medical records research articles

• Open access
• Published: 27 October 2021

A narrative review on the validity of electronic health record-based research in epidemiology

• Milena A. Gianfrancesco 1 &
• Neal D. Goldstein   ORCID: orcid.org/0000-0002-9597-5251 2  

BMC Medical Research Methodology volume  21 , Article number:  234 ( 2021 ) Cite this article

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Electronic health records (EHRs) are widely used in epidemiological research, but the validity of the results is dependent upon the assumptions made about the healthcare system, the patient, and the provider. In this review, we identify four overarching challenges in using EHR-based data for epidemiological analysis, with a particular emphasis on threats to validity. These challenges include representativeness of the EHR to a target population, the availability and interpretability of clinical and non-clinical data, and missing data at both the variable and observation levels. Each challenge reveals layers of assumptions that the epidemiologist is required to make, from the point of patient entry into the healthcare system, to the provider documenting the results of the clinical exam and follow-up of the patient longitudinally; all with the potential to bias the results of analysis of these data. Understanding the extent of as well as remediating potential biases requires a variety of methodological approaches, from traditional sensitivity analyses and validation studies, to newer techniques such as natural language processing. Beyond methods to address these challenges, it will remain crucial for epidemiologists to engage with clinicians and informaticians at their institutions to ensure data quality and accessibility by forming multidisciplinary teams around specific research projects.

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The proliferation of electronic health records (EHRs) spurred on by federal government incentives over the past few decades has resulted in greater than an 80% adoption-rate at hospitals [ 1 ] and close to 90% in office-based practices [ 2 ] in the United States. A natural consequence of the availability of electronic health data is the conduct of research with these data, both observational and experimental [ 3 ], due to lower overhead costs and lower burden of study recruitment [ 4 ]. Indeed, a search on PubMed for publications indexed by the MeSH term “electronic health records” reveals an exponential growth in biomedical literature, especially over the last 10 years with an excess of 50,000 publications.

An emerging literature is beginning to recognize the many challenges that still lay ahead in using EHR data for epidemiological investigations. Researchers in Europe identified 13 potential sources of “bias” (bias was defined as a contamination of the data) in EHR-based data covering almost every aspect of care delivery, from selective entrance into the healthcare system, to variation in care and documentation practices, to identification and extraction of the right data for analysis [ 5 ]. Many of the identified contaminants are directly relevant to traditional epidemiological threats to validity [ 4 ]. Data quality has consistently been invoked as a central challenge in EHRs. From a qualitative perspective, healthcare workers have described challenges in the healthcare environment (e.g., heavy workload), imperfect clinical documentation practices, and concerns over data extraction and reporting tools, all of which would impact the quality of data in the EHR [ 6 ]. From a quantitative perspective, researchers have noted limited sensitivity of diagnostic codes in the EHR when relying on discrete codings, noting that upon a manual chart review free text fields often capture the missed information, motivating such techniques as natural language processing (NLP) [ 7 ]. A systematic review of EHR-based studies also identified data quality as an overarching barrier to the use of EHRs in managing the health of the community, i.e. “population health” [ 8 ]. Encouragingly this same review also identified more facilitators than barriers to the use of EHRs in public health, suggesting that opportunities outweigh the challenges. Shortreed et al. further explored these opportunities discussing how EHRs can enhance pragmatic trials, bring additional sophistication to observational studies, aid in predictive modeling, and be linked together to create more comprehensive views of patients’ health [ 9 ]. Yet, as Shortreed and others have noted, significant challenges still remain.

It is our intention with this narrative review to discuss some of these challenges in further detail. In particular, we focus on specific epidemiological threats to validity -- internal and external -- and how EHR-based epidemiological research in particular can exacerbate some of these threats. We note that while there is some overlap in the challenges we discuss with traditional paper-based medical record research that has occurred for decades, the scale and scope of an EHR-based study is often well beyond what was traditionally possible in the manual chart review era and our applied examples attempt to reflect this. We also describe existing and emerging approaches for remediating these potential biases as they arise. A summary of these challenges may be found in Table 1 . Our review is grounded in the healthcare system in the United States, although we expect many of the issues we describe to be applicable regardless of locale; where necessary, we have flagged our comments as specific to the U.S.

Challenge #1: Representativeness

The selection process for how patients are captured in the EHR is complex and a function of geographic, social, demographic, and economic determinants [ 10 ]. This can be termed the catchment of the EHR. For a patient record to appear in the EHR the patient must have been registered in the system, typically to capture their demographic and billing information, and upon a clinical visit, their health details. While this process is not new to clinical epidemiology, what tends to separate EHR-based records from traditional paper-based records is the scale and scope of the data. Patient data may be available for longer periods of time longitudinally, as well as have data corresponding to interactions with multiple, potentially disparate, healthcare systems [ 11 ]. Given the consolidation of healthcare [ 12 ] and aggregated views of multiple EHRs through health information networks or exchanges [ 11 ] the ability to have a complete view of the patients’ total health is increasing. Importantly, the epidemiologist must ascertain whether the population captured within the EHR or EHR-derived data is representative of the population targeted for inference. This is particularly true under the paradigm of population health and inferring the health status of a community from EHR-based records [ 13 ]. For example, a study of Clostridium difficile infection at an urban safety net hospital in Philadelphia, Pennsylvania demonstrated notable differences in risk factors in the hospital’s EHR compared to national surveillance data, suggesting how catchment can influence epidemiologic measures [ 14 ]. Even health-related data captured through health information exchanges may be incomplete [ 15 ].

Several hypothetical study settings can further help the epidemiologist appreciate the relationship between representativeness and validity in EHR research. In the first hypothetical, an EHR-based study is conducted from a single-location federally qualified health center, and in the second hypothetical, an EHR-based study is conducted from a large academic health system. Suppose both studies occur in the same geographic area. It is reasonable to believe the patient populations captured in both EHRs will be quite different and the catchment process could lead to divergent estimates of disease or risk factor prevalence. The large academic health system may be less likely to capture primary care visits, as specialty care may drive the preponderance of patient encounters. However, this is not a bias per se : if the target of inference from these two hypothetical EHR-based studies is the local community, then selection bias becomes a distinct possibility. The epidemiologist must also consider the potential for generalizability and transportability -- two facets of external validity that respectively relate to the extrapolation of study findings to the source population or a different population altogether -- if there are unmeasured effect modifiers, treatment interference, or compound treatments in the community targeted for inference [ 16 ].

There are several approaches for ascertaining representativeness of EHR-based data. Comparing the EHR-derived sample to Census estimates of demography is straightforward but has several important limitations. First, as previously described, the catchment process may be driven by discordant geographical areas, especially for specialty care settings. Second and third, the EHR may have limited or inaccurate information on socioeconomic status, race, and ethnicity that one may wish to compare [ 17 , 18 ], and conversely the Census has limited estimates of health, chiefly disability, fertility, and insurance and payments [ 19 ]. If selection bias is suspected as a result of missing visits in a longitudinal study [ 20 ] or the catchment process in a cross-sectional study [ 21 ], using inverse probability weighting may remediate its influence. Comparing the weighted estimates to the original, non-weighted estimates provides insight into differences in the study participants. In the population health paradigm whereby the EHR is used as a surveillance tool to identify community health disparities [ 13 ], one also needs to be concerned about representativeness. There are emerging approaches for producing such small area community estimates from large observational datasets [ 22 , 23 ]. Conceivably, these approaches may also be useful for identifying issues of representativeness, for example by comparing stratified estimates across sociodemographic or other factors that may relate to catchment. Approaches for issues concerning representativeness specifically as it applies to external validity may be found in these references [ 24 , 25 ].

Challenge #2: Data availability and interpretation

Sub-challenge #2.1: billing versus clinical versus epidemiological needs.

There is an inherent tension in the use of EHR-based data for research purposes: the EHR was never originally designed for research. In the U.S., the Health Information Technology for Economic and Clinical Health Act, which promoted EHRs as a platform for comparative effectiveness research, was an attempt to address this deficiency [ 26 ]. A brief history of the evolution of the modern EHR reveals a technology that was optimized for capturing health details relevant for billing, scheduling, and clinical record keeping [ 27 ]. As such, the availability of data for fundamental markers of upstream health that are important for identifying inequities, such as socioeconomic status, race, ethnicity, and other social determinants of health (SDOH), may be insufficiently captured in the EHR [ 17 , 18 ]. Similarly, behavioral risk factors, such as being a sexual minority person, have historically been insufficiently recorded as discrete variables. It is only recently that such data are beginning to be captured in the EHR [ 28 , 29 ], or techniques such as NLP have made it possible to extract these details when stored in free text notes (described further in “ Unstructured data: clinical notes and reports ” section).

As an example, assessing clinical morbidities in the EHR may be done on the basis of extracting appropriate International Classification of Diseases (ICD) codes, used for billing and reimbursement in the U.S. These codes are known to have low sensitivity despite high specificity for accurate diagnostic status [ 30 , 31 ]. Expressed as predictive values, which depend upon prevalence, presence of a diagnostic code is a likely indicator of a disease state, whereas absence of a diagnostic code is a less reliable indicator of the absence of that morbidity. There may further be variation by clinical domain in that ICD codes may exist but not be used in some specialties [ 32 ], variation by coding vocabulary such as the use of SNOMED for clinical documentation versus ICD for billing necessitating an ontology mapper [ 33 ], and variation by the use of “rule-out” diagnostic codes resulting in false-positive diagnoses [ 34 , 35 , 36 ]. Relatedly is the notion of upcoding, or the billing of tests, procedures, or diagnoses to receive inflated reimbursement, which, although posited to be problematic in EHRs [ 37 ] in at least one study, has not been shown to have occurred [ 38 ]. In the U.S., the billing and reimbursement model, such as fee-for-service versus managed care, may result in varying diagnostic code sensitivities and specificities, especially if upcoding is occurring [ 39 ]. In short, there is potential for misclassification of key health data in the EHR.

Misclassification can potentially be addressed through a validation study (resources permitting) or application of quantitative bias analysis, and there is a rich literature regarding the treatment of misclassified data in statistics and epidemiology. Readers are referred to these texts as a starting point [ 40 , 41 ]. Duda et al. and Shepherd et al. have described an innovative data audit approach applicable to secondary analysis of observational data, such as EHR-derived data, that incorporates the audit error rate directly in the regression analysis to reduce information bias [ 42 , 43 ]. Outside of methodological tricks in the face of imperfect data, researchers must proactively engage with clinical and informatics colleagues to ensure that the right data for the research interests are available and accessible.

Sub-challenge #2.2: Consistency in data and interpretation

For the epidemiologist, abstracting data from the EHR into a research-ready analytic dataset presents a host of complications surrounding data availability, consistency and interpretation. It is easy to conflate the total volume of data in the EHR with data that are usable for research, however expectations should be tempered. Weiskopf et al. have noted such challenges for the researcher: in their study, less than 50% of patient records had “complete” data for research purposes per their four definitions of completeness [ 44 ]. Decisions made about the treatment of incomplete data can induce selection bias or impact precision of estimates (see Challenges #1 , #3 , and #4 ). The COVID-19 pandemic has further demonstrated the challenge of obtaining research data from EHRs across multiple health systems [ 45 ]. On the other hand, EHRs have a key advantage of providing near real-time data as opposed to many epidemiological studies that have a specific endpoint or are retrospective in nature. Such real-time data availability was leveraged during COVID-19 to help healthcare systems manage their pandemic response [ 46 , 47 ]. Logistical and technical issues aside, healthcare and documentation practices are nuanced to their local environments. In fact, researchers have demonstrated how the same research question analyzed in distinct clinical databases can yield different results [ 48 ].

Once the data are obtained, choices regarding operationalization of variables have the potential to induce information bias. Several hypothetical examples can help demonstrate this point. As a first example, differences in laboratory reporting may result in measurement error or misclassification. While the order for a particular laboratory assay is likely consistent within the healthcare system, patients frequently have a choice where to have that order fulfilled. Given the breadth of assays and reporting differences that may differ lab to lab [ 49 ], it is possible that the researcher working with the raw data may not consider all possible permutations. In other words, there may be lack of consistency in the reporting of the assay results. As a second example, raw clinical data requires interpretation to become actionable. A researcher interested in capturing a patient’s Charlson comorbidity index, which is based on 16 potential diagnoses plus the patient’s age [ 50 ], may never find such a variable in the EHR. Rather, this would require operationalization based on the raw data, each of which may be misclassified. Use of such composite measures introduces the notion of “differential item functioning”, whereby a summary indicator of a complexly measured health phenomenon may differ from group to group [ 51 ]. In this case, as opposed to a measurement error bias, this is one of residual confounding in that a key (unmeasured) variable is driving the differences. Remediation of these threats to validity may involve validation studies to determine the accuracy of a particular classifier, sensitivity analysis employing alternative interpretations when the raw data are available, and omitting or imputing biased or latent variables [ 40 , 41 , 52 ]. Importantly, in all cases, the epidemiologists should work with the various health care providers and personnel who have measured and recorded the data present in the EHR, as they likely understand it best.

Furthermore and related to “Billing versus Clinical versus Epidemiological Needs” section, the healthcare system in the U.S. is fragmented with multiple payers, both public and private, potentially exacerbating the data quality issues we describe, especially when linking data across healthcare systems. Single payer systems have enabled large and near-complete population-based studies due to data availability and consistency [ 53 , 54 , 55 ]. Data may also be inconsistent for retrospective longitudinal studies spanning many years if there have been changes to coding standards or practices over time, for example due to the transition from ICD-9 to ICD-10 largely occurring in the mid 2010s or the adoption of the Patient Protection and Affordable Care Act in the U.S. in 2010 with its accompanying changes in billing. Exploratory data analysis may reveal unexpected differences in key variables, by place or time, and recoding, when possible, can enforce consistency.

Sub-challenge #2.3: Unstructured data: clinical notes and reports

There may also be scenarios where structured data fields, while available, are not traditionally or consistently used within a given medical center or by a given provider. For example, reporting of adverse events of medications, disease symptoms, and vaccinations or hospitalizations occurring at different facility/health networks may not always be entered by providers in structured EHR fields. Instead, these types of patient experiences may be more likely to be documented in an unstructured clinical note, report (e.g. pathology or radiology report), or scanned document. Therefore, reliance on structured data to identify and study such issues may result in underestimation and potentially biased results.

Advances in NLP currently allow for information to be extracted from unstructured clinical notes and text fields in a reliable and accurate manner using computational methods. NLP utilizes a range of different statistical, machine learning, and linguistic techniques, and when applied to EHR data, has the potential to facilitate more accurate detection of events not traditionally located or consistently used in structured fields. Various NLP methods can be implemented in medical text analysis, ranging from simplistic and fast term recognition systems to more advanced, commercial NLP systems [ 56 ]. Several studies have successfully utilized text mining to extract information on a variety of health-related issues within clinical notes, such as opioid use [ 57 ], adverse events [ 58 , 59 ], symptoms (e.g., shortness of breath, depression, pain) [ 60 ], and disease phenotype information documented in pathology or radiology reports, including cancer stage, histology, and tumor grade [ 61 ], and lupus nephritis [ 32 ]. It is worth noting that scanned documents involve an additional layer of computation, relying on techniques such as optical character recognition, before NLP can be applied.

Hybrid approaches that combine both narrative and structured data, such as ICD codes, to improve accuracy of detecting phenotypes have also demonstrated high performance. Banerji et al. found that using ICD-9 codes to identify allergic drug reactions in the EHR had a positive predictive value of 46%, while an NLP algorithm in conjunction with ICD-9 codes resulted in a positive predictive value of 86%; negative predictive value also increased in the combined algorithm (76%) compared to ICD-9 codes alone (39%) [ 62 ]. In another example, researchers found that the combination of unstructured clinical notes with structured data for prediction tasks involving in-hospital mortality and 30-day hospital readmission outperformed models using either clinical notes or structured data alone [ 63 ]. As we move forward in analyzing EHR data, it will be important to take advantage of the wealth of information buried in unstructured data to assist in phenotyping patient characteristics and outcomes, capture missing confounders used in multivariate analyses, and develop prediction models.

Challenge #3: Missing measurements

While clinical notes may be useful to recover incomplete information from structured data fields, it may be the case that certain variables are not collected within the EHR at all. As mentioned above, it is important to remember that EHRs were not developed as a research tool (see “ Billing versus clinical versus epidemiological needs ” section), and important variables often used in epidemiologic research may not be typically included in EHRs including socioeconomic status (education, income, occupation) and SDOH [ 17 , 18 ]. Depending upon the interest of the provider or clinical importance placed upon a given variable, this information may be included in clinical notes. While NLP could be used to capture these variables, because they may not be consistently captured, there may be bias in identifying those with a positive mention as a positive case and those with no mention as a negative case. For example, if a given provider inquires about homelessness of a patient based on knowledge of the patient’s situation or other external factors and documents this in the clinical note, we have greater assurance that this is a true positive case. However, lack of mention of homelessness in a clinical note should not be assumed as a true negative case for several reasons: not all providers may feel comfortable asking about and/or documenting homelessness, they may not deem this variable worth noting, or implicit bias among clinicians may affect what is captured. As a result, such cases (i.e. no mention of homelessness) may be incorrectly identified as “not homeless,” leading to selection bias should a researcher form a cohort exclusively of patients who are identified as homeless in the EHR.

Not adjusting for certain measurements missing from EHR data can also lead to biased results if the measurement is an important confounder. Consider the example of distinguishing between prevalent and incident cases of disease when examining associations between disease treatments and patient outcomes [ 64 ]. The first date of an ICD code entered for a given patient may not necessarily be the true date of diagnosis, but rather documentation of an existing diagnosis. This limits the ability to adjust for disease duration, which may be an important confounder in studies comparing various treatments with patient outcomes over time, and may also lead to reverse causality if disease sequalae are assumed to be risk factors.

