorithms perform well over time. A related challenge is the lack of a strong business case for using high-quality AI. Because of this, many health systems choose to use out-of-the-box AI algorithms, which may be of poor quality overall (or are unsuited to particular settings) and may also be “black box” algorithms (i.e., not customizable by the health system because the vendor will not allow access to the underlying code). 16 The variable quality and the lack of transparency may cause mistrust by clinicians and overall aversion to AI interventions.  

In an attempt to address these concerns, one article in 2022 detailed best practices for AI implementation in health systems, focusing on the business case. Best practices include using AI to address a priority problem for the health system rather than treating it as an end itself. Additionally, testing the AI using the health system’s patients and data to demonstrate applicability and accuracy for that setting, confirming that the AI can provide a return on investment, and ensuring that the AI can be implemented easily and efficiently are also important. Another white paper described a human-factors and ergonomics framework for developing AI in order to improve the implementation within healthcare systems, teams, and workflows. The federal government and international organizations have also published AI guidelines, focusing on increasing trustworthiness (National Artificial Intelligence Initiative) 17 and ensuring ethical governance (World Health Organization). 18   

Conclusion and Next Steps 

As highlighted in this review, the scope and complexity of technology and its application in healthcare can be intimidating for healthcare systems to approach and implement. Researchers last year thus created a framework that health systems can use to assess their digital maturity and guide their plans for further integration.  

The field would benefit from more research in several areas in upcoming years. First and foremost, high-quality prospective outcome studies are needed to validate the effectiveness of the new technologies. Second, more work is needed on system usability, how the systems are integrated into workflows, and how they affect the documentation burden placed on clinicians. For CDS specifically, more focus is needed on patient-centered CDS (PC CDS), which supports patient-centered care by helping clinicians and patients make the best decisions given each individual’s circumstances and preferences. 19 AHRQ is already leading efforts in this field with their CDS Innovation Collaborative project. 20 Finally, as it becomes more common to incorporate EHR scribes to ease the documentation burden, research on their impact on patient safety will be needed, especially in relation to new technological approaches. For example, when a scribe encounters a CDS alert, do they alert the clinician in all cases? 

In addition to the approaches mentioned in this article, other emerging technologies in early stages of development hold theoretical promise for improving patient safety. One prominent example is “computer vision,” which uses cameras and AI to gather and process data on what physically happens in healthcare settings beyond what is captured in EHR data, 21 including being able to detect immediately that a patient fell in their room. 22  

As technology continues to expand and improve, researchers, clinicians, and health systems must be mindful of potential stumbling blocks that could impede progress and threaten patient safety. However, technology presents a wide array of opportunities to make healthcare more integrated, efficient, and safe.  

• Cohen CC, Powell K, Dick AW, et al. The Association Between Nursing Home Information Technology Maturity and Urinary Tract Infection Among Long-Term Residents . J Appl Gerontol . 2022;41(7):1695-1701. doi: 10.1177/07334648221082024. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9232878/
• https://www.healthit.gov/topic/safety/safer-guides
• https://cds.ahrq.gov/cdsconnect/repository
• https://www.cms.gov/about-cms/obrhi
• McBride S, Makar E, Ross A, et al. Determining awareness of the SAFER guides among nurse informaticists. J Inform Nurs. 2021;6(4). https://library.ania.org/ania/articles/713/view
• Sittig DF, Sengstack P, Singh H. Guidelines for US hospitals and clinicians on assessment of electronic health record safety using SAFER guides. J ama . 2022;327:719-720.
• https://library.ahima.org/doc?oid=300027#.Y-6RhXbMKHt
• https://www.healthit.gov/faq/what-computerized-provider-order-entry#:~:text=Computerized%20provider%20order%20entry%20(CPOE,paper%2C%20fax%2C%20or%20telephone
• https://digital.ahrq.gov/2018-year-review/research-spotlights/leveragin…
• Holmgren AJ, Downing NL, Bates DW, et al. Assessment of electronic health record use between US and non-US health systems. JAMA Intern Med. 2021;181:251-259. https://doi.org/10.1001/jamainternmed.2020.7071
• Holmgren AJ, Apathy NC. Trends in US hospital electronic health record vendor market concentration, 2012–2021. J Gen Intern Med. 2022. https://link.springer.com/article/10.1007/s11606-022-07917-3#citeas
• Co Z, Holmgren AJ, Classen DC, et al. The tradeoffs between safety and alert fatigue: data from a national evaluation of hospital medication-related clinical decision support. J Am Med Inform Assoc. 2020;27:1252-1258. https://pubmed.ncbi.nlm.nih.gov/32620948/
• Wong A, Otles E, Donnelly JP, et al. External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients. JAMA Intern Med. 2021;181:1065-1070. https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2781307
• Parikh RB, Zhang Y, Kolla L, et al. Performance drift in a mortality prediction algorithm among patients with cancer during the SARS-CoV-2 pandemic. J Am Med Inform Assoc. 2022;30:348-354. https://academic.oup.com/jamia/advance-article/doi/10.1093/jamia/ocac221/6835770?login=false
• https://effectivehealthcare.ahrq.gov/products/racial-disparities-health…
• https://www.statnews.com/2022/05/24/market-failure-preventing-efficient-diffusion-health-care-ai-software/
• https://www.ai.gov/strategic-pillars/advancing-trustworthy-ai/
• Ethics and governance of artificial intelligence for health (WHO guidance). Geneva: World Health Organization; 2021. https://www.who.int/publications/i/item/9789240029200
• Dullabh P, Sandberg SF, Heaney-Huls K, et al. Challenges and opportunities for advancing patient-centered clinical decision support: findings from a horizon scan. J Am Med Inform Assoc. 2022: 29(7):1233-1243. doi: 10.1093/jamia/ocac059. PMID: 35534996; PMCID: PMC9196686.
• https://cds.ahrq.gov/cdsic
• Yeung S, Downing NL, Fei-Fei L, et al. Bedside computer vision: moving artificial intelligence from driver assistance to patient safety. N Engl J Med. 2018;387:1271-1273. https://www.nejm.org/doi/10.1056/NEJMp1716891
• Espinosa R, Ponce H, Gutiérrez S, et al. A vision-based approach for fall detection using multiple cameras and convolutional neural networks: a case study using the UP-Fall detection dataset. Comput Biol Med. 2019;115:103520. https://doi.org/10.1016/j.compbiomed.2019.103520