Methods to supplement EHR data with external data have been used to capture missing information. These methods may include imputation if information (e.g. race, lab values) is collected on a subset of patients within the EHR. It is important to examine whether missingness occurs completely at random or at random (“ignorable”), or not at random (“non-ignorable”), using the data available to determine factors associated with missingness, which will also inform the best imputation strategy to pursue, if any [ 65 , 66 ]. As an example, suppose we are interested in ascertaining a patient's BMI from the EHR. If men were less likely to have BMI measured than women, the probability of missing data (BMI) depends on the observed data (gender) and may therefore be predictable and imputable. On the other hand, suppose underweight individuals were less likely to have BMI measured; the probability of missing data depends on its own value, and as such is non-predictable and may require a validation study to confirm. Alternatively to imputing missing data, surrogate measures may be used, such as inferring area-based SES indicators, including median household income, percent poverty, or area deprivation index, by zip code [ 67 , 68 ]. Lastly, validation studies utilizing external datasets may prove helpful, such as supplementing EHR data with claims data that may be available for a subset of patients (see Challenge #4 ).

As EHRs are increasingly being used for research, there are active pushes to include more structured data fields that are important to population health research, such as SDOH [ 69 ]. Inclusion of such factors are likely to result in improved patient care and outcomes, through increased precision in disease diagnosis, more effective shared decision making, identification of risk factors, and tailoring services to a given population’s needs [ 70 ]. In fact, a recent review found that when individual level SDOH were included in predictive modeling, they overwhelmingly improved performance in medication adherence, risk of hospitalization, 30-day rehospitalizations, suicide attempts, and other healthcare services [ 71 ]. Whether or not these fields will be utilized after their inclusion in the EHR may ultimately depend upon federal and state incentives, as well as support from local stakeholders, and this does not address historic, retrospective analyses of these data.

Challenge #4: Missing visits

Beyond missing variable data that may not be captured during a clinical encounter, either through structured data or clinical notes, there also may be missing information for a patient as a whole. This can occur in a variety of ways; for example, a patient may have one or two documented visits in the EHR and then is never seen again (i.e. right censoring due to lost to follow-up), or a patient is referred from elsewhere to seek specialty care, with no information captured regarding other external issues (i.e. left censoring). This may be especially common in circumstances where a given EHR is more likely to capture specialty clinics versus primary care (see Challenge #1 ). A third scenario may include patients who appear, then are not observed for a long period of time, and then reappear: this case is particularly problematic as it may appear the patient was never lost to follow up but simply had fewer visits. In any of these scenarios, a researcher will lack a holistic view of the patient’s experiences, diagnoses, results, and more. As discussed above, assuming absence of a diagnostic code as absence of disease may lead to information and/or selection bias. Further, it has been demonstrated that one key source of bias in EHRs is “informed presence” bias, where those with more medical encounters are more likely to be diagnosed with various conditions (similar to Berkson’s bias) [ 72 ].

Several solutions to these issues have been proposed. For example, it is common for EHR studies to condition on observation time (i.e. ≥n visits required to be eligible into cohort); however, this may exclude a substantial amount of patients with certain characteristics, incurring a selection bias or limiting the generalizability of study findings (see Challenge #1 ). Other strategies attempt to account for missing visit biases through longitudinal imputation approaches; for example, if a patient missed a visit, a disease activity score can be imputed for that point in time, given other data points [ 73 , 74 ]. Surrogate measures may also be used to infer patient outcomes, such as controlling for “informative” missingness as an indicator variable or using actual number of missed visits that were scheduled as a proxy for external circumstances influencing care [ 20 ]. To address “informed presence” bias described above, conditioning on the number of health-care encounters may be appropriate [ 72 ]. Understanding the reason for the missing visit may help identify the best course of action and before imputing, one should be able to identify the type of missingness, whether “informative” or not [ 65 , 66 ]. For example, if distance to a healthcare location is related to appointment attendance, being able to account for this in analysis would be important: researchers have shown how the catchment of a healthcare facility can induce selection bias [ 21 ]. Relatedly, as telehealth becomes more common fueled by the COVID-19 pandemic [ 75 , 76 ], virtual visits may generate missingness of data recorded in the presence of a provider (e.g., blood pressure if the patient does not have access to a sphygmomanometer; see Challenge #3 ), or necessitate a stratified analysis by visit type to assess for effect modification.

Another common approach is to supplement EHR information with external data sources, such as insurance claims data, when available. Unlike a given EHR, claims data are able to capture a patient’s interaction with the health care system across organizations, and additionally includes pharmacy data such as if a prescription was filled or refilled. Often researchers examine a subset of patients eligible for Medicaid/Medicare and compare what is documented in claims with information available in the EHR [ 77 ]. That is, are there additional medications, diagnoses, hospitalizations found in the claims dataset that were not present in the EHR. In a study by Franklin et al., researchers utilized a linked database of Medicare Advantage claims and comprehensive EHR data from a multi-specialty outpatient practice to determine which dataset would be more accurate in predicting medication adherence [ 77 ]. They found that both datasets were comparable in identifying those with poor adherence, though each dataset incorporated different variables.

While validation studies such as those using claims data allow researchers to gain an understanding as to how accurate and complete a given EHR is, this may only be limited to the specific subpopulation examined (i.e. those eligible for Medicaid, or those over 65 years for Medicare). One study examined congruence between EHR of a community health center and Medicaid claims with respect to diabetes [ 78 ]. They found that patients who were older, male, Spanish-speaking, above the federal poverty level, or who had discontinuous insurance were more likely to have services documented in the EHR as compared to Medicaid claims data. Therefore, while claims data may help supplement and validate information in the EHR, on their own they may underestimate care in certain populations.

Research utilizing EHR data has undoubtedly positively impacted the field of public health through its ability to provide large-scale, longitudinal data on a diverse set of patients, and will continue to do so in the future as more epidemiologists take advantage of this data source. EHR data’s ability to capture individuals that traditionally aren’t included in clinical trials, cohort studies, and even claims datasets allows researchers to measure longitudinal outcomes in patients and perhaps change the understanding of potential risk factors.

However, as outlined in this review, there are important caveats to EHR analysis that need to be taken into account; failure to do so may threaten study validity. The representativeness of EHR data depends on the catchment area of the center and corresponding target population. Tools are available to evaluate and remedy these issues, which are critical to study validity as well as extrapolation of study findings. Data availability and interpretation, missing measurements, and missing visits are also key challenges, as EHRs were not specifically developed for research purposes, despite their common use for such. Taking advantage of all available EHR data, whether it be structured or unstructured fields through NLP, will be important in understanding the patient experience and identifying key phenotypes. Beyond methods to address these concerns, it will remain crucial for epidemiologists and data analysts to engage with clinicians and informaticians at their institutions to ensure data quality and accessibility by forming multidisciplinary teams around specific research projects. Lastly, integration across multiple EHRs, or datasets that encompass multi-institutional EHR records, add an additional layer of data quality and validity issues, with the potential to exacerbate the above-stated challenges found within a single EHR. At minimum, such studies should account for correlated errors [ 79 , 80 ], and investigate whether modularization, or submechanisms that determine whether data are observed or missing in each EHR, exist [ 65 ].

The identified challenges may also apply to secondary analysis of other large healthcare databases, such as claims data, although it is important not to conflate the two types of data. EHR data are driven by clinical care and claims data are driven by the reimbursement process where there is a financial incentive to capture diagnoses, procedures, and medications [ 48 ]. The source of data likely influences the availability, accuracy, and completeness of data. The fundamental representation of data may also differ as a record in a claims database corresponds to a “claim” as opposed to an “encounter” in the EHR. As such, the representativeness of the database populations, the sensitivity and specificity of variables, as well as the mechanisms of missingness in claims data may differ from EHR data. One study that evaluated pediatric quality care measures, such as BMI, noted inferior sensitivity based on claims data alone [ 81 ]. Linking claims data to EHR data has been proposed to enhance study validity, but many of the caveats raised in herein still apply [ 82 ].

Although we focused on epidemiological challenges related to study validity, there are other important considerations for researchers working with EHR data. Privacy and security of data as well as institutional review board (IRB) or ethics board oversight of EHR-based studies should not be taken for granted. For researchers in the U.S., Goldstein and Sarwate described Health Insurance Portability and Accountability Act (HIPAA)-compliant approaches to ensure the privacy and security of EHR data used in epidemiological research, and presented emerging approaches to analyses that separate the data from analysis [ 83 ]. The IRB oversees the data collection process for EHR-based research and through the HIPAA Privacy Rule these data typically do not require informed consent provided they are retrospective and reside at the EHR’s institution [ 84 ]. Such research will also likely receive an exempt IRB review provided subjects are non-identifiable.

Conclusions

As EHRs are increasingly being used for research, epidemiologists can take advantage of the many tools and methods that already exist and apply them to the key challenges described above. By being aware of the limitations that the data present and proactively addressing them, EHR studies will be more robust, informative, and important to the understanding of health and disease in the population.

Availability of data and materials

All data and materials used in this review are described herein.

Abbreviations

Body Mass Index

Electronic Health Record

International Classification of Diseases

Institutional review board/ethics board

Health Insurance Portability and Accountability Act

Natural Language Processing

Social Determinants of Health

Socioeconomic Status

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Acknowledgements

The authors thank Dr. Annemarie Hirsch, Department of Population Health Sciences, Geisinger, for assistance in conceptualizing an earlier version of this work.

Research reported in this publication was supported in part by the National Institute of Arthritis and Musculoskeletal and Skin Diseases of the National Institutes of Health under Award Number K01AR075085 (to MAG) and the National Institute Of Allergy And Infectious Diseases of the National Institutes of Health under Award Number K01AI143356 (to NDG). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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Gianfrancesco, M.A., Goldstein, N.D. A narrative review on the validity of electronic health record-based research in epidemiology. BMC Med Res Methodol 21 , 234 (2021). https://doi.org/10.1186/s12874-021-01416-5

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Harnessing Electronic Medical Records in Cardiovascular Clinical Practice and Research

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The use of electronic medical records has rapidly been adopted world-wide, which has resulted in multiple new opportunities for cardiovascular research. These include the following: (1) the development and assessment of clinical decision tools, meant to increase quality of care; (2) harnessing data linkages to examine genetic, epidemiological, and pharmacological associations on an unprecedented scale; and (3) harnessing electronic medical records to facilitate the conduct of cardiovascular clinical trials. While these opportunities promise to revolutionize cardiovascular care and research, enthusiasm should be tempered while further assessment of true clinical utility has been undertaken.

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Gouda, P., Ezekowitz, J. Harnessing Electronic Medical Records in Cardiovascular Clinical Practice and Research. J. of Cardiovasc. Trans. Res. 16 , 546–556 (2023). https://doi.org/10.1007/s12265-022-10313-1

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Research Article

Electronic Medical Records implementation in hospital: An empirical investigation of individual and organizational determinants

Contributed equally to this work with: Anna De Benedictis, Emanuele Lettieri

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Current address: Department of Healthcare Professions, University Hospital Campus Bio-Medico, Rome, Italy

Affiliations Department of Healthcare Professions, University Hospital Campus Bio-Medico, Rome, Italy, Faculty of Medicine & Surgery, University Campus Bio-Medico, Rome, Italy

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Current address: Department of Economics, Management and Industrial Engineering, Politecnico of Milan, Milan, Italy

Affiliation Department of Economics, Management and Industrial Engineering, Politecnico of Milan, Milan, Italy

Roles Conceptualization, Data curation, Methodology, Writing – review & editing

Roles Conceptualization, Methodology, Writing – review & editing

Roles Formal analysis, Investigation, Project administration

Affiliation Department of Healthcare Professions, University Hospital Campus Bio-Medico, Rome, Italy

Roles Conceptualization, Writing – review & editing

• Anna De Benedictis, 
• Emanuele Lettieri, 
• Luca Gastaldi, 
• Cristina Masella, 
• Alessia Urgu, 
• Daniela Tartaglini

• Published: June 4, 2020
• https://doi.org/10.1371/journal.pone.0234108
• Reader Comments

The implementation of hospital-wide Electronic Medical Records (EMRs) is still an unsolved quest for many hospital managers. EMRs have long been considered a key factor for improving healthcare quality and safety, reducing adverse events for patients, decreasing costs, optimizing processes, improving clinical research and obtaining best clinical performances. However, hospitals continue to experience resistance from professionals to accepting EMRs. This study combines institutional and individual factors to explain which determinants can trigger or inhibit the EMRs implementation in hospitals, and which variables managers can exploit to guide professionals’ behaviours. Data have been collected through a survey administered to physicians and nurses in an Italian University Hospital in Rome. A total of 114 high-quality responses had been received. Results show that both, physicians and nurses, expect many benefits from the use of EMRs. In particular, it is believed that the EMRs will have a positive impact on quality, efficiency and effectiveness of care; handover communication between healthcare workers; teaching, tutoring and research activities; greater control of your own business. Moreover, data show an interplay between individual and institutional determinants: normative factors directly affect perceived usefulness (C = 0.30 **), perceived ease of use (C = 0.26 **) and intention to use EMRs (C = 0.33 **), regulative factors affect the intention to use EMRs (C = -0.21 **), and perceived usefulness directly affect the intention to use EMRs (C = 0.33 **). The analysis carried out shows that the key determinants of the intention to use EMRs are the normative ones (peer influence) and the individual ones (perceived usefulness), and that perceived usefulness works also as a mediator between normative factors and intention to use EMRs. Therefore, Management can leverage on power users to motivate, generate and manage change.

Citation: De Benedictis A, Lettieri E, Gastaldi L, Masella C, Urgu A, Tartaglini D (2020) Electronic Medical Records implementation in hospital: An empirical investigation of individual and organizational determinants. PLoS ONE 15(6): e0234108. https://doi.org/10.1371/journal.pone.0234108

Editor: Stefano Triberti, University of Milan, ITALY

Received: September 16, 2019; Accepted: May 19, 2020; Published: June 4, 2020

Copyright: © 2020 De Benedictis et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Healthcare is the most complex and fast-moving industry that exists. New digital technologies are constantly being developed, all with the potential to support the clinical practice by bringing many advantages into the healthcare sector [ 1 ]. Nevertheless, the healthcare industry has lagged behind other sectors in the adoption of Information Technology (IT) in the workplace [ 2 ]. Electronic Medical Records (EMRs) have long been considered a key factor for improving healthcare quality and safety, reducing adverse events for patients, decreasing costs, optimizing processes, improving clinical research and obtaining best clinical performance [e.g., 3 – 5 ]. However, the pace of adoption of EMRs–as other digital technologies–in healthcare continues to lag [ 2 , 6 ], and hospitals continue to experience resistance from professionals to accepting digital technology [ 7 ]. Though many research and development programs exist and venture capital investment has been growing, successful IT projects in healthcare continue to be rare, and a plan to accelerate innovation is needed beginning with a diagnosis of the problem [ 2 ]. Some studies analyzed both individual and organizational factors that affect the acceptance and implementation of technology [ 8 ], but they have generated mixed results [ 9 ]. Indeed, mechanisms that drive the adoption and implementation of IT in hospitals remain unclear. Organizational studies conceive organizations as strongly institutionalized settings in which individual behaviours are influenced by regulations, social norms and cultural systems [ 10 , 11 ]. In contrast, Information Science has mostly adopted user acceptance models, which emphasise individuals’ rational and volitional assessment of the costs and benefits they would attain from the new digital technology [ 11 ].

Hospitals are highly institutionalized and regulated contexts, in terms of regulatory oversight and professional roles, and are operationally and technically complex [ 12 ]. Physicians and nurses have a high level of professionalism and they often affiliate within their specialities via professional training and participation in speciality-focused organizations [ 13 ]. Successful adoption or perceived usefulness of EMRs by others within their specialities may influence hospital professionals’ decisions, particularly if they are uncertain about individual benefits. Nevertheless, the majority of academic research in IT adoption in healthcare focused on the individual level [ 14 ]. The most widely used model to explore issues related to the acceptance of technology is the Technology Acceptance Model (TAM) [ 15 ], which identifies two main antecedents the perceived usefulness and the perceived ease of use of technology. The TAM has been validated in multiple settings [e.g. 16 – 18 ]. In its basic framework, the end user’s attitudes and perceptions regarding the use of new technology determine the user’s behavioural intention to use it. Institutional theory, instead, is based on the assumption that individual behaviours are modelled by regulations, social norms and meaning systems and that institutions embodied in routines rely on automatic cognition and uncritical processing of existing schemata and privilege consistency with stereotypes and speed over accuracy [ 19 ]. Thus, in this theory, normative and cultural conditions are co-determinants of the adoption of new technologies [ 20 ]. The use of institutional theory in Information Science is rare compared to other fields such as organization science [ 21 ]. However, several studies have used an institutional approach for exploring the adoption of technology considering institutional forces as crucial to shaping organizational actions and the opinions of the decision-makers [ 22 , 23 , 24 ].

Both institutional theory and user acceptance models have independently tried to incorporate elements of the other theory to enrich their explanatory power [ 2 ]. User acceptance models have incorporated the direct effects of social influences and organizational conditions on individuals’ behavioural intention [ 25 , 26 ], and institutional studies have demonstrated that even when professionals are subject to institutional influences, their self-determination plays an important role even in highly-institutionalized and regulated settings such as hospitals [ 27 ]. Previous studies about technology acceptance and adoption compared individual and social levels including environmental factors [ 22 , 28 – 30 ], typically based on the diffusion of innovation theory (DOI) [ 31 ] or the TOE (technology, organization, and environment) framework [ 32 ]. Moreover, only a few studies have tested both explanations (institutional and individual) in an integrative framework [ 23 ] to explain the behaviour of organizations.

The main purpose of this study was to explore which are the main determinants of hospital professionals’ intention to use EMRs through a novel theoretical model that combines organizational theories and technology acceptance models. By combining these theories, this study investigated the interplay between organizational and individual factors, thus offering novel insights on the determinants of hospital professionals’ acceptance of digital technology by showing how and to what extent the interplay between individual and organizational determinants might trigger or inhibit the acceptance of digital technology. This study focused on perceived usefulness and perceived ease of use as explanatory factors at the individual level, and on inter-hospital normative and regulative forces as explanatory factors at the organizational level. Intention to use has been preferred to repetitive use as the dependent variable. This choice is because of the still relatively low adoption rate of EMRs in many Countries such as Italy, where this study is located. In the specific case of Italy, a recent report issued by the Politecnico di Milano within the research activities of the Permanent Observatory of Digital Transformation in Health Care [ 33 ] pointed out that only 53% of Italian hospitals have in place an EMRs for therapy management, only 30% of Italian hospitals have in place an EMRs that collects vital parameters and informed consensus, and only 19% of Italian hospitals have in place an EMRs that supports clinical decision-making. In this view, a large number of Italian hospitals–as well as hospitals from other Countries who are still lagging in the adoption of EMRs–is expected to commit in the next years to adopt EMRs and the understanding of which individual or organizational factors might shape hospital professionals’ intention to use EMRs might contribute to the successful adoption and implementation of such and other digital technologies. In this view, the results of this study might be valuable for hospital managers and professionals of different countries who are going to invest in the digital transformation of their hospitals.

Material and methods

Ethics statement.

The study has been approved by the Ethics Board of the University Hospital Campus Bio-Medico of Rome. (Approval number: 61/16 OSS ComEt CBM), and written consent has been obtained by professionals involved in the study.

Theoretical background

To evaluate the potential interplay between individual and institutional variables, a research framework has been created ( Fig 1 ). The framework integrates into a coherent view of two theories that belong to two different bodies of literature:

• The Technology Acceptance Model (TAM), from Information Science, that has been widely used in the last decades in healthcare to understand what leads professionals or patients to accept or reject Information Technology [ 15 ];
• The Institutional Theory, from Public Management, that has been largely adopted in the last decades to assess how institutional factors shape professionals’ behaviours [ 34 – 36 ].

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https://doi.org/10.1371/journal.pone.0234108.g001

Technology acceptance model.

Davis introduced the TAM in 1989 [ 15 ]. The main problem raised by the author was to understand what leads people to accept or reject Information Technology. In this regards, two main variables have been identified: the perceived usefulness and the ease of use. Perceived usefulness measures “the degree to which a person believes that using a particular system would enhance his or her job performance” [ 15 ], and therefore induces individuals to use technology as it allows to obtain better results. On the other hand, the ease of use measures “the degree to which a person believes that using a system would be free of effort” [ 15 ] and induces the potential users to use a certain technology since it requires low energy expenditure while it may bring advantages. The first one induces an individual to use technology as it allows to obtain better results in his work; the ease of use, on the other hand, stimulates potential users to use a certain technology since many advantages are supported with low energy expenditure.

Institutional theory.

The Institutional Theory refers to a line of organizational research that recognize the significant organizational effects that are associated with the increase of cultural and social forces. According to Scott [ 34 – 36 ], “Institutions are made up of cultural-cognitive, normative and regulative elements, which together with associated activities and resources offer stability and meaning to social life.” These three forces are present in totally developed institutional systems, with economists and political scientists emphasizing regulative, sociological and normative factors, and anthropologists and organizational theorists emphasizing cognitive-cultural factors. According to this perspective, individuals are embedded in institutional pillars that limit the scope of their rational assessment and direct the engagement of specific behaviours [ 34 – 36 ]. Scott [ 34 – 36 ] defines the three institutional pillars as follows:

• regulative pillars : which regard the existence of regulations, rules and processes whose breach is monitored and sanctioned;
• normative pillars : which introduce a social dimension of appropriate behaviours in the organization;
• cultural pillars : which emphasize the use of common schemas, frames, and other shared symbolic representations that create an attachment to the ‘appropriate’ behaviour.

Research framework

Consistently to our research questions, we combined the two theories described above to develop an original, comprehensive research framework where individual and institutional determinants have been interlinked to explore their potential interplay in explaining hospital professionals’ intention to use an EMR. Coherently to past researches about user acceptance of new technologies [ 36 , 37 ], we considered age and job seniority as key control variables. Additionally, to narrow the knowledge gap about how hospital professionals belonging to either different profession (e.g., physicians vs. nurses) or different speciality (e.g., cardiology vs. orthopaedics) might be interested to use an EMR, we included clinical speciality and profession as control variables. Fig 1 offers a synoptic view of our research framework, where the independent variable (i.e., the intention to use an EMR) is explained by individual factors from TAM (i.e., perceived usefulness and perceived ease of use) as well as by institutional factors from Institutional Theory (i.e., regulative factors that refer to the degree of adhesion to hospital managers’ goals, and normative factors that explain the peer influence among hospital colleagues. Control variables have been also displayed.

According to the research questions and the research framework, the following research hypotheses (H) were stated: H1: Individual factors (perceived usefulness, perceived ease of use) directly affect the intention to use EMRs; H2: Organizational factors (normative and regulative factors) directly affect individual factors and the intention to use EMRs; H3: Some control variables (age, seniority, clinical specialities and different professions) directly affect individual factors and the intention to use EMRs.

Setting and research methodology

Given the explorative nature of this study, a single case study research design has been adopted. The choice of a single case study offers the opportunity to eliminate potential confounding factors due to the heterogeneity–in terms of strategy, legacy, professionals’ behaviours and technology infrastructure–that different hospitals might show. We selected the Teaching Hospital Campus Bio-Medico (CBM) in Rome (Italy) as an adequate setting for investigating our research questions. This hospital is mid-size (around 300 beds), many-disciplines, teaching and private. Being a teaching hospital, there is more room for divergent goals between professionals and managers, thus creating the correct setting where to investigate the interplay between individual and organizational factors. Being many-discipline, there is room to study the potential conflict among professionals from different disciplines concerning the intention to use EMRs. Finally, being mid-size, CBM is a valid setting to observe the potential divergence between nurses and doctors in the intention to use EMRs. A quantitative study has been performed using a survey administered to hospital professionals (physicians and nurses). The questionnaire has been designed based on the scales identified in the literature and reviewed in detail by the authors. Moreover, a pilot test of the questionnaire has been carried out before the survey. The initial questionnaire comprised 20 items that were reviewed for face validity by a panel of four experts, consisting of one nurse and one physician—with more than 9 years of work experience -, and two engineers with expertise in Information Science. Panel members were asked to evaluate each statement for clarity, ease of use and appropriateness. Based on their comments and suggestions, five items were removed and changes were made in the wording of several items to increase clarity.

This 15-item questionnaire was tested for content validity by 10 experts not involved in the preceding phase to identify its ability to measure the determinants of the intention to use EMRs in hospitals and to identify, for each item, utility, consistency with the research objectives, easy of reply and other important aspects to take into account. Audio-recorded individual interviews using a semi-structured grid were carried out with 10 experts including two nurses, three head nurses, two managers and three physicians. The interviews lasted 60 minutes on average and were conducted in a designated room by three researchers: one acted as the interviewer, the other two helped with audio-recording and with filling out the grid for item evaluation. Based on the expert evaluation, three items were modified.

The questionnaire consists of two main sections: scales and constructs of the proposed model; control variables and characteristics of respondents. Eleven items evaluated individual variables, in particular, the scale for the measurement of perceived usefulness has been adapted from the studies of Venkatesh [ 38 , 39 ]. Organizational variables were explored through 4 items related to normative and regulative factors. The scale for the measurement of normative and regulative factors has been adapted from the study of Scott [ 20 ]. The survey items are available in Annex ( S1 Table ). Additional questions have been designed to gather demographic and sample information. All questionnaire items related to the constructs of the proposed model were explored using a 7 point Likert scale with 1 indicating “strongly disagree” and 7 “strongly agree”. The first re-call has been made one week after the expiration date for compilation. Three days after the first follow-up, the second recall has been sent. Finally, three days after, the third recall has been sent.

The statistical analysis was performed using the software Stata 14.1®. The internal consistency was evaluated through Cronbach’s Alpha coefficients, the path analysis was performed to test the proposed model considering a p-value of <0.05 as significant. The correlation between profession (doctors vs. nurses) and the answers provided for each item was analyzed through the Fisher’s test; a p-value of <0.05 was considered significant.

The study has been approved by the General Management and the Ethics Board of CBM. The link for the online questionnaire was sent by e-mail to 380 nurses and 250 physician representatives of different clinical areas. All questionnaires were filled out anonymously in a period between February and September 2018. The final sample included 114 hospital professionals (response rate 19%), composed by 78 (68%) nurses and 36 (32%) physicians. They were 84 (74%) females and 30 (36%) males, aged 37.4 years on average (range 23–66, SD 9.6), with a mean work experience of 13.24 (range 0.5–41, SD 8.73). The sample of respondents has been compared–in terms of age, gender and clinical experience–to the whole population of doctors and nurses enrolled at CBM confirming the absence of potential response biases related to the non-respondents.

Questionnaire’s internal consistency

The internal consistency of constructs was evaluated through Cronbach's Alpha coefficients, values greater than or equal to 0.7 were considered acceptable. (α ≥ 0.90 were considered excellent; 0.8 ≤ α < 0.9 good; 0.7 ≤ α < 0.8 acceptable; 0.6 ≤ α < 0.7 questionable; 0.5 ≤ α < 0.6 poor; α < 0.5 unacceptable) ( Table 1 ).

https://doi.org/10.1371/journal.pone.0234108.t001

Determinants of current behaviours

Data show that both physicians and nurses expect many benefits from the use of EMRs. In particular, they think EMRs will have a positive impact on relevant factors such as quality, efficiency and effectiveness of care; handover communication among healthcare workers; teaching, tutoring and research activities; greater control of their tasks. Data confirm that perceived usefulness (C = 0.33**) directly affects the intention to use EMRs. Concerning the organizational factors, data prove that there does exist an interplay between them and individual determinants. In fact, normative factors directly affect perceived usefulness (C = 0.30**), perceived ease of use (C = 0.26**) and intention to use EMRs (C = 0.33**). Regulative factors affect the intention to use EMRs, with a negative sign (C = -0.21**). Control variables (i.e., age, seniority, clinical area and profession) have no impact on other variables in our model. Fig 2 offers a graphical representation of our results.

https://doi.org/10.1371/journal.pone.0234108.g002

Moreover, the findings show a significant correlation between being a nurse or a physician and the perceived ease of use and intention to use EMRs. In particular, more nurse than physicians perceive EMRs as easy to use (p = 0.019 for the item “the EMR will be easy to use”) and state that they would like to use it (p = 0.01 for the item “if I had the opportunity I would use the EMR for most of my work’s processes”).

This study sought to better clarify the relationship between organizational and individual determinants of the intention to use EMRs in a hospital setting by nurses and physicians. Previous studies [ 40 – 46 ] have focused mainly on either the barriers or the facilitators that might impact on the implementation of EMRs, but, to the best of authors’ knowledge, it has never been deepened if and how organizational and individual factors do interact and affect jointly hospital professionals’ motivation to use EMRs. Findings confirmed the positive role played by the perceived usefulness as driving individual factor to the intention to use EMRs and shed light on the significant positive role played by the normative (peer influence) factors [ 2 ], both with direct and indirect effects. In this view, hospital managers can leverage on lead peer influence (i.e., innovation champions) to motivate, generate and manage change and generate a virtuous circle inside the hospital to motivate the use of EMRs. The EMRs implementation process should take into account that professionals need proper time to re-establish control over their tasks and processes. The introduction of EMRs in daily clinical practice changes the status quo and, if, on one hand, it allows many new opportunities, on the other hand, it involves changes that can have different effects on hospital professionals also based on their own characteristics, knowledge, skills and work type. In general, this is what happens in the case of effective implementation, while the consequences of poorly managed implementation can be very complex and involve a greater expenditure of time, energy and money to restart the processes at the previous speed and functionality. In this sense, to increase the motivation of users in all phases of the project represent an essential point for effective management of change. This study confirms the importance of involving front-line professionals, as soon as the hospital decides to start the implementation phase to increase their motivation to use EMRs. In fact, as a result of their involvement, professionals will better understand the rationale of this technological shift and their perception of usefulness will increase consequently. Moreover, it is important to consider that, as reported by Gastaldi et al. [ 2 ] in the absence of coercive mechanisms, institutional pressures toward EMR use are primarily normative and/or mimetic [ 2 ].

In the study, the construct “Regulative factor” has been derived from the Institutional theory and is aimed at exploring the pressure that a hospital professional might perceive from the goals set by hospital managers. This pressure is intended to be independent of the specific strategy/initiative and to be a general availability of a hospital professional to align his/her behaviour to the goals set by hospital managers. An example of a question is: “I very much agree with most of the objectives of the management”. The regulative factor should be analyzed together with the construct “Normative factor” that crystallizes the perceived pressure from peers. Hospitals are intended as professional bureaucracies where professionals feel more the pressures from peers rather than from apex managers. What is interesting is that the regulative factor affects negatively the intention to use, meaning that more the general agreement with managers’ goals less the intention to use an EMR. This finding might appear as counter-intuitive and contrary to what has been found in other studies [ 47 ]. This result cannot be explained by the potential misalignment between hospitals managers’ goals and those of physicians and nurses, being the former more focused on the efficiency and the latter on the effectiveness of care delivery. Managers at CBM have proved to be committed to the quality of care and not to efficiency strategies that might reduce the effectiveness of care. This context is quite typical in Italy, where the tensions between “medicine” and “management” are less evident than in other countries, such as in the US. We think that the negative impact of the regulative factors on the perception of usefulness is because hospital managers did not detail enough their goals about the digital transformation of care delivery, thus impacting negatively on hospital professionals’ perception about the usefulness of an EMR. Being these goals enough general–e.g., providing support to research activities and care delivery, promoting efficiency and process redesign–while the linkage between the regulative factors and the perception of usefulness failed to materialize, the linkage between the regulative factors and the intention to use EMRs became negative as hospital professionals lost the connection between EMR usage and managers’ goals. In this view, more contextualized goals about the usage of EMR are expected to positively affect the intention to use it among those professionals who are more willing to be adherent to managers’ goals. This finding should be tested and confirmed by further replication studies that might capture more in detail the relationships between regulative factors and either the perceived usefulness or the intention to use. For instance, it might be valuable to understand whether and how the co-development of hospitals goals between managers and professionals might impact these relationships as well as the specific content of hospital goals (financial vs. quality of care, operative vs. research).

This study offers original insights to further the ongoing debate about the digital transformation of hospitals, with a focus to EMRs. Our results show that there is an interplay between individual and organizational factors in shaping hospital professionals’ intention to use EMRs. The study showed that the main determinants of the intention to use EMRs are the normative ones (peer influence) and the individual ones (perceive usefulness).

From an academic viewpoint, the study offers an original perspective and a new theoretical framework, which combines organizational theories and technology acceptance models to explain hospital professionals’ acceptance of EMRs. In particular, the results confirm the importance of individual variables, not only as directly related to the acceptance of new technology, but also as important mediators between institutional variables and acceptance, thus highlight and confirming the importance of the connections between organizational studies and information science.

Despite the original contributions, this study suffers at least two limitations that should be addressed by future research. First, the research design is based on a single case study. Further research should consider a multi-centre design, thus allowing the generalization of our results. Moreover, a multi-centre study will allow exploring the role that hospital characteristics–in terms of strategy, legacy, etc.–might have on shaping both the organizational and individual factors investigated in this study. Second, this study investigated the intention to use EMRs as the dependent variable. Further research should consider hospitals where EMRs are already mature technologies, thus allowing the investigation of the actual use and which factors might facilitate/inhibit the translation of the intention to use into actual use.

Supporting information

S1 table. questionnaire..

https://doi.org/10.1371/journal.pone.0234108.s001

S2 Table. Perceived usefulness.

https://doi.org/10.1371/journal.pone.0234108.s002

S3 Table. Perceived ease of use.

https://doi.org/10.1371/journal.pone.0234108.s003

S4 Table. Intention to use.

https://doi.org/10.1371/journal.pone.0234108.s004

S5 Table. Normative factors (Peer influence).

https://doi.org/10.1371/journal.pone.0234108.s005

S6 Table. Regulative factors (Adhesion to the management objectives).

https://doi.org/10.1371/journal.pone.0234108.s006

Acknowledgments

We want to thank Dr Federica Segato for her valuable comments in all phases of this study.

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• Research article
• Open access
• Published: 02 March 2020

A systematic review of patient access to medical records in the acute setting: practicalities, perspectives and ethical consequences

• Stephanie N. D’Costa 1 ,
• Isla L. Kuhn 2 &
• Zoë Fritz   ORCID: orcid.org/0000-0001-9403-409X 2  

BMC Medical Ethics volume  21 , Article number:  18 ( 2020 ) Cite this article

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Internationally, patient access to notes is increasing. This has been driven by respect for patient autonomy, often recognised as a primary tenet of medical ethics: patients should be able to access their records to be fully engaged with their care. While research has been conducted on the impact of patient access to outpatient and primary care records and to patient portals, there is no such review looking at access to hospital medical records in real time, nor an ethical analysis of the issues involved in such a change in process.

This study employed a systematic review framework in two stems, to integrate literature identified from two searches: Medline, CINAHL and Scopus databases were conducted, (for (1) hospitalised patients, patient access to records and its effects on communication and trust within the doctor-patient relationship; and (2) patient access to medical records and the ethical implications identified). The qualitative and quantitative results of both searches were integrated and critically analysed.

3954 empirical and 4929 ethical studies were identified; 18 papers representing 16 studies were identified for review (12 empirical and 6 ethical). The review reveals a consensus that our current approach to giving information to patients – almost exclusively verbally – is insufficient; that patient access to notes is a welcome next step for patient-centred care, but that simply allowing full access, without explanation or summary, is also insufficient. Several ethical implications need to be considered: increased information could improve patient trust and knowledge but might transfer an (unwelcome) sense of responsibility to patients; doctors and patients have conflicting views on how much information should be shared and when; sharing written information might increase the already significant disparity in access to health care, and have unforeseen opportunity costs. The impact on medical practice of sharing notes in real time will also need to be evaluated.

Conclusions

The review presents encouraging data to support patient access to medical notes. However, sharing information is a critical part of clinical practice; changing how it is done could have significant empirical and ethical impacts; any changes should be carefully evaluated.

Peer Review reports

It is unusual for patients to request access to their medical hospital records, despite their legal right to do so [ 1 ]. The U.K. government mandated that patients should be able to readily access their electronic medical record by 2018, a promise which has not been fulfilled, mostly due to logistical difficulties [ 2 ]. This mandate was built on respect for patient autonomy as a primary tenet of medical ethics: patients should be able to access their records to be fully engaged with their care. Access to records allows patients to be more informed which may increase opportunities for them to question their care plans and request second opinions.

Internationally, patients are more readily able to access their notes, and there has been evidence of positive outcomes in maternity records [ 3 ]; in primary care [ 4 , 5 ]; for specific diseases, [ 6 , 7 ] and for specific interventions [ 8 , 9 ]. A 2003 (Ross and Lin) [ 10 ] and 2007 (Ferreira et al) [ 11 ] review of the literature in these fields found that patient access was unlikely to cause harm and can improve doctor-patient communication and relations; the latter review also identified the potential for patients to spot and correct mistakes in their records.