This project was funded under contract number 75Q80119C00004 from the Agency for Healthcare Research and Quality (AHRQ), U.S. Department of Health and Human Services. The authors are solely responsible for this report’s contents, findings, and conclusions, which do not necessarily represent the views of AHRQ. Readers should not interpret any statement in this report as an official position of AHRQ or of the U.S. Department of Health and Human Services. None of the authors has any affiliation or financial involvement that conflicts with the material presented in this report. View AHRQ Disclaimers

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Improving the specificity of drug-drug interaction alerts: can it be done? April 6, 2022

A qualitative study of prescribing errors among multi-professional prescribers within an e-prescribing system. December 23, 2020

The tradeoffs between safety and alert fatigue: data from a national evaluation of hospital medication-related clinical decision support. July 29, 2020

Assessment of health information technology-related outpatient diagnostic delays in the US Veterans Affairs health care system: a qualitative study of aggregated root cause analysis data. July 22, 2020

Reducing drug prescription errors and adverse drug events by application of a probabilistic, machine-learning based clinical decision support system in an inpatient setting. August 21, 2019

Improving medication-related clinical decision support. March 7, 2018

The frequency of inappropriate nonformulary medication alert overrides in the inpatient setting. April 6, 2016

The effect of provider characteristics on the responses to medication-related decision support alerts. July 15, 2015

Best practices: an electronic drug alert program to improve safety in an accountable care environment. July 1, 2015

Impact of computerized physician order entry alerts on prescribing in older patients. March 25, 2015

Differences of reasons for alert overrides on contraindicated co-prescriptions by admitting department. December 17, 2014

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More From Forbes

Saving time and health: leveraging technology to improve the healthcare industry.

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Author, Hitesh Dev is the COO at Devout Corporation and a health tech consultant supporting Federal and Commercial organizations since 2002.

Denis Waitley is believed to have said, "Time and health are two precious assets that we don't recognize and appreciate until they have been depleted." And in today's fast-paced world, managing time and health are extremely important as these are the assets that make or break our lives.

Further, by leveraging artificial intelligence (AI) tools and new technology, healthcare companies can automate mundane tasks, leverage personalized health insights and also improve patients' quality of life.

Depleting Assets: Time And Health

We do not realize that some of these tools are already part and parcel of our daily lives. For example, Outlook and Google excel in managing calendars, setting reminders and automating routine emails/tasks. Apple's Siri offers seamless integration with iOS devices, making it perfect for similar functions. You can leverage AI tools like ChatGPT for getting ready for your meetings, summarizing documents and cutting down the hours spent on research. Your co-workers can also get task notifications and reminders as needed.

Modern technology has also brought us a plethora of AI-powered tools that offer personalized health monitoring and management, making it easier to take care of our well-being. Who doesn't use Fitbit and Apple Watch these days? These tools ask us to take 10,000 steps a day and stand up frequently, and also remind us to drink water.