More recently, the use of patient ‘portals’ – an electronic route to targeted parts of the medical record – has become more common. Several systematic reviews on the design, use and impact of such portals have been conducted [ 12 , 13 , 14 ]. Patients are generally enthusiastic about the possibility of accessibility, and positive or neutral health outcomes were observed. However, it was noted that clinician contact for portal users increased, and, perhaps related to this, disparity of uptake among different ethnic and socioeconomic groups was noted.

While these reviews demonstrate significant bodies of research on the impact of patient access to outpatient and primary care records and to patient portals (see Table  1 for a summary table of the systematic reviews in these domains), there is no such review looking at access to hospital medical records in real time, nor an ethical analysis of the issues involved in such a change in process.

Real-time access to medical records (particularly as they are currently written) may have unintended consequences on patient care both directly and indirectly – for example, by altering how things are recorded in the notes.

In this paper, we focus on adult access to notes in the medical acute care setting. We define this as the environment which comprises an adult medical patient’s presentation to hospital and their initial (up to 5 days) in-patient stay. This is a busy environment in which a sick patient generally only receives verbal communication, and in which decisions need to be made quickly, often by or with clinicians unfamiliar to the patient. In this context, access to notes may serve a different purpose than in the chronic disease or outpatient setting. Our aim was to review empirical papers relating to patient access and contribution to medical records, and consider the ethical issues raised by this proposed change in practice to fully appreciate the consequences of access to notes in real time.

Our review therefore set out to answer two questions:

1) What studies have there been of sharing records with medical patients in the in-patient setting, and in particular on the impact on trust and communication between patients and doctors?

2) What are the ethical issues associated with sharing records with medical patients?

This study employed a systematic review framework in two stems, to integrate literature identified from two searches surrounding our research questions. This ensured a robust, replicable searching strategy from which we could extract data clearly defined by inclusion and exclusion criteria (an initial attempt to search ethical issues relating to sharing medical records in acute care yielded no relevant results). We conducted critical interpretive synthesis [ 16 ] to the data extracted, an application of qualitative enquiry that allowed us to critically analyse and integrate both the qualitative and quantitative results of both searches into main themes.

The review was registered on the PROSPERO database (registration ID CRD42018114125). PRISMA guidelines have been used to inform the methodology and write up.

Identification of studies

A replicable search strategy was developed to answer our two research questions, using two literature searches, on the Medline via OVID, CINAHL via Ebsco and Scopus databases (See Appendix 1 for the full search strategies for both searches). Searches were run on the 23rd February 2018. Reference lists of included studies were reviewed for additional papers. A complete record of all identified articles was kept on a managed reference database.

Literature search of the empirical data

Search words, phrases and subject headings (including MeSH) were used to search for literature surrounding the topics of (1) hospitalised patients, (2) patient access to records and (3) its effects on communication and trust within the doctor-patient relationship.

The inclusion criteria limited the literature to studies about adult, hospitalised patients in the acute setting. Limits were applied for English language papers published since 1997 were included. Exclusion criteria consisted of paediatric, disease-specific studies and those focussed on confidentiality and data sharing. Studies relating to the design of a system allowing patient access to records were also excluded.

Literature search of the ethical issues

The second search consisted of a range of terms for (1) patient access to records and (2) ethical implications. This search therefore did not specify hospitalised patients or the effect access to notes has on communication and trust and was run from inception to the search date. The exclusion criteria remained the same.

Study selection

For each search, the titles and abstracts of references were screened by one reviewer (either SD or ZF) who selected those appropriate for full text analysis. 100 references in every 1000 were independently screened by both reviewers to assess for concordance and prevent drift, refining the inclusion criteria if needed. Any references where there was ambiguity were discussed by both authors and a decision made. Reference lists of included studies were screened by both authors. The results of the study selection are shown in Fig.  1 .

PRISMA Article Selection Flow chart

Data extraction and risk of bias

SD extracted the following data from the included studies: setting, nature of study, sample size, nature and contribution of participants, nature of analysis and summary of results, shown in Table  1 . Both researchers conducted thematic analysis on the papers, identifying four major themes.

Planned methods of analysis

Meta-analysis was inappropriate for the heterogenous nature of the search results and therefore a critical interpretive synthesis [ 16 ] was undertaken to discover emerging themes from the literature. Analysis of the papers was followed by extraction of data and discussion between the two authors, to consider the themes underlying these results. The ethics literature, which encompassed a wider range of settings than the empirical literature, was examined for themes which would be applicable across health care settings. An iterative process was utilised, examining and grouping them into overarching themes that both organised and illustrated the findings of the review.

Of the 3954 empirical and 4929 ethical studies identified through the two searches, 18 papers representing 16 studies were identified for review (12 empirical and 6 ethical) see Fig.  1 .

Two studies used questionnaires [ 17 , 18 ]; four used interviews or focus groups [ 19 , 20 , 21 , 22 , 23 , 24 ]; two used mixed methods [ 25 , 26 ]. One note analysis, [ 27 ] one portal analysis [ 28 ] and one clinical trial [ 29 , 30 ] was conducted, and six analysis articles were identified [ 31 , 32 , 33 , 34 , 35 , 36 ]. One empirical study came from each of Israel [ 19 ], Norway [ 20 , 21 ] and Canada [ 18 ]; the rest originated from the USA. No papers looked at perspectives of the multidisciplinary team. The data extraction is summarised in Table  2 .

Four main themes emerged on analysis: Impact on patient care; Conflicts between patient and physician perspective; divergent views on doctor and patient roles; cultural differences and societal risks.

Impact on patient care

Sharing notes was seen to empower patients by improving trust and knowledge [ 30 ], facilitating patients to work with doctors [ 28 ]. Communication of written information was considered superior to verbal explanations; one patient was reported as saying “Yeah, they come and update me but..I mean I can’t keep track of it all. That’s why I like this.” [ 24 ] No studies revealed objective changes in care such as reduced length of stay. Access to their own notes might enable patients to correct inaccuracies, [ 21 , 36 ] although this raised the possibility of patients feeling responsible if something was missed: [ 20 , 32 ]

“patients could end up feeling they are to blame for their own poor outcomes.” [ 32 ]

Some participants thought written information might ‘facilitate verbal communication’. [ 26 ] Others were concerned that a written note might supplant face-to-face interaction [ 22 ]; this did not manifest in the only study to trial giving patients a written daily summary [ 17 ].

Conflict between doctor and patient perspectives

Patients and doctors had discordant perceptions of how accessing the medical record might affect care: whilst doctors were concerned access to notes will overwhelm or unnecessarily worry patients, [ 17 , 24 ] patients were reassured by the shared information [ 37 ]. Grossman et al suggested that ‘it may be prudent to omit or explain potentially alarming information that carries a low degree of certainty such as a cancer on a differential diagnosis list” [ 28 ] .

A reoccurring conflict was the release of lab (and other) results in real time – patients strongly supported this whereas doctors preferred a delay, [ 24 , 29 , 31 ] in part so they could interpret them appropriately, offer support and create a future healthcare plan. Without this, some participants theorised that results could be prone to misinterpretation and unnecessary anxiety could be provoked [ 32 ]. As a physician participant said: “one of the primary duties of a physician is not only to alert the patient to abnormal results but also to educate them on their condition and appraise them of the follow up that will be needed” [ 32 ] . If delayed release did exist, however, there was a question about who would take responsibility for this [ 31 ].. Interestingly, this was not mentioned in the papers reporting direct experience.

There was also debate about whether patients should be co-creators of notes: Doctors, again hypothesising, were concerned that patients editing their own record might make them less reliable [ 34 , 35 ].

Divergent views on doctor and patient roles

A range of alternative approaches have been developed to share non-verbal information, and they reveal a variety of implicit perspectives about the role of the patient and the doctor. Tools designed to ensure patient choice and satisfaction are for those who perceive the patient as client ; one participant was quoted as saying: “I would like to be able to see background information [ about my doctor] like where they went to school” [ 25 ]. Providing information in the hope that patients will become more actively involved in their care see the patient as collaborator [ 22 , 29 ]. The different perspectives influence the purpose (and extent) of information sharing.

Cultural difference and societal risks

Different healthcare systems worldwide vary in their approach and concerns regarding access to notes – one study set in Israel found that the doctors more willing to share notes with patients originated from English-speaking countries, suggesting a cultural influence towards this [ 19 ]. In some countries such as the USA and Norway, liability seems to be more of a concern for the doctors and more of a motive for patients to access notes [ 21 , 24 ].

Across geographical boundaries, however, there was a recognition that there would be variation in patients’ willingness and ability to access notes, and that this might lead to disparity in health care, [ 22 , 35 ] with those from lower socioeconomic groups less likely to engage despite an often greater need; ‘{to] what extent should less engaged individuals be punished for their ‘ignorance’ [ 35 ] . As Lyles et al stated : “there is an ethical imperative to work to reduce the potential for the emergence or amplification of health disparities with respect to portal use’ [ 33 ] . Large screens, simple formats and buttons will help accessibility for some [ 26 , 38 ]; empirical research assessing the impact on access to health care or impact on different socioeconomic groups was not identified.

Finally, the questions of privacy and security of patient notes were raised, although papers focussing solely on this issue were excluded from the study. Some patients were concerned about the security of having information on their own devices, [ 26 ] while others did not voice privacy concerns [ 25 ]. Patients need to be able to trust their details are stored and shared securely, so they can contribute to them in a transparent manner [ 35 ].

The review reveals a consensus that our current approach to giving information to patients – almost exclusively verbally – is insufficient; that patient access to notes is a welcome next step for patient-centred care, but that simply allowing full access, without explanation or summary, is also insufficient. Several ethical implications need to be considered: increased information could improve patient trust and knowledge but might transfer an (unwelcome) sense of responsibility to patients; doctors and patients have conflicting views on how much information should be shared and when; sharing written information might increase the already significant disparity in access to health care, and have unforeseen opportunity costs.

It is also clear that we need to consider the impact that sharing notes in real time will have on medical practice.

Trust and the medical record

Although trust, both in doctors individually and generally, is often measured, it is rarely sufficiently specified in the medical literature. Trust is necessary when there is a degree of uncertainty and vulnerability (Becker 1996), both of which are present in the patient-doctor relationship; uncertainty about diagnosis and treatment, vulnerability not only because the patient is physically unwell, but because of the anxiety which often accompanies illness, and which can affect judgment. Trust is often described as a ‘three place relation’: ‘A’ trusts ‘B’ with ‘C’ [ 39 ].

In healthcare, the factors which can determine trust can relate to the patient (‘A’) and the doctor (‘B’), as well as what is entrusted (‘C’), namely the patient’s care [ 40 ]. Since the degree of care required is related to the severity and circumstances of the illness, these are also factors which can affect the patient’s vulnerability and need to trust. While trust is necessary for a functioning patient-doctor relationship, too much trust could be detrimental [ 41 ]. It may lead to reduced patient involvement in decision-making, or fewer questions being asked, leading to the possibility of sub-standard patient care.

What we want to achieve is well-placed patient trust, a concept O’Neil refers to as trust of the trustworthy, [ 42 ] where a patient can be confident that their trust in their clinician is justified and thus can reasonably entrust decisions and actions about their care to him or her. This places an obligation on clinicians to be trustworthy, but it also requires patients to be able to ask questions to satisfy themselves that their trust is well-placed. Providing access to medical records enables patients to determine what they are entrusting (more about what is wrong with them, and more about what treatments and investigations are planned) and enables them to place their trust well (or withhold it). Patients reading their own records might in turn alter physicians’ behaviours to be more trustworthy: they may, as they have done with clinic letters, modulate their language and ensure better verbal communication to avoid misconstruction of what is written.

Increased knowledge, increased responsibility?

While trust is important, the relationship between trust and autonomy has been well explored [ 43 ]. In medical ethics analysis of the last 40 years, autonomy has been given primacy [ 44 , 45 ]; part of respecting patient autonomy is ensuring that they have sufficient information to participate in shared decision-making [ 46 ]. There appears to be a recognition that the current approach – of only relaying verbal information to patients until their discharge – is inadequate. Patients forget, [ 47 ] relatives are concerned, questions are not asked [ 48 ].

It is thus unsurprising that imparting more (or more accessible) information to patients was welcomed by both patients and doctors. However, concerns were expressed that giving more information to patients also transferred responsibility to them: responsibility to check for errors; to deal with uncertainty; to worry about results. This responsibility may not always be desired by the patient. As Alfred Taubert says: “In the so-called co-operative mode, guidance dominates to the point where most patients, realistically and appropriately, want the doctor to take responsibility for their health.” By giving patients increased information, we may be removing their choice to defer responsibility – and associated ‘emotional work’ [ 49 ] or worry - to their physician.

Too much information, too soon?

A specific example of emotional work or worry related to receiving test results in real time: whilst patients expressed a strong desire for this, doctors’ concerns are two-fold. Firstly, they were concerned that patients lack the medical expertise to gauge the clinical importance of results. Secondly, they were worried that they (the doctor) would not be present to offer support and interpretation if the patient receives distressing news. Receiving emotional support from their doctor was a primary reason found for why patients audio-record consultations [ 50 ]; getting results without the doctor present would deprive them of that immediate support. Outside the acute setting, Milliat-Guittard showed that 21% of breast cancer patients did not want to hold records; they did not want to come across a comment that they were not expecting. Instead, they wished to come to terms with the disease in their own way [ 51 ].

Unintended worsening of inequality

Some interventions unintentionally increase inequalities by disproportionately benefiting less disadvantaged groups [ 52 ]. Giving patients access to records might be one such intervention: clinical teams acknowledged that they were working in a stretched system - an intervention which could divert resources to those who could read and understand their medical notes (or who had the confidence to ask questions) might lead to disparities. Awareness of this, and establishing and testing ways to mitigate this risk would be an important element to consider when introducing shared medical records.

Impact on medical practice

Medical records are not only a patient narrative – of their presentation, their investigations and their progress - but a working medical document which reflects dynamic thinking, [ 53 ] consultations, and acts as a tool for handover and for training [ 54 ]. If doctors do not reflect concerns clearly in the notes for fear of worrying the patient, handover could be compromised, impacting negatively on the patient’s care and training of future doctors.

Strengths and limitations

This review synthesised a wide range of papers from medical, nursing and ethical literatures, and was rigorously conducted. However, it identified only papers written in western cultures, and in English, and the conclusions made here should not be extrapolated to other environments. In addition, 7/10 of the studies were carried out in the USA, where the patient doctor relationship also includes a transactional component – doctors need to ensure that patients know what they are paying for. In other health systems represented in these studies (Canada, Norway, Israel) this is not the case, and so the motivations and repercussions of information sharing may be different.

Conclusions and future directions

These studies - and the timing of their publication - reveal that there is significant growth in the approach of sharing more medical information with patients, and significant variation in the type and quantity of information which is being shared. Empirical work with integrated ethical analysis is needed examining the impact of sharing medical records on patient-doctor and multi-disciplinary team communication, on patient trust, on physician training and on resources. The overarching question is what changes will occur to the role of doctor and patient as a result of routinely sharing more information, and, normatively, if there is a “right” amount of information to share with patients in the hospital setting.

Sharing information is a critical part of clinical practice; changing how it is done could have significant empirical and ethical impacts. This review has highlighted what those potential impacts might be. We recommend that careful evaluation of what is recorded and what care is given – both at individual and societal levels – need to be conducted when changes are made to how information is shared.

Availability of data and materials

There are no further data to present other than that which is presented here.

Abbreviations

Cumulative index of nursing and allied health literature

Elton B Stephens Company (A database search database)

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Acknowledgments

We would like to thank Anne-Marie Slowther for helpful conversations which guided us during this research.

Zoe Fritz is funded by the Wellcome trust Grant reference numbers: 208213/Z/17/Z, and WT100577MA. The funding body had no role in the design of the study, collection, analysis, interpretation of data, or of the writing the manuscript.

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D’Costa, S.N., Kuhn, I.L. & Fritz, Z. A systematic review of patient access to medical records in the acute setting: practicalities, perspectives and ethical consequences. BMC Med Ethics 21 , 18 (2020). https://doi.org/10.1186/s12910-020-0459-6

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• Published: 13 July 2023

What prevents us from reusing medical real-world data in research

• Julia Gehrmann   ORCID: orcid.org/0000-0002-4101-5458 1 ,
• Edit Herczog   ORCID: orcid.org/0000-0002-2930-5401 2 ,
• Stefan Decker   ORCID: orcid.org/0000-0001-6324-7164 3 , 4 &
• Oya Beyan   ORCID: orcid.org/0000-0001-7611-3501 1 , 4  

Scientific Data volume  10 , Article number:  459 ( 2023 ) Cite this article

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Medical real-world data stored in clinical systems represents a valuable knowledge source for medical research, but its usage is still challenged by various technical and cultural aspects. Analyzing these challenges and suggesting measures for future improvement are crucial to improve the situation. This comment paper represents such an analysis from the perspective of research.

Introduction

Recent studies show that Medical Data Science (MDS) carries great potential to improve healthcare 1 , 2 , 3 . Thereby, considering data from several medical areas and of different types, i.e. using multimodal data, significantly increases the quality of the research results 4 , 5 . On the other hand, the inclusion of more features in an MDS analysis means that more medical cases are required to represent the full range of possible feature combinations in a quantity that would be sufficient for a meaningful analysis. Historically, data acquisition in medical research applies prospective data collection, e.g. in clinical studies. However, prospectively collecting the amount of data needed for advanced multimodal data analyses is not feasible for two reasons. Firstly, such a data collection process would cost an enormous amount of money. Secondly, it would take decades to generate enough data for longitudinal analyses, while the results are needed now. A worthwhile alternative is using real-world data (RWD) from clinical systems of e.g. university hospitals. This data is immediately accessible in large quantities, providing full flexibility in the choice of the analyzed research questions 6 , 7 . However, when compared to prospectively curated data, medical RWD usually lacks quality due to the specificities of medical RWD outlined in section 2. The reduced quality makes its preparation for analysis more challenging. Table  1 summarizes the advantages and disadvantages of both data curation strategies.

Considering all the above-mentioned aspects, secondary use of RWD is a great opportunity to immediately enable comprehensive and meaningful MDS analyses. These, in turn, promise increased clinical process efficiency, higher patient safety, performant clinical decision support systems, personalized care and improved healthcare system sustainability 1 . Yet MDS reusing RWD is still not established in practice for various reasons 2 . One such reason is the lack of standardized data curation frameworks specifying how to access and combine multimodal clinical data from operational clinical systems 8 , 9 . To maximize the usability of medical RWD for research, such a framework should support data management according to the “FAIR” paradigm, which states that properly managed data should be discoverable, accessible, interoperable, and reusable (FAIR). These are high-level principles, i.e., they do not specify a specific technology, method, or standard, but rather serve as guidance 10 . The extent to which a data set fulfills the four principles is known as its FAIRness. The process of increasing the FAIRness of data is referred to as FAIRification 11 .

To support the scientific reuse of medical RWD with maximal FAIRness, the German Medical Informatics Initiative (MI-I) established Data Integration Centers (DIC) and Medical Data Integration Centers (MeDIC) at German University Hospitals 12 , 13 , 14 , 15 , 16 . The challenges encountered at MeDIC Cologne have compelled us to write this comment paper, which aims to address key issues surrounding the reuse of medical real-world data (RWD) in research. In addition to the technical challenges extensively discussed in existing literature, we also delve into the cultural aspects and uncertainties that scientists, patients, and governing entities confront when reusing medical RWD. As part of our contribution, we propose high-level measures to enhance the reusability of medical RWD for research purposes. Finally, we evaluate the current usability of medical RWD in terms of the FAIR principles. Our insights draw upon personal experiences, as well as relevant findings from recent English and German literature (2016–2022) obtained through Google Scholar. However, it is important to note that the challenges and measures presented in this paper primarily reflect our personal perspectives and may not encompass all possible aspects.

Specificities of medical real-world data

The main difference between medical data and other scientific data is its high level of intrinsic sensitivity requiring thorough preservation of privacy 17 . Medical data can contain a variety of information, including demographics, healthcare provider notes, radiological findings, results of laboratory or genetic tests, presence or absence of biomarkers, administrative information, case summaries for clinical registries, biometric information, patient-reported information and recordings from medical devices or wearable sensors 18 , 19 . This variety is also reflected in the data formats available that range from tabular, time series and natural language data to images and videos 20 . Issues that are typically attributed to the secondary use of medical RWD are their low volume, i.e. small data set sizes, their high sparsity and their tendency towards poor quality 21 . These issues result from the inherent heterogeneity of treatments, outcomes, study design, analytical methods, and approaches for collecting, processing and interpreting data in the medical field 19 . Thus, the availability and quality of features for a patient strongly depend on the conditions present, the treating or examining department, comorbidity as well as current or previous examination results.

Reusing medical real-world data for medical data science

The main tasks in facilitating, or even enabling, the reuse of medical RWD in a research context are to promote interoperability, harmonization, data quality, and ensure privacy, to optimize the retrieval and management of patient consent, and to establish rules for data use and access 12 , 13 . These measures aim to address the various challenges of scientifically reusing routine clinical data described below.

Challenges in balancing benefits and harms

Personal, i.e. non-anonymized medical data, is inherently sensitive 1 , 17 , 22 . As a result, uncertainties in MDS project preparation and execution arise for all roles involved in performing research on medical RWD, i.e. for patients, researchers and governing entities. The patients may lack trust in research using their personal data. Concerns about data misuse, becoming completely transparent and data leakage - especially in the case of long-term storage - can result in the patients overprotecting their own data and not giving their consent for its reuse in research 23 , 24 , 25 . On the other hand, it has also been shown that most EU citizens support secondary use of medical data if it serves further common good 24 . So, convincing patients about the social expediency of MDS can decrease their ambivalence and avoid overprotection. This can be achieved, for example, by reporting on MDS success stories 13 . A second important aspect is patient empowerment by informing patients about the processing and use of their data through open scientific communication and enabling their active engagement in the form of a dynamic consent management 12 , 23 .

However, there are also concerns on the part of the researcher resulting e.g. from a lack of explicit training in a complex landscape of ethical and legal requirements. These could be mitigated by discussions in interdisciplinary team meetings but differences in the daily work routine make it difficult to arrange such meetings 8 , 9 , 18 , 21 . As a consequence of unresolved concerns, researchers could delay or even cancel their MDS projects. Moreover, even governing entities such as data protection officers and ethics committees exhibit a certain level of uncertainty regarding permissible practices in MDS. They tend to overprotect the rights of the patients whose medical data is to be used while underestimating the necessity of reusing medical RWD for research purposes 9 , 23 , 26 , 27 . This leads to restrictive policies hindering scientific progress.

In general, education is a promising approach to address the uncertainties mentioned above. Technical training for medical researchers and governing entities as well as ethical and legal training for technical experts can increase confidence in project-related decision making 1 , 18 , 23 , 24 , 27 , 28 . The same effect can be achieved by developing MDS guidelines and actionable data protection concepts (DPC) 13 , 14 , 15 , 16 . A good example is the DPC of the MI-I that was developed in collaboration with the German working group of medical ethics committees (AK-EK) 12 . Figure  1 summarizes the sources and consequences of the aforementioned uncertainties that lead to significant challenges in the reuse of medical RWD. Each source of uncertainty is associated with the roles it affects and possible measures to mitigate its impact. The challenges posed by these uncertainties are discussed in more detail below.

Sources and consequences of uncertainties that lead to significant challenges in the reuse of medical RWD. The sources of uncertainties are individually assigned to the roles they affect and possible measures to counteract them.

Uncertainties due to the legal framework

As mentioned above, the complex legal landscape resulting from various intervening laws contributes significantly to the uncertainty surrounding the reuse of medical RWD. At the European level, the General Data Protection Regulation (GDPR) holds substantial influence over the legal framework. In general, it prohibits the processing of health-related personal data (GDPR Art. 9 (1)) unless the informed consent of every affected person is given (GDPR Art. 9 (2a)) or a scientific exemption is present (GDPR Art. 9 (2j)). The latter is the case if the processing is in the public interest, secured by data protection measures, and adequately justified by a sufficient scientific goal. However, substantiating the presence of such a scientific exemption poses significant challenges 29 , 30 . Similarly, or even more difficult, is obtaining informed consent of patients after they have left the clinics. As such, both GDPR-based possibilities to justify the secondary use of RWD in research are difficult to implement in practice 26 , 29 . If the processing is legally based on the scientific exemption, GDPR Art. 89 further mandates the implementation of appropriate privacy safeguards supported by technical and organizational measures. Additionally, it stipulates that only the data necessary for the project should be utilized (principle of data minimization) 30 , 31 . This ensures the protection of sensitive personal data, but also introduces further challenges for the researchers.

The situation becomes further complicated due to the GDPR allowing for various interpretations by the data protection laws of EU member states 30 , 31 . Moreover, there are country-specific regulations, such as job-specific laws, that impact the legal framework of MDS 31 . This complex scenario poses particular challenges for international MDS projects 29 . As a result, identifying the correct legal basis and implementing appropriate data protection measures becomes exceptionally difficult 29 , 30 . This task, crucial in the preparation of clinical data set compilation, necessitates not only technical and medical expertise but also a comprehensive understanding of legal aspects. Thus, a well-functioning interdisciplinary team or researchers with broad training are essential.

Analyses of the current legal framework for data-driven medical research suggest that this framework is remote from practice and thus inhibits scientific progress 31 , 32 . To address these limitations, certain legal amendments or substantial infrastructure enhancements are necessary. Particularly, the infrastructure should focus on incorporating components and tools that facilitate semi-automated data integration and data anonymization. Although the current legal framework permits physicians to access, integrate, and anonymize data from their own patients, they often lack the technical expertise and time to effectively carry out these tasks. By implementing an infrastructure that enables semi-automated data integration and anonymization, researchers would be able to legally utilize valuable medical RWD without imposing additional workload on physicians 29 , 30 . Attaining a fully automated solution is not feasible since effective data integration and anonymization, leading to meaningful data sets, necessitate manual parameter selection by a domain expert. Nonetheless, by prioritizing maximal automation and specifically assigning domain experts to handle the manual steps in the process, rapid and compliant access to medical RWD, along with reduced uncertainties for researchers, can be achieved.

Ethical considerations and overprotectiveness

Not only the legal framework, but also ethical considerations can cause uncertainties. These can affect the patients and researchers but, in the context of an MDS project, especially the ethics committees as they have to judge whether a project is ethically justifiable. There are a variety of ethical principles to be taken into account for such a decision. These principles encompass patient privacy, data ownership, individual autonomy, confidentiality, necessity of data processing, non-maleficence and beneficence 1 , 33 . Considered jointly, they result in a trade-off to be made between the preservation of ethical rights of treated patients and the beneficence of the scientific project 15 , 18 , 26 . Criticism often arises concerning the prevailing trade-off in favor of patients’ privacy, where ethics committees tend to overprotect patient data 23 , 27 . What is frequently overlooked is the ethical responsibility to share and reuse medical RWD to advance medical progress in diagnoses and treatment. Thus, a consequence of overprotecting data is suboptimal patient care which is, in turn, unethical 1 , 9 , 26 . Measures to prevent overprotection are increasing the awareness of its risks through education, as well as the development of clear ethical regulations and guidelines 28 . To facilitate the latter, the data set compilation process for medical RWD should be simplified, e.g. by standardization of processes and data formats because its current complexity challenges the creation of regulations and guidelines 17 .

Uncertainties in project planning

Many of the mentioned concerns related to legal and ethical requirements occur during project planning and design. Here a variety of decisions are made regarding the composition of the RWD set and its processing. These affect all subsequent project steps, but must be determined at an early stage if the project framework necessitates approvals from governing entities. This is because the governing entities require all planned processing steps to be documented in a study plan, serving as the foundation for their decision-making process. This results in long project planning phases due to uncertainties in a complex multi-player environment 13 , 14 , 15 , 16 , 21 . Additionally, creating a strict study plan usually works for clinical trials, but in data science, meaningful results often require more flexibility. For instance, it might be necessary to redesign the project plan throughout data processing. Therefore, project frameworks that show researchers how to reshape their project in specific cases would be much better suited for secondary use of medical RWD 25 , 34 . Taking it a step further, a general guideline or regulation on how to conduct MDS projects would decrease planning time and the risk of errors, both of which are higher if each project is designed individually 14 . To already now minimize the uncertainties in project planning and, thereby, the duration of the planning phase, research teams should communicate intensely and collaboratively plan their tasks 9 , 18 . Since this is a challenging task in a highly interdisciplinary environment, early definition of structures, binding deadlines, and clear assignment of responsibilities, such as designating a person responsible for timely data provision in each department, are crucial 8 , 14 .

The role of the patient consent

As mentioned in the introduction to this section 3.1, dynamical consent management allowing the patients to effectively give and withdraw their consent at any point in time is a crucial measure to foster patient empowerment. As a result, it also leads to more acceptance of MDS by the affected individuals. Furthermore, in section 3.1.1 the informed patient consent was mentioned as a possible legal justification for processing personal sensitive data. However, the traditional informed consent requires patients to explicitly consent to the specific processing of their data. This means their consent is tied to a specific project 35 , 36 . For retrospective projects such a consent cannot be obtained during the patients’ stay at the hospital because the project idea does not exist at that time. Hence, the researcher would have to retrospectively contact all patients whose data is needed for the project, describe the project objective and methodology to them and then ask for their consent. This requires great effort, is, itself, questionable in terms of data protection and even not feasible if the patients are deceased. Making clinical data truly reusable in a research context, therefore, requires a broad consent in which the patients generally agree to the secondary use of their data in ethically approved research contexts. Furthermore, the retrieval of such a broad consent must be integrated into daily clinical routine and the consent management needs to be digitized. Otherwise, the information about the patient consent status might not be easily retrievable for the researcher 8 , 18 , 21 , 37 .

Previous research has documented that most patients are willing to share their data and even perceive sharing their medical data as a common duty 38 . Therefore, it is highly likely that extensively introducing a broad consent such as the one developed by the MI-I in Germany into clinical practice, combined with a fully digital and dynamic consent management, would have a significant positive impact on the feasibility of MDS projects 39 . It would allow patients to actively determine which future research projects may use their data.

Technical challenges

When describing the challenges resulting from balancing benefits and harms in MDS projects, some measures were suggested that require technical solutions. One example for this is the implementation of data protection measures like data access control, safe data transfer, encryption, or de-identification 20 . However, there are not only technical solutions but also challenges, as shown in Fig.  2 .

Technical challenges of curating medical RWD sets and possible measures for improvement.

One category of technical challenges results from the specificities of medical data outlined in section 2. Medical RWD is characterized by a higher level of heterogeneity regarding data types and feature availability than data from any other scientific field 18 , 19 , 26 . Thus, compiling usable medical data sets from RWD requires the technical capabilities of skillful data integration, type conversion and data imputation. However, heterogeneity is not restricted to data formats. A common problem is differences in the primary purpose of data acquisition or primary care leading to different data formats and standards being used 8 . This results in different physicians, clinical departments, or clinical sites not necessarily using the same data scales or units, syntax, data models, ontology, or terminology. Hence, it is difficult to decide which standards to use in an MDS project. A subsequent challenge arising from this lack of interoperability is the conversion between standards that potentially leads to information loss 19 , 26 , 40 . Last but not least, heterogeneity is also reflected in different identifiers being used in different sites. This challenges the linkage of related medical records, which may even become impossible once the data is de-identified 41 . Promising and important measures to meet the challenges concerning heterogeneity are the development, standardization, harmonization and, eventually, deployment of conceptual frameworks, data models, formats, terminologies, and interfaces 8 , 13 , 14 , 16 , 42 . An example illustrating the feasibility and effectiveness of these measures is the widely used DICOM standard for Picture Archiving and Communications systems (PACS) 18 . Similar effects are expected from the deployment of the HL7 FHIR standard for general healthcare related data that is currently being developed 43 . However, besides appreciating the benefits of new approaches, the potential of already existing standards like the SNOMED CT terminology should not be neglected. It still has limitations, such as its complexity challenging the identification of respectively fitting codes and its incompleteness partly requiring to add own codes. On the other hand, SNOMED CT is already very comprehensive. Once its practical applicability is improved, SNOMED CT could be introduced as an obligatory standard in medical data systems fostering interoperability 13 , 16 , 42 .

Another significant technical challenge is the fact that a majority of medical RWD is typically available in a semi-structured or unstructured format, while the application of most machine learning algorithms necessitates structured data 8 , 19 , 42 , 44 . Primary care documentation often relies on free text fields or letters because they can capture all real-world contingencies while structured and standardized data models cannot. Additionally documenting the cases in a structured way, is too time-consuming for clinical practice. So, the primary clinical systems mainly contain semi-structured or unstructured RWD 7 , 13 , 23 . To increase the amount of available structured data, automated data structuring using Natural Language Processing (NLP) is a possible solution. However, it is not easy to implement for various reasons. Among them are the already mentioned inconsistent application of terms and abbreviations in medical texts and the requirement to manually structure some free text data sets to get annotated training data 13 , 42 .

Workflows in primary care settings not only lead to predominantly semi-structured or unstructured documentation of medical cases, but also greatly influence the design of clinical data management systems. In primary care and administrative contexts, such as accounting, clinical staff typically need a comprehensive overview of all data pertaining to an individual patient or case. As a result, clinical data management systems have been developed with a case- or patient-centric design that presents data in a transaction-oriented manner. However, this design is at odds with the need for query-driven extract-transform-load (ETL) processes when accessing data for MDS projects. These projects typically require only a subset of the available data features, but for a group of patients 8 , 26 . Developing a functional ETL pipeline is further complicated by the overall lack of accessible interfaces to the data management systems and the fragmented distribution of data across various clinical departments’ systems 8 , 13 .

This means the design of primary clinical systems could be improved significantly if it allowed for more flexibility, i.e. support patient- and case-centricity for primary care as well as data-centricity for secondary use. Moreover, the system design should comply with data specifications and developed standards rather than requiring the data to be created according to system specifications 13 . However, a complete redesign of primary clinical systems is most likely not feasible. An alternative solution is creating clinical data repositories in the form of data lakes or data warehouses that extract and transform medical RWD from primary systems and make it usable for research 45 , 46 . In this context, the use of standardized platforms and frameworks such as OMOP or i2b2 further increases the interoperability of the collected data 47 . In Germany, the MI-I established DIC and MeDIC whose goal is the creation of such data repositories for the medical RWD gathered at German university hospitals. As a common standard they agreed on the HL7 FHIR based MI-I core data set (CDS) 48 . Because this is work in progress and the data repositories are populated with data from primary clinical systems, the DIC and MeDIC still need to address the challenges identified in this comment paper to create FAIR data repositories for research.

Can we enable practical and FAIR research on medical real-world data?

The previous section has shown that compiling medical RWD sets for research carries several cultural and technical challenges. We can see that classical medical research and data science on RWD have not yet reached agreement. At university hospitals, there is still a clear focus on primary care and traditional clinical trials that is at odds with the demands of data science. Besides the technical and regulatory conflicts, there is the conflict between the principle of data minimization in medical research contradicting the explorative big data approach of data science. Thus, it should be assessed by governing entities whether the beneficence of explorative big data outweighs the ethical benefits of data minimization.

Another important measure to enable FAIR MDS is to offer data systems, e.g. data repositories, meeting the needs of data scientists. These systems should enable comprehensive query-driven data exports and increase interoperability by using shared coding systems and terminologies. To simultaneously foster compliance to legal and ethical requirements, the systems should follow the paradigm of Privacy by Design, i.e. enforcing data protection e.g. by authorization, authentication and only allowing de-identified data to be exported. A resulting positive effect would be a decrease in uncertainties for the researchers since they would have to deal with fewer concerns about data protection and security. As long as the data infrastructure does not follow Privacy by Design, the uncertainties about the secondary use of routine clinical data remain for researchers, e.g. when determining the correct legal basis for the processing of medical RWD or designing the project aiming for ethical compliance. A possible measure to decrease these uncertainties is the simplification of project approval processes, e.g. by only requiring a single project application to be sent to an interdisciplinary deciding committee covering ethics, data security and data protection. Further simplification could be achieved by requesting flexible project frameworks rather than strict project plans from the researchers in the design phase. On the part of patients and governing entities, uncertainty regarding the justification of an MDS analysis often manifests itself in the form of overprotection. Section 3.1 described that an important measure to mitigate all such concerns is offering trainings for researchers, governing entities and patients. Moreover, enhanced patient engagement in form of open science communication and dynamic consent management could further decrease the ambivalence of patients. Secondly, a digital and dynamic consent management would increase the availability and reliability of the information whether a patient currently consents to the secondary usage of their data.