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AI's role also extends into more specialized health care and long-term management, particularly for chronic conditions or the aging population. Some of the ways healthcare companies are leveraging AI is by automating tasks such as medical image analysis, diagnosis and treatment planning. Natural language processing is also being used to extract valuable information from unstructured clinical notes and research literature.

Overcoming The Challenges Of New Technology In The Healthcare Industry

Implementing new technologies can bring numerous benefits, but it also comes with its own set of challenges and pitfalls. Implementing technology without clearly defined goals can lead to ineffective outcomes and wasted resources. Is the goal to improve patient outcomes or reduce costs, or both?

Inefficient user training can also hinder the successful adoption of new technologies. It’s essential to provide comprehensive training to all user personas in order to change the culture and utilization of the new system.

Some of the other areas that are important to keep in mind are integration with existing systems and incorporating a feedback loop. In my experience, using a scaled agile framework ( SAFe ) can help to build a culture of continuous improvement. Also, involving solution architects throughout the project can help ensure that all the existing systems are integrated with newer technologies. (Disclosure: My company helps with this, as do others.)

Finally, data security and privacy are at the forefront of many recent news stories. Failure to implement adequate security measures can lead to data breaches, regulatory violations and damage to reputation. It’s essential to prioritize data security from the outset—and compliance with regulations such as HIPAA (Health Insurance Portability and Accountability Act) and GDPR (General Data Protection Regulation).

I think the integration of AI in the health industry underscores a transformative shift toward proactive and personalized healthcare. By leveraging these technologies, companies can engage in more preventative care and provide tailored support for long-term well-being. Also, by using the right time and productivity management tools, professionals can get their time back!

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Importance of Therapeutic Communication in Nursing

This essay about therapeutic communication in nursing highlights its pivotal role in fostering trust, understanding, and healing between nurses and patients. It explores the significance of human connection through communication in healthcare settings, emphasizing active listening, empathy, and respect. The essay discusses how therapeutic communication builds trust, gathers crucial patient information, enhances satisfaction, and benefits both patients and nursing professionals. It underscores the importance of honing communication skills for delivering high-quality, patient-centered care in the evolving healthcare landscape.

How it works

Therapeutic communication stands as the secret sauce in the rich recipe of nursing care, infusing the clinical setting with warmth, understanding, and healing. In the hustle and bustle of healthcare, where technology often takes the spotlight, the human touch through communication emerges as the unsung hero, weaving bonds of trust and empathy between nurses and their patients. It’s a symphony of verbal and nonverbal gestures aimed not just at conveying information, but at creating a safe space where patients feel truly seen, heard, and valued.

At its essence, therapeutic communication embodies the art of active listening, empathy, and honoring the patient’s autonomy. It’s about more than just exchanging words—it’s about building bridges of understanding that traverse the chasm between illness and wellness. Nurses who excel in this dance of dialogue understand that each interaction is an opportunity to forge a connection, to reach beyond the clinical veneer and touch the human heart beneath.

One of the magical outcomes of therapeutic communication is its ability to sow the seeds of trust and camaraderie between nurse and patient. Trust is the cornerstone of healing, the bedrock upon which patients feel empowered to share their deepest fears, concerns, and hopes. By lending an empathetic ear and a caring presence, nurses cultivate a fertile ground where patients feel safe to be vulnerable, to express themselves without fear of judgment.

Moreover, therapeutic communication serves as a treasure trove of insights, a window into the inner world of the patient. Through attentive listening and skillful questioning, nurses unlock the mysteries that lie beyond the surface—a twinge of pain hidden behind a smile, a flicker of anxiety concealed beneath a brave facade. This holistic approach to communication enables nurses to tailor their care plans to fit the unique contours of each patient’s journey, ensuring that no voice goes unheard, no need goes unmet.

Beyond the tangible benefits for patients, therapeutic communication casts a radiant glow upon the nursing profession itself. Engaging in genuine, meaningful interactions with patients becomes a source of nourishment for the soul, a reminder of why nurses embarked on this noble calling in the first place. In the shared moments of laughter, tears, and everything in between, nurses find fulfillment, purpose, and a deep sense of belonging to a profession built on the pillars of compassion and humanity.

In the grand tapestry of healthcare, therapeutic communication emerges as a golden thread, weaving its way through the fabric of healing and transforming sterile clinical environments into havens of compassion and empathy. As the healthcare landscape continues to evolve, one thing remains constant—the profound impact of human connection, forged through the simple yet profound act of communication. In the words left unspoken, in the gestures of kindness, lies the transformative power to heal, to uplift, and to remind us all of our shared humanity

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