Considering FAIRness as the gold standard for scientific usability of data, the current usability level of medical RWD for MDS can be improved significantly:

Findability : The data system infrastructure at university hospitals is so fragmented that most data features are only findable with intense communication or experience, either from previous projects or clinical routine. Systematic investigation on available features in the individual data systems and the creation of data repositories as carried out by the DIC and MeDIC of the MI-I could help to increase findability.

Accessibility : The access to medical data is currently complicated by uncertainties regarding privacy protection, complex ethico-legal requirements and the design of primary clinical systems lacking query orientation and accessible interfaces. Redesigning the systems or creating data repositories aiming for Privacy by Design and technical accessibility of clinical data would significantly ease the compilation of medical RWD sets for research.

Interoperability : The interoperability is currently mainly restricted to the usage of the same patient identifiers within a hospital. Different departments often use different documentation policies, abbreviations, units, or own case IDs while different hospitals use different patient identifiers. Standardization as an agreement on common terminology, data models and coding systems would help to increase interoperability.

Reusability : Given the current legal situation, true reusability is only achievable with anonymized data sets or a broad patient consent allowing the processing of patient data in ethically approved MDS projects. Otherwise, data sets are compiled and used on a project-specific basis. Once the legal basis for creating a reusable data set is established and implemented, metadata documenting data provenance should be created to further promote reusability.

To conclude, reusing medical RWD in MDS is not infeasible, but the current situation still poses a variety of challenges. This comment paper has outlined these challenges from the research perspective with a special focus on the situation in Germany and proposed high-level measures on how to effectively address them. Implementing these measures will itself be a big challenge but significantly increase the usability of medical RWD for MDS and hence promote improvements in future healthcare. Thereby the technical changes will be easier to implement than the cultural ones.

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Gehrmann, J., Herczog, E., Decker, S. et al. What prevents us from reusing medical real-world data in research. Sci Data 10 , 459 (2023). https://doi.org/10.1038/s41597-023-02361-2

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The influence of electronic health record use on collaboration among medical specialties

• Janita F. J. Vos 1 ,
• Albert Boonstra   ORCID: orcid.org/0000-0002-3565-0623 1 ,
• Arjen Kooistra 2 ,
• Marc Seelen 3 &
• Marjolein van Offenbeek 1  

BMC Health Services Research volume  20 , Article number:  676 ( 2020 ) Cite this article

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One of the main objectives of Electronic Health Records (EHRs) is to enhance collaboration among healthcare professionals. However, our knowledge of how EHRs actually affect collaborative practices is limited. This study examines how an EHR facilitates and constrains collaboration in five outpatient clinics.

We conducted an embedded case study at five outpatient clinics of a Dutch hospital that had implemented an organization-wide EHR. Data were collected through interviews with representatives of medical specialties, administration, nursing, and management. Documents were analyzed to contextualize these data. We examined the following collaborative affordances of EHRs: (1) portability, (2) co-located access, (3) shared overviews, (4) mutual awareness, (5) messaging, and (6) orchestrating.

Our findings demonstrate how an EHR will both facilitate and constrain collaboration among specialties and disciplines. Affordances that were inscribed in the system for collaboration purposes were not fully actualized in the hospital because:

(a) The EHR helps health professionals coordinate patient care on an informed basis at any time and in any place but only allows asynchronous patient record use.

(b) The comprehensive patient file affords joint clinical decision-making based on shared data, but specialty- and discipline-specific user-interfaces constrain mutual understanding of that data. Moreover, not all relevant information can be easily shared across specialties and outside the hospital.

(c) The reduced necessity for face-to-face communication saves time but is experienced as hindering collective responsibility for a smooth workflow.

(d) The EHR affords registration at the source and registration of activities through orders, but the heightened administrative burden for physicians and the strict authorization rules on inputting data constrain the flexible, multidisciplinary collaboration.

(e) While the EHR affords a complete overview, information overload occurs due to the parallel generation of individually owned notes and the high frequency of asynchronous communication through messages of varying clinical priority.

Conclusions

For the optimal actualization of EHRs’ collaborative affordances in hospitals, coordinated use of these affordances by health professionals is a prerequisite. Such coordinated use requires organizational, technical, and behavioral adaptations. Suggestions for hospital-wide policies to enhance trust in both the EHR and in its coordinated use for effective collaboration are offered.

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Collaboration among health professionals of various disciplines is considered a key factor in achieving high quality patient care [ 1 , 2 ]. The symptoms of many of today’s patients, especially those with chronic diseases, are complex, and often require health professionals from different medical specialties to collaborate [ 3 ]. To collaborate effectively, it is necessary to share knowledge and skills, integrate information, and work as a cohesive health care team often while being in different locations [ 3 , 4 ].

Electronic Health Records (EHRs) are implemented for various reasons, including to support coordination, collaboration, and shared decision- making, and are considered as a major means to deliver high-value care [ 5 , 6 , 7 , 8 ]. Physicians who have access to a patient’s health data through EHRs are able to immediately review the patient’s medical history, lab results, and other relevant information. However, EHRs have also been identified as constraining medical work [ 9 ], including collaboration [ 10 , 11 , 12 , 13 ]. Studies have addressed unanticipated problems such as alert fatigue [ 14 ], paper persistence [ 15 ], workflow mismatches [ 16 ] and time consuming system demands [ 17 , 18 , 19 ] resulting in reduced face-to-face patient care [ 20 ] making EHR-enabled collaboration troublesome and highly context dependent [ 21 ]. Given this situation, we are interested in how healthcare professionals interact and communicate, and eventually collaborate or are constrained in their collaboration as a result of the affordances offered by an EHR [ 11 ].

Affordances are potentials for behaviors that arise from the relationship between an artefact (here, an EHR) and goal-oriented actors (here, medical specialists) to achieve specific outcomes such as multidisciplinary collaboration [ 7 ]. In this paper, we label the affordances that are specifically inscribed in an EHR to facilitate or constrain collaboration as collaborative affordances. Recently, Bardram and Houben [ 22 ] demonstrated how EHRs provide the following four affordances for collaboration: Portability, Co-located Access, Shared Overview and Mutual Awareness.

Drawing on data from five multidisciplinary outpatient clinics of a Dutch hospital that had implemented a comprehensive EHR, this study examines how the collaborative affordances of an EHR facilitate or constrain actual collaboration.

Collaboration in healthcare

The relevance of collaboration in healthcare is growing, and visions for more collaborative care are evident in both academic literature [ 2 , 23 , 24 ] and current practice [ 25 ]. We adopt the following definition of collaboration: “a complex phenomenon that brings together two or more individuals, often from different professional disciplines, who work to achieve shared aims and objectives” ([ 26 ], p.41].

Distinctions can be made in the degree of collaboration. First, multidisciplinary collaboration refers to healthcare professionals using the “skills and experience of individuals from different disciplines, with each discipline approaching the patient from their own perspective” ([ 27 ], p.1). Here, the disciplines work independently on discipline-specific care plans that are implemented together, but are not yet integrated into a single approach. Second, interdisciplinary collaboration integrates distinctive disciplinary approaches into a single consultation [ 27 ]. Healthcare professionals who work in an interdisciplinary way build on each other’s expertise and skills to obtain mutually defined goals [ 23 ]. However, how and how effectively healthcare professionals collaborate is influenced by their work context. Clear goals and rules, respect and trust between actors, clear organizational structures, and organizational support may all help effective collaboration [ 28 ].

Evidence shows that healthcare professionals can have divergent goals [ 29 , 30 ]. Divergent goals in using an EHR can lead to non-aligned use patterns between collaborating professionals. Further, the established communication patterns within clinical departments can also result in divergent use of a hospital-wide EHR [ 31 ]. Moreover, some care professionals and departments may use an EHR in unstructured ways, and this could decrease the documentation quality [ 11 ]. A possible consequence of a lowered documentation quality could be reduced trust in the system, and this could constrain EHR-enabled collaboration among healthcare professionals from different departments. While an EHR can facilitate multidisciplinary collaboration during ward rounds, this depends on certain wider design issues being addressed, such as the social ergonomics of the devices involved, inclusion of paper records, and support in improving the technical system [ 32 ]. Data quality and accessibility issues have been found to threaten the EHR’s usefulness for multidisciplinary relationship-building, communicating, coordinating, and collaborative decision-making [ 10 ]. These authors argued that a multiplicity of communication channels, including the EHR-induced ones, may actually inhibit collaboration. Indeed, in another study, the introduction of an EHR increased documentation variability and limited collaboration between clinicians [ 33 ]. The complexity of healthcare practice, and demands for flexibility, may actually require EHRs that go beyond passive information storage and offer stronger support for collaboration [ 33 ]. To summarize, greater insight is required into the linkages between inter-professional communication patterns and the use of specific EHR affordances if one is to improve EHR integration in healthcare practices [ 10 ]. In the current paper, affordance theory is applied in unravelling these linkages.

Theory of affordances

In this paper, we adopt the theory of affordances as a theoretical lens through which to understand the relationship linking information systems, actors, and use outcomes [ 34 ]. According to Gibson’s landmark definition [ 35 ], an affordance is what is offered, provided, or furnished to someone or something by an object. Within the information systems field, the basic principle of affordance theory is that information systems provide affordances for action possibilities, such as collaboration.

The theory of affordances views an information system in terms of what it affords or constrains actors to do [ 34 , 36 , 37 ]. These potentials for behaviors arise from the ability of a relationship between an artefact (e.g. an EHR) and goal-oriented actors (e.g. medical specialists) to achieve immediate concrete outcomes (e.g. inter-professional collaboration) [ 7 ]. Significantly, users first have to perceive these affordances and only then can they actualize them in order to effectively use an information system. As such, the theory takes the system into consideration but also recognizes the influence of actors. Affordances can exist without being actualized or even recognized by the actors: they only offer potential for action [ 35 , 38 , 39 ]. In other words, EHRs provide certain affordances that are built into the system by the EHR provider with specific actors in mind. However, the benefits that accrue from using these affordances depend on how these actors perceive and actualize them [ 7 ].

Strong et al. [ 7 ] specifically show that EHRs provide affordances to coordinate, monitor, standardize, and integrate care; capture, access, and use data about patients; substitute healthcare professionals, shift work across roles; and increasingly use information in clinical decision-making. More recently, Bardram and Houben [ 22 ] show that EHRs contain so-called collaborative affordances that enable collaborative action and workflow among different actors. In their study, they define collaborative affordances as “a relation between an artifact and a set of human actors, that affords the opportunity for these actors to perform a collaborative action within a specific social context ” ([ 22 ], p. 8). The authors identify four collaborative affordances: portability (to navigate health records between locations), co-located access (to support simultaneous access), shared overview (to collectively build a shared information overview), and mutual awareness (to maintain mutual awareness of the work’s progress), but acknowledge that future research may identify further collaborative affordances.

EHRs’ affordances are not actualized in a vacuum: contextual factors influence how actors perceive and/or actualize them [ 39 ]. A hospital could, for example, organize improvement meetings to share ideas and stimulate affordance actualization across its departments [ 7 ]. In a remote telemedicine project in Nepal, the actualization of crucial affordances was found to depend on the accommodation of changes in personal, social, and cultural arrangements [ 40 ]. The latter study also points to the interdependency between different collaborative affordances: if actors actualize some affordances, this may lead to an outcome that may trigger the recognition of other affordances [ 40 ]. By drawing on data from five diverse multidisciplinary outpatient clinics in a hospital, we examined how an EHR’s inscribed collaborative affordances facilitated and constrained actual collaboration within and between disciplines and medical specialties.

Research site and department selection

To examine how an EHR’s collaborative affordances support or impede multidisciplinary collaboration between medical specialties within five outpatient clinics, we conducted an embedded case study in a hospital in the Netherlands. Adopting an embedded case-study approach allowed us to study EHR-facilitated collaboration in its natural setting and to recognize the complexities involved.

In the hospital investigated, a commercial cloud-based standard EHR system from a well-known global vendor had been implemented a year before the start of this study. The EHR implementation was also a response to the nationwide legislation prescribing to record patient data in organization wide Electronic Health Records. Before its implementation, departments used their own departmental IT applications and paper based systems to support their healthcare processes. After the EHR implementation, hospital-wide policies were developed to promote EHR use and the entering of patient data during the medical examination and visits. However, there were no formal sanctions for not using the EHR in line with the way intended by the implementers. Further, each clinic had its own management board, which had some discretion regarding the adoption and use of the EHR routines. The clinics researched were selected to give variation in the number of specialties involved, type of care, and the workflow dependencies between the specialties. Table  1 provides an overview of the breadth of the outpatient clinics and the functions of those we interviewed.

Data collection

In selecting interviewees, we aimed to develop a comprehensive overview of the range of perspectives on collaboration. For each clinic, at least one medical specialist was interviewed. The administrative support perspective was included because the EHR introduction partly shifted administrative tasks from the medical administration to the medical professionals (medical specialists, nurse specialists, residents) so that registration could be realized at source. The managerial perspective was included because managers of the outpatient clinics had a keen interest in enhanced multidisciplinary collaboration.

Interviewees were selected with the support of two managers who had been involved in the EHR implementation. Initially, only those disciplines that had a clear role in the care delivery or fulfilled a management role were selected: heads of the medical administration, medical specialists, medical managers, and the business managers of outpatient clinics. Interviewees were invited by mail, and all invitees accepted the invitation. Once they had accepted the invitation, appointments were scheduled. Following a theoretical sampling logic [ 41 ], if the initial interviews showed that other disciplines were involved in a clinic’s collaborative practices, these other disciplines would be added to the list of interviewees (see Table 1 ). Similarly, three interviewees suggested to have an EHR expert present during the interview to help in explaining their experiences with the system, which resulted in three two-person interviews. Interviewees cautioned that the interview data might be negatively biased since the hospital was still in the post-implementation phase. To check this, two additional interviews were held (by phone) with two nurse specialists of a hospital that had already been working with the same EHR suite for five years. Thus, in total, 29 people were interviewed of which 27 were employed at the focal hospital.

Data were collected between September 2018 and February 2019. The face-to-face interviews at the clinic were all were voice-recorded with the exception of one interview where we took notes during the conversation. The interviews were semi-structured, leaving room for further probing and lasted 25–45 min. The interview protocol included questions on the four collaborative affordances proposed by Bardram and Houben [ 22 ]: (1) Portability, (2) Co-located access, (3) Shared overview, and (4) Mutual awareness (see Additional File  1 ). Moreover, open questions about the EHR’s effect on collaboration made it possible for further affordances to emerge.

Data analysis

We analyzed the data by following the four steps.

Step 1. All the interview data were transcribed and read through thoroughly for each outpatient clinic individually.

Step 2. Codes were generated by two coders to create a comprehensive codebook (see Additional File  2 ). This data analysis approach was chosen because it allowed the context of each outpatient clinic to be kept in mind and offered opportunities to discover the facilitating or constraining conditions for collaborative affordance actualization.

Step 3. Data were coded that could be associated with collaborative affordances, or collaboration within and between specialties, or collaboration within and between disciplines, or that concerned facilitating or constraining conditions for affordance actualization [ 42 ]. The coding procedure was primarily inductive within the four collaborative affordance categories deductively derived from the literature (Portability, Co-located access, Shared overview and Mutual awareness (see [ 14 ]), while maintaining an open eye for other affordances or themes (see codebook in Additional File 2 . The inductive codes were derived from the data [ 43 ]. First, the emerging themes were described by using a first-order code that preserved the practitioner’s voice. Second, these first-order codes were classified into second-order code groups.

Step 4. Second-order codes were aggregated into seven themes, consisting of the four initial collaborative affordances, two additional ones (Messaging and Orchestrating), and Conditions for affordance actualization. Finally, these aggregated themes and the included first-order and second-order codes were cross-checked among the cases.

This section reports on the cross-case analysis, with the underlying, within-case descriptions available in Additional File  3 . Table  2 summarizes how the six identified collaborative affordances of the EHR facilitated and/or constrained collaboration within and between disciplines and medical specialties.

Affordance 1: portability

A widely shared view among the interviewed representatives of the clinics was that information from each specialty was integrated in the EHR. The former (legacy) system had already provided this functionality but the EHR ensured that notes by Medical Specialists were now also included. Several functional groups voiced the importance of collecting data from all specialties since this resulted in a comprehensive overview of the available information. Based on this shared overview, the medical specialists were better able to develop mutual awareness. This was most strongly expressed in the most intensively collaborating outpatient clinics (A, C, and D).

In three clinics (A, C, and E), the Medical Specialists argued that photographs imported into the EHR were sometimes still only accessible by certain clinical specialties. As a result, medical specialists were not able to discuss these images during meetings. This was experienced as a negative influence on collaboration. Moreover, it was argued that, in all cases, the health records of hospitalized patients were still tied to specific medical domains. As a consequence, medical specialists could only access these health records if they logged onto specific domains. However, no one commented that this negatively affected collaboration.

Interviewees from three clinics (B, D, and E) commented that data could not be shared with neighboring-hospitals through the EHR. Since these clinics receive many patients from other hospitals, this interfered with communication with medical specialists from the neighboring hospitals which hindered mutual awareness. Further, outpatient clinic B works intensively with external parties such as research institutions and its Business Manager indicated the difficulties in sharing relevant EHR data with these parties: “I find it a big disadvantage that we cannot easily get reports from the EHR. We can’t do that ourselves: we are constantly dependent on others! [...]. But, we as [specific specialty] have to share lots of data with external agencies and we have struggled with that for a long time.” - [B-BM1].

Affordance 2: co-located access

In all the clinics, respondents mentioned that the EHR enabled simultaneous access to health records, but also complained that the EHR prevented users simultaneously modifying health records. Several medical specialists from all the outpatient clinics mentioned that they could not place orders when a colleague was working on the same health record.

It was also striking that the different functional groups who collaborated in the same office or clinic were frequently hindered. For example, three medical specialists and medical residents of outpatient clinic E mentioned that they, temporarily, could not complete their work during joint consultations with nursing specialists. In such situations, only one professional could have access to the health record. As a consequence, another professional was denied access and was therefore unable to process orders or relevant data in the EHR. Whether this should be an issue was questioned by one medical specialist since he was convinced that EHR users would often be working on different parts of the EHR database and could therefore not imagine that co-located access needed to be obstructed. Moreover, he argued that it would only make sense to impede co-located access when professionals were trying to work on the same part of the EHR database.

A very large number of notes were created in the EHR, in part because these could only be changed by their owners. Concerns were expressed in outpatient clinics A, C, D, and E about the quality of the shared overview since this was complicated by dozens of notes by various specialists. On a different but related issue, medical specialists were hindered in collaborating during multidisciplinary meetings when they had access to only one desktop because they then had to switch between medical results and the notes of the meeting which made it difficult to remember which patient was being discussed. Some had already seen that this could resolved by using a second desktop.

Affordance 3: shared overview

As mentioned above, the large number of notes negatively affected the quality of the shared overview. As a result, medical specialists of outpatient clinics A, C, D, and E commented that they were hindered in gaining a mutual awareness of other specialists’ notes. Since data were ordered on priority and not on the chronology of events, all the interviewed medical specialists felt impeded in easily understanding what had occurred in the medical timeline of their patients. Interviewees from outpatient clinics B, D, and E commented that handwritten notes were something from the past, because notes were now entered in the EHR. Therefore, they argued, medical specialists should “finally” be able to understand the notes of their colleagues.

In all the outpatient clinics, medical specialists argued that the medical history and problem lists of patients were not useful in gaining a mutual awareness of the issues with other specialties. Two reasons emerged from the interviews. First, within specialties, there are different views on which information was important for providing high quality care. Therefore, within specialties, data were entered in different ways, resulting in specialty-specific information that was less useful for other specialties. Second, it was even argued that, within some specialties, no use was made of the medical history and problem lists, leading to friction between specialties when patients were referred with an empty health record when it came to certain specialties. Interestingly, some specialties involved in outpatient clinics A and C had developed a uniform policy for the use of the medical history and problem lists. As a result, all the medical specialists of these specialties entered the required data.

The importance of integrated information resources in providing high quality care was expressed by many medical specialists. For example, for some outpatient clinics (D and E), the medical history and problem lists were seen as highly important since these clinics often treat patients with an extensive medical history. However, as already mentioned, there were concerns about the quality of the data. Indeed, some interviewees of outpatient clinics D and E said that they did not make use of the medical history in their medical consultations and surgeries because they simply did not trust the data stored in the system. Specifically, some medical specialists and medical residents commented that important information was occasionally missing from the medical history and problem lists. Consequently, these interviewees explicitly read letters (as contained in the EHR) to develop a mutual awareness with other medical specialists.

Data included in the medical history and problem lists were tied to certain codes of the EHR’s vocabulary. As a result, symptoms that were not in its vocabulary, or symptoms that were misspelt could not be added. In this respect, the medical specialists of outpatient clinic E highlighted that the EHR did not include an adequate search functionality, impeding them in connecting the correct diagnosis with an appropriate code. This was considered to decrease the quality of the shared overview.

In all the clinics, it was argued that the provision of a shared overview is a requirement for collaboration between specialties as it increases their mutual awareness. However, this would only be effective if all the hospital’s specialties used the EHR consistently, which was not the case.

Affordance 4: mutual awareness

In each of the clinics, interviewees argued that medical specialists were impeded in developing a mutual awareness between specialties because information was not clearly represented in the EHR. Two causes were offered: (1) the shared overview was not clear because each specialty entered the data differently, which negatively influenced the mutual awareness between medical specialists of different specialties, and (2) the data in the EHR were sorted on priority what impeded specialists in seeing what had happened in the medical timelines of their patients. As discussed earlier, the mutual awareness of some medical specialists from outpatient clinics A, C, and E was decreased because images were still tied to certain departments.

On the other hand, the EHR could support medical specialists in improving their mutual awareness of patients’ medical timelines since this process was now more transparent. Moreover, the mutual awareness between medical specialists was increased due to portable notes. With all the specialties of the hospital integrated in the EHR, interviewees in four outpatient clinics (B, C, D, and E) commented that patients could be referred more easily between different specialties by means of the orchestrating affordance. The use of the messaging affordance was also seen as an important component in supporting mutual awareness between medical specialists.

No single functionality of the EHR could be directly linked to the mutual awareness of healthcare professionals. However, all the other collaborative affordances had an influence on actors’ mutual awareness. Therefore, the mutual awareness between different healthcare professionals was seen by many interviewees as a highly important factor in collaboration. However, the mutual awareness between medical specialists depended on uniform use of the EHR.

Affordance 5: messaging

The advantages of the messaging affordance were experienced differently in each outpatient clinic. However, interviewees in all the clinics appreciated the benefit of having the possibility to attach health records to messages. Previously, patient-related matters were discussed through Outlook. This frequently led to misunderstandings between medical specialists because health records could not be attached to an email. In three outpatient clinics (A, C, and D), it was mentioned that some specialties had developed a policy that required the use the messaging option. Through this, various functional groups within these specialties could be assured that the “receiver” had actually read their message. Accordingly, the healthcare professionals in these specialties were better equipped to gain a mutual awareness.

Conversely, in clinics B and E, there was no observed shift to adopt messaging. Specialties in these outpatient clinics did not adopt a uniform policy for the use of the messaging affordance. In these cases, the collaborative advantages depended on the medical specialists’ individual decisions to use the afforded messaging. Certainly, some of the medical specialists in outpatient clinic E did not use messaging. Interviewees from both clinics B and E commented that some medical administrators did use messaging, but that some medical specialists did not. As a result, the medical administrators’ messages were not answered.

As such, the delivery of the collaborative advantages offered by the messaging affordance depended on its uniform use in and between clinics. It was widely argued that the messaging opportunity currently led to an information overload, mostly due to it being used for uninformative medical results or letters. A decrease in the use of the messaging option had been noted in outpatient clinics B and E. Side effects of the messaging system were also expressed. For example, verbal communication between collaborating disciplines was lost in most cases, and interviewees saw this as negatively impacting on their collaboration.

Affordance 6: orchestrating

All the business managers and medical administrators argued that the use of the orchestrating affordance was more efficient than the use of paper notes because orders were processed immediately and sent to the correct actor. On the other hand, most medical specialists argued that the use of the orchestrating affordance led to time consuming digitalized healthcare processes and marginalized verbal communication with colleagues, patients and the medical administration. As reported by previous studies [ 17 , 18 ], the time spent to use the EHR was experienced to decrease the time spent on verbal communication with colleagues and patients. Here, this was seen to negatively affect the collaboration between different functional groups and the quality of healthcare. This was expressed by a Medical specialist as follows: “The disadvantage is that the EHR takes away the interaction between people and I think that is actually a drawback: interactions between people are often more useful than the parametric recording of data. We have to make sure that we don’t diminish this interaction too much.” - [D-MS3]. Hence, making use of the orchestrating affordance was seen as boosting efficiency from a managerial perspective but was perceived as less desirable from a patient care standpoint.

In four outpatient clinics (B, C, D, and E), it was voiced that different disciplines were not able to work in a natural way because all processes were now based on digitalized orders. Examples were provided of some disciplines not collaborating without an order as this was the hospital’s policy. For example, daily face-to-face collaborative processes between nurses and medical specialists were hindered by this in outpatient clinics B, D, and E.

Most interviewees argued that the orchestrating affordance did not easily support them in arranging multidisciplinary consultations with other specialties. Although the nature of these consultations was not affected, the orchestrating opportunity did not properly guide financial information flows within the hospital. As a result, some outpatient clinics did not receive financial compensation for organizing these meetings. As a consequence, one of outpatient clinic A’s specialties was in debt for organizing these consultations. Moreover, medical specialists from outpatient clinics D and E mentioned that it was too difficult to arrange multidisciplinary consultations through the EHR. Consequently, these interviewees did not make use of this functionality.

Some medical specialists raised the difficulty of inviting other medical specialists to consultations. One medical specialist argued that this could be seen more as a future potential of the EHR to further support the collaboration between different specialties. However, several business managers and medical specialists expressed the view that multidisciplinary meetings were better registered in the EHR and that the results of these meetings could more easily be found than before.

Influence of organizational choices and policies

EHR users interviewed from all the clinics agreed that the strict role authorization and different system representations in the EHR hindered interdisciplinary collaboration. The hospital had only authorized medical specialists to enter patient-related data in the EHR. These strict role authorizations limited interdisciplinary collaboration as a business manager explained: “ The Board of our organization decided that only medical specialists would be authorized to enter patient-related data. Therefore, the medical administrators are no longer authorized although it is, of course, a team that is collaborating .” [C-BM1]. Before the introduction of the EHR, medical administrators were authorized to enter patient-related data and, therefore, a shift was perceived in the administrative burden from the medical administration to the medical specialists.

Various medical specialties found that they were not able to collaborate well because each specialty worked in a different medical context in the EHR. As a result, EHR users were hindered in understanding what had taken place when something went wrong in terms of orchestrating because the two parties had different system representations.

In two outpatient clinics (A and D), the lack of organization-wide policies made it difficult to actualize the collaborative affordances. However, all the medical specialties involved were required to use the EHR according to the department’s own policy. As such, all the specialties were assured that certain affordances were used. As a result, actors were guaranteed that their messages would be actually read by the right actor. In the other outpatient clinics (B, C, and E), no indications were found that a department-wide policy had been implemented. As a consequence, collaborative affordances were perceived and actualized differently by the various medical specialists.

The core processes seen in hospitals are highly collaborative in nature and many hospitals have implemented comprehensive Electronic Health Records to facilitate multidisciplinary collaboration. By adopting an affordance lens, this study has examined how an Electronic Health Record facilitates or impedes collaboration in five outpatient clinics. Through an analysis of the interplay between collaborative EHR affordances, we contribute to the literature by demonstrating how multidisciplinary collaboration is not only facilitated but also constrained, and how EHRs can have intended as well as unintended impacts on collaboration [ 9 ].

This study draws on Bardram and Houben’s [ 22 ] identification of four collaborative affordances (Portability, Co-located access, Shared overview and Mutual awareness) and complements their findings with two additional, inductively derived, collaborative affordances: Messaging and Orchestrating. The labelling and definitions of the latter two were aligned with the research of Chase [ 11 ]. Messaging concerns information transfer and communication between professionals, and also with other members of the hospital. Orchestrating ensures that the right person is doing the right thing at the right time for the patient. This study illustrates that the introduction of an EHR changes important structuring devices in the organizational processes of hospitals [ 44 ].

Our study shows that Electronic Health Records facilitate collaboration through the systematic integration of patient data from different specialties, which results in a shared and comprehensive health record to which users can have simultaneous access independent of time and place. This promotes mutual understanding and enables health professionals to coordinate their activities and prevents the duplication of activities such as tests. At the focal hospital, the EHR served as a joint communication channel. This discouraged health professionals from continuing with their handwritten notes and their local health records, and promoted the use of shared data. The EHR also facilitates collaboration by providing the information necessary for joint clinical decision-making, which is especially important for the quality of care of chronic patients who are often treated by several medical specialists. The EHR also promotes hospital-wide shared working processes, which creates conditions necessary for collaboration.

However, our study also shows how the EHR in the focal hospital can at times inhibit multidisciplinary collaboration [ 10 , 11 , 12 , 13 ]. Many specialty- and discipline-specific user interfaces were seen as constraining a mutual understanding of patient data. Medical departments utilized particular functionalities, such as medical histories and problem lists, in different ways, making the EHR less reliable and thereby complicating collaboration. Many physicians also argued that entering and reading large amounts of patient information is time consuming, creates information overload and harms effective collaboration. This was partly caused by the specific use of the system, e.g. physicians posting numerous individual notes. The EHR reduces the need for direct face-to-face communication which is intended to save time, but is experienced as hindering a smooth workflow and the building of mutual trust and effective collaboration. Several of the interviewed medical specialists expressed the risks they associated with receiving electronic warnings of life-threatening medical results by means of the messaging function. Before the introduction of the EHR, medical specialists were informed of such results by phone or face-to-face. Finally, this EHR, with its inward-looking focus on the hospital, was seen as complicating smooth information sharing and collaboration with healthcare providers outside the hospital.

This study showed that shared data can be difficult to use when the user interfaces differ among work units. Leonardi [ 34 ] and Orlikowski [ 45 ] both demonstrated that the extent that the intended benefits of a new technology are reaped is dependent on how actors actualize its affordances. In the hospital studied, the clinics actualized the EHR collaborative affordances differently, resulting in constrained collaboration among medical specialties [ 45 ]. This implies that the enforcement of hospital-wide policies on the use of EHRs are necessary to reap the potential benefits of these systems. This study also supports the research of Thapa and Sein [ 40 ] who argue that contextual factors largely determine the extent to which collaborative affordances are actualized by groups of individuals. Our findings show that EHR-enabled collaboration is dependent on contextual factors including role authorizations, system representations, organizational policies, and how medical professionals use the collaborative affordances.

Practical implications

This study shows that both healthcare organizations and EHR providers should be aware of several issues related to the collaborative affordances of EHRs. First, hospitals consist of clinical departments that have different working routines and expectations from an EHR [ 22 ]. This implies that policies are needed to achieve effective collaboration among departments through an EHR. Without organization-wide policies, departments may actualize collaborative affordances differently and, as a result, as this study showed, the mutual awareness and common ground between different healthcare professionals can be harmed. One of these policies is to involve the different medical disciplines and to achieve shared decision-making and ownership regarding the selection, implementation and adaptation of collaborative technologies, such as EHRs. Hospitals should also recognize the downside of strict role-authorizations in EHRs as our findings indicate that these negatively influenced collaboration among different functional groups. Further, practitioners need to be aware of the drawbacks of the different system-representations (medical contexts) in EHRs and how these may negatively affect collaboration on the clinical department level. Finally, this study shows that EHRs can both support and impede collaboration on different organizational levels. Since achieving the advantages is dependent on the affordance actualization process adopted by multiple clinical departments and disciplines, managers should carefully guide this process if they wish to reap the full potential benefits in terms of an EHR’s collaborative affordances.

Limitations and opportunities for further research

This study focused on five outpatient clinics that all involved multiple specialties. We recognize that including collaborative practices in and with other clinical facilities as well as interorganizational collaboration might have resulted in additional perspectives on how each of the EHR’s affordances facilitate or constrain collaboration. We also acknowledge that this study was conducted at only one institution with one EHR system. Previous research [ 46 ] shows wide variation in how different institutions use the same EHR product in different ways and how different EHR products offer different functionalities. Another limitation is that this EHR system was implemented only one year before we conducted this study. We can imagine that this relatively short timeframe has influenced the study’s outcome. When users become more familiar with the affordances, they may find converging ways to make it instrumental per type of multi-disciplinary collaboration. Therefore, we would urge further research on how hospitals and EHR providers can overcome the constraining influences we identified, notably those related to each discipline having different system representations. We saw that this discipline-related system representation afforded efficiency in electronic record use within a discipline and prevented information overload, but hindered the cross- and interdisciplinary collaboration needed for integrated patient care. Another relevant direction for further research is to examine how the collaborative affordances of an EHR impact the care provider – patient relationship and the resulting quality of clinical care.

The aim of this study was to examine how the collaborative affordances of an EHR are actualized in its use by the disciplines responsible for patient care in outpatient clinics. Our findings indicate that the EHR’s affordances do have the intended facilitating influences on collaboration but, simultaneously and unintendedly, constrain collaboration in other ways. This prevented full actualization of the collaborative affordances in the focal hospital. In order to actualize the collaborative affordances of EHRs more fully, health professionals need to be able to retrieve, understand, and trust each other’s information. Only then can they rely on each other’s appropriate and timely use of the system. Such multifaceted trust can gradually develop through hospital-wide policies that stimulate a more coordinated use of the system, which may involve the formal recognition of positive workarounds to counter the constraints identified. Further research is needed to determine which organizational, technical, and behavioral adaptations can more fundamentally solve the constraining influences.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Electronic Health Record

Head of the Medical Administration

Medical specialist or resident

Nursing specialist

Medical manager

Business manager

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Acknowledgments

We are indebted to the project members and participating hospital employees for their trust and willingness to share their views and experiences.

There was no funding for this research project.

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Vos, J.F.J., Boonstra, A., Kooistra, A. et al. The influence of electronic health record use on collaboration among medical specialties. BMC Health Serv Res 20 , 676 (2020). https://doi.org/10.1186/s12913-020-05542-6

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

Published on 16.4.2024 in Vol 26 (2024)

Adverse Event Signal Detection Using Patients’ Concerns in Pharmaceutical Care Records: Evaluation of Deep Learning Models

Authors of this article:

Original Paper

• Satoshi Nishioka 1 , PhD   ; 
• Satoshi Watabe 1 , BSc   ; 
• Yuki Yanagisawa 1 , PhD   ; 
• Kyoko Sayama 1 , MSc   ; 
• Hayato Kizaki 1 , MSc   ; 
• Shungo Imai 1 , PhD   ; 
• Mitsuhiro Someya 2 , BSc   ; 
• Ryoo Taniguchi 2 , PhD   ; 
• Shuntaro Yada 3 , PhD   ; 
• Eiji Aramaki 3 , PhD   ; 
• Satoko Hori 1 , PhD  

1 Division of Drug Informatics, Keio University Faculty of Pharmacy, Tokyo, Japan

2 Nakajima Pharmacy, Hokkaido, Japan

3 Nara Institute of Science and Technology, Nara, Japan

Corresponding Author:

Satoko Hori, PhD

Division of Drug Informatics

Keio University Faculty of Pharmacy

1-5-30 Shibakoen

Tokyo, 105-8512

Phone: 81 3 5400 2650

Email: [email protected]

Background: Early detection of adverse events and their management are crucial to improving anticancer treatment outcomes, and listening to patients’ subjective opinions (patients’ voices) can make a major contribution to improving safety management. Recent progress in deep learning technologies has enabled various new approaches for the evaluation of safety-related events based on patient-generated text data, but few studies have focused on the improvement of real-time safety monitoring for individual patients. In addition, no study has yet been performed to validate deep learning models for screening patients’ narratives for clinically important adverse event signals that require medical intervention. In our previous work, novel deep learning models have been developed to detect adverse event signals for hand-foot syndrome or adverse events limiting patients’ daily lives from the authored narratives of patients with cancer, aiming ultimately to use them as safety monitoring support tools for individual patients.

Objective: This study was designed to evaluate whether our deep learning models can screen clinically important adverse event signals that require intervention by health care professionals. The applicability of our deep learning models to data on patients’ concerns at pharmacies was also assessed.

Methods: Pharmaceutical care records at community pharmacies were used for the evaluation of our deep learning models. The records followed the SOAP format, consisting of subjective (S), objective (O), assessment (A), and plan (P) columns. Because of the unique combination of patients’ concerns in the S column and the professional records of the pharmacists, this was considered a suitable data for the present purpose. Our deep learning models were applied to the S records of patients with cancer, and the extracted adverse event signals were assessed in relation to medical actions and prescribed drugs.

Results: From 30,784 S records of 2479 patients with at least 1 prescription of anticancer drugs, our deep learning models extracted true adverse event signals with more than 80% accuracy for both hand-foot syndrome (n=152, 91%) and adverse events limiting patients’ daily lives (n=157, 80.1%). The deep learning models were also able to screen adverse event signals that require medical intervention by health care providers. The extracted adverse event signals could reflect the side effects of anticancer drugs used by the patients based on analysis of prescribed anticancer drugs. “Pain or numbness” (n=57, 36.3%), “fever” (n=46, 29.3%), and “nausea” (n=40, 25.5%) were common symptoms out of the true adverse event signals identified by the model for adverse events limiting patients’ daily lives.

Conclusions: Our deep learning models were able to screen clinically important adverse event signals that require intervention for symptoms. It was also confirmed that these deep learning models could be applied to patients’ subjective information recorded in pharmaceutical care records accumulated during pharmacists’ daily work.

Introduction

Increasing numbers of people are expected to develop cancers in our aging society [ 1 - 3 ]. Thus, there is increasing interest in how to detect and manage the side effects of anticancer therapies in order to improve treatment regimens and patients’ quality of life [ 4 - 8 ]. The primary approaches for side effect management are “early signal detection and early intervention” [ 9 - 11 ]. Thus, more efficient approaches for this purpose are needed.

It has been recognized that patients’ voices concerning adverse events represent an important source of information. Several studies have indicated that the number, severity, and time of occurrence of adverse events might be underevaluated by physicians [ 12 - 15 ]. Thus, patient-reported outcomes (PROs) have recently received more attention in the drug evaluation process, reflecting patients’ real voices. Various kinds of PRO measures have been developed and investigated in different disease populations [ 16 , 17 ]. Health care authorities have also encouraged the pharmaceutical industry to use PROs for drug evaluation [ 18 , 19 ], and it is becoming more common to take PRO assessment results into consideration for drug marketing approval [ 20 , 21 ]. Similar trends can be seen in the clinical management of individual patients. Thus, health care professionals have an interest in understanding how to appropriately gather patients’ concerns in order to improve safety management and clinical decisions [ 22 - 24 ].

The applications of deep learning for natural language processing have expanded dramatically in recent years [ 25 ]. Since the development of a high-performance deep learning model in 2018 [ 26 ], attempts to apply cutting-edge deep learning models to various kinds of patient-generated text data for the evaluation of safety events or the analysis of unscalable subjective information from patients have been accelerating [ 27 - 31 ]. Most studies have been conducted to use patients’ narrative data for pharmacovigilance [ 27 , 32 - 35 ], while few have been aimed at improvement of real-time safety monitoring for individual patients. In addition, there have been some studies on adverse event severity grading based on health care records [ 36 - 39 ], but none has yet aimed to extract clinically important adverse event signals that require medical intervention from patients’ narratives. It is important to know whether deep learning models could contribute to the detection of such important adverse event signals from concern texts generated by individual patients.

To address this question, we have developed deep learning models to detect adverse event signals from individual patients with cancer based on patients’ blog articles in online communities, following other types of natural language processing–related previous work [ 40 , 41 ]. One deep learning model focused on the specific symptom of hand-foot syndrome (HFS), which is one of the typical side effects of anticancer treatments [ 42 ], and another focused on a broad range of adverse events that impact patients’ activities of daily living [ 43 ]. We showed that our models can provide good performance scores in targeting adverse event signals. However, the evaluation relied on patients’ narratives from the patients’ blog data used for deep learning model training, so further evaluation is needed to ensure the validity and applicability of the models to other texts regarding patients’ concerns. In addition, the blog data source did not contain medical information, so it was not feasible to assess whether the models could contribute to the extraction of clinically important adverse event signals.

To address these challenges, we focused on pharmaceutical care records written by pharmacists at community pharmacies. The gold standard format for pharmaceutical care records in Japan is the SOAP (subjective, objective, assessment, plan)-based document that follows the “problem-oriented system” concept proposed by Weed [ 44 ] in 1968. Pharmacists track patients’ subjective concerns in the S column, provide objective information or observations in the O column, give their assessment from the pharmacist perspective in the A column, and suggest a plan for moving forward in the P column [ 45 , 46 ]. We considered that SOAP-based pharmaceutical care records could be a unique data source suitable for further evaluation of our deep learning models because they contain both patients’ concerns and professional health care records by pharmacists, including the medication prescription history with time stamps. Therefore, this study was designed to assess whether our deep learning models could extract clinically important adverse event signals that require intervention by medical professionals from these records. We also aimed to evaluate the characteristics of the models when applied to patients’ subjective information noted in the pharmaceutical care records, as there have been only a few studies on the application of deep learning models to patients’ concerns recorded during pharmacists’ daily work [ 47 - 49 ].

Here, we report the results of applying our deep learning models to patients’ concern text data in pharmaceutical care records, focusing on patients receiving anticancer treatment.

Data Source

The original data source was 2,276,494 pharmaceutical care records for 303,179 patients, created from April 2020 to December 2021 at community pharmacies belonging to the Nakajima Pharmacy Group in Japan [ 50 ]. To focus on patients with cancer, records of patients with at least 1 prescription for an anticancer drug were retrieved by sorting individual drug codes (YJ codes) used in Japan (YJ codes starting with 42 refer to anticancer drugs). Records in the S column (ie, S records) were collected from the patients with cancer as the text data of patients’ concerns for deep learning model analysis.

Deep Learning Models

The deep learning models used for this research were those that we constructed based on patients’ narratives in blog articles posted in an online community and that showed the best performance score in each task in our previous work (ie, a Bidirectional Encoder Representations From Transformers [BERT]–based model for HFS signal extraction [ 42 ] and a T5-based model for adverse event signal extraction [ 43 ]). BERT [ 26 ] and T5 [ 51 ] both belong to a type of deep learning model that has recently shown high performance in several studies [ 29 , 52 ]. Hereafter, we refer to the deep learning model for HFS signals as the HFS model, the model for any adverse event signals as All AE (ie, all or any adverse events) model, and the model for adverse event signals limited to patients’ activities of daily living as the AE-L (adverse events limiting patients’ daily lives) model. It was also confirmed that these deep learning models showed similar or higher performance scores for the HFS, All AE, or AE-L identification tasks using 1000 S records randomly extracted from the data source of this study compared to the values obtained in our previous work [ 42 , 43 ] (the performance scores of sentence-level tasks from our previous work are comparable, as the mean number of words in the sentences in the data source in our previous work was 32.7 [SD 33.9], which is close to that of the S records used in this study, 38.8 [SD 29.4]). The method and results of the performance-level check are described in detail in Multimedia Appendix 1 [ 42 , 43 ]. We applied the deep learning models to all text data in this study without any adjustment in setting parameters from those used in constructing them based on patient-authored texts in our previous work [ 42 , 43 ].

Evaluation of Extracted S Records by the Deep Learning Models

In this study, we focused on the evaluation of S records that our deep learning models extracted as HFS or AE-L positive. Each positive S record was assessed as if it was a true adverse event signal, a sort of adverse event symptom, whether or not an intervention was made by health care professionals. We also investigated the kind of anticancer treatment prescription in connection with each adverse event signal identified in S records.

To assess whether an extracted positive S record was a true adverse event signal, we used the same annotation guidelines as in our previous work [ 43 ]. In brief, each S record was treated as an “adverse event signal” if any untoward medical occurrence happened to the patient, regardless of the cause. For the AE-L model only, if a positive S record was confirmed as an adverse event signal, it was further categorized into 1 or more of the following adverse event symptoms: “fatigue,” “nausea,” “vomiting,” “diarrhea,” “constipation,” “appetite loss,” “pain or numbness,” “rash or itchy,” “hair loss,” “menstrual irregularity,” “fever,” “taste disorder,” “dizziness,” “sleep disorder,” “edema,” or “others.”

For the assessment of interventions by health care professionals and anticancer treatment prescriptions, information from the O, A, and P columns and drug prescription history in the data source were investigated for the extracted positive S records. The interventions by health care professionals were categorized in any of the following: “adding symptomatic treatment for the adverse event signal,” “dose reduction or discontinuation of causative anticancer treatment,” “consultation with physician,” “others,” or “no intervention (ie, just following up the adverse event signal).” The actions categorized in “others” were further evaluated individually. For this assessment, we also randomly extracted 200 S records and evaluated them in the same way for comparison with the results from the deep learning model. Prescription history of anticancer treatment was analyzed by primary category of mechanism of action (MoA) with subcategories if applicable (eg, target molecule for kinase inhibitors).

Applicability Check to Other Text Data Including Patients’ Concerns

To check the applicability of our deep learning models to data from a different source, interview transcripts from patients with cancer were also evaluated. The interview transcripts were created by the Database of Individual Patient Experiences-Japan (DIPEx-Japan) [ 53 ]. DIPEx-Japan divides the interview transcripts into sections for each topic, such as “onset of disease” and “treatment,” and posts the processed texts on its website. Processing is conducted by accredited researchers based on qualitative research methods established by the University of Oxford [ 54 ]. In this study, interview text data created from interviews with 52 patients with breast cancer conducted from January 2008 to October 2018 were used to assess whether our deep learning models can extract adverse event signals from this source. In total, 508 interview transcripts were included with the approval of DIPEx-Japan.

Ethical Considerations

This study was conducted with anonymized data following approval by the ethics committee of the Keio University Faculty of Pharmacy (210914-1 and 230217-1) and in accordance with relevant guidelines and regulations and the Declaration of Helsinki. Informed consent specific to this study was waived due to the retrospective observational design of the study with the approval of the ethics committee of the Keio University Faculty of Pharmacy. To respect the will of each individual stakeholder, however, we provided patients and pharmacists of the pharmacy group with an opportunity to refuse the sharing of their pharmaceutical care records by posting an overview of this study at each pharmacy store or on their web page regarding the analysis using pharmaceutical care records. Interview transcripts from DIPEx-Japan were provided through a data sharing arrangement for using narrative data for research and education. Consent for interview transcription and its sharing from DIPEx-Japan was obtained from the participants when the interviews were recorded.

From the original data source of 2,180,902 pharmaceutical care records for 291,150 patients, S records written by pharmacists for patients with a history of at least 1 prescription of an anticancer drug were extracted. This yielded 30,784 S records for 2479 patients with cancer ( Table 1 ). The mean and median number of words in the S records were 38.8 (SD 29.4) and 32 (IQR 20-50), respectively. We applied our deep learning models, HFS, All AE, and AE-L, to these 30,784 S records for the evaluation of the deep learning models for adverse event signal detection.

For interview transcripts created by DIPEx-Japan, the mean and median number of words were 428.9 (SD 160.9) and 416 (IQR 308-526), respectively, in the 508 transcripts for 52 patients with breast cancer.

a SOAP: subjective, objective, assessment, plan.

b S: subjective.

Application of the HFS Model

First, we applied the HFS model to the S records for patients with cancer. The BERT-based model was used for this research as it showed the best performance score in our previous work [ 42 ].

S Records Extracted as HFS Positive

The S records extracted as HFS positive by the HFS model ( Table 2 ) amounted to 167 (0.5%) records for 119 (4.8%) patients. A majority of the patients had 1 HFS-positive record in their S records (n=91, 76.5%), while 2 patients had as many as 6 (1.7%) HFS-positive records. When we examined whether the extracted S records were true adverse event signals or not, 152 records were confirmed to be adverse event signals, while the other 15 records were false-positives. All the false-positive S records were descriptions about the absence of symptoms or confirmation of improving condition (eg, “no diarrhea, mouth ulcers, or limb pain so far” or “the skin on the soles of my feet has calmed down a lot with this ointment”). Some examples of S records that were predicted as HFS positive by the model are shown in Table S1 in Multimedia Appendix 2 .

The same examination was conducted with interview transcripts from DIPEx-Japan. Only 1 (0.2%) transcript was extracted as HFS positive by the HFS model, and it was a true adverse event signal (100%). The actual transcript extracted as HFS positive is shown in Table S2 in Multimedia Appendix 2 .

a S: subjective.

b HFS: hand-foot syndrome.

c All false-positive S records were denial of symptoms or confirmation of improving condition.

Interventions by Health Care Professionals

The 167 S records extracted as HFS positive as well as 200 randomly selected records were checked for interventions by health care professionals ( Figure 1 ). The proportion showing any action by health care professionals was 64.1% for 167 HFS-positive S records compared to 13% for the 200 random S records. Among the actions taken for HFS positives, “adding symptomatic treatment” was the most common, accounting for around half (n=79, 47.3%), followed by “other” (n=18, 10.8%). Most “other” actions were educational guidance from pharmacists, such as instructions on moisturizing, nail care, or application of ointment and advice on daily living (eg, “avoid tight socks”).

Anticancer Drugs Prescribed

The types of anticancer drugs prescribed for HFS-positive patients are summarized based on the prescription histories in Table 3 . For the 152 adverse event signals identified by the HFS model in the previous section, the most common MoA class of anticancer drugs used for the patients was antimetabolite (n=62, 40.8%), specifically fluoropyrimidines (n=59, 38.8%). Kinase inhibitors were next (n=49, 32.2%), with epidermal growth factor receptor (EGFR) inhibitors and multikinase inhibitors as major subgroups (n=28, 18.4% and n=14, 9.2%, respectively). The third and fourth most common MoAs were aromatase inhibitors (n=24, 15.8%) and antiandrogen or estrogen drugs (n=7, 4.6% each) for hormone therapy.

a EGFR: epidermal growth factor receptor.

b VEGF: vascular endothelial growth factor.

c HER2: human epidermal growth factor receptor-2.

d CDK4/6: cyclin-dependent kinase 4/6.

Application of the All AE or AE-L model

The All AE and AE-L models were also applied to the same S records for patients with cancer. The T5-based model was used for this research as it gave the best performance score in our previous work [ 43 ].

S Records Extracted as All AE or AE-L positive

The numbers of S records extracted as positive were 7604 (24.7%) for 1797 patients and 196 (0.6%) for 142 patients for All AE and AE-L, respectively. In the case of All AE, patients tended to have multiple adverse event positives in their S records (n=1315, 73.2% of patients had at least 2 positives). In the case of AE-L, most patients had only 1 AE-L positive (n=104, 73.2%), and the largest number of AE-L positives for 1 patient was 4 (2.8%; Table 4 ).

We focused on AE-L evaluation due to its greater importance from a medical viewpoint and lower workload for manual assessment, considering the number of positive S records. Of the 197 AE-L–positive S records, it was confirmed that 157 (80.1%) records accurately extracted adverse event signals, while 39 (19.9%) records were false-positives that did not include any adverse event signals ( Table 4 ). The contents of the 39 false-positives were all descriptions about the absence of symptoms or confirmation of improving condition, showing a similar tendency to the HFS false-positives (eg, “The diarrhea has calmed down so far. Symptoms in hands and feet are currently fine” and “No symptoms for the following: upset in stomach, diarrhea, nausea, abdominal pain, abdominal pain or stomach cramps, constipation”). Examples of S records that were predicted as AE-L positive are shown in Table S3 in Multimedia Appendix 2 .

The deep learning models were also applied to interview transcripts from DIPEx-Japan in the same manner. The deep learning models identified 84 (16.5%) and 18 (3.5%) transcripts as All AE or AE-L positive, respectively. Of the 84 All AE–positive transcripts, 73 (86.9%) were true adverse event signals. The false-positives of All AE (n=11, 13.1%) were categorized into any of the following 3 types: explanations about the disease or its prognosis, stories when their cancer was discovered, or emotional changes that did not include clear adverse event mentions. With regard to AE-L, all the 18 (100%) positives were true adverse event signals (Table S4 in Multimedia Appendix 2 ). Examples of actual transcripts extracted as All AE or AE-L positive are shown in Table S5 in Multimedia Appendix 2 .

b All AE: all (or any of) adverse event.

c AE-L: adverse events limiting patients’ daily lives.

d All false-positive S records were denial of symptoms or confirmation of improving condition.

Whether or not interventions were made by health care professionals was investigated for the 196 AE-L–positive S records. As in the HFS model evaluation, data from 200 randomly selected S records were used for comparison ( Figure 2 ). In total, 91 (46.4%) records in the 196 AE-L–positive records were accompanied by an intervention, while the corresponding figure in the 200 random records was 26 (13%) records. The most common action in response to adverse event signals identified by the AE-L model was “adding symptomatic treatment” (n=71, 36.2%), followed by “other” (n=11, 5.6%). “Other” included educational guidance from pharmacists, inquiries from pharmacists to physicians, or recommendations for patients to visit a doctor.

The types of anticancer drugs prescribed for patients with adverse event signals identified by the AE-L model were summarized based on the prescription histories ( Table 5 ). In connection with the 157 adverse event signals, the most common MoA of the prescribed anticancer drug was antimetabolite (n=62, 39.5%) and fluoropyrimidine (n=53, 33.8%), which accounted for the majority. Kinase inhibitor (n=31, 19.7%) was the next largest category with multikinase inhibitor (n=14, 8.9%) as the major subgroup. These were followed by antiandrogen (n=27, 17.2%), antiestrogen (n=10, 6.4%), and aromatase inhibitor (n=10, 6.4%) for hormone therapy.

b JAK: janus kinase.

c VEGF: vascular endothelial growth factor.

d BTK: bruton tyrosine kinase.

e FLT3: FMS-like tyrosine kinase-3.

f PARP: poly-ADP ribose polymerase.

g CDK4/6: cyclin-dependent kinase 4/6.

h CD20: cluster of differentiation 20.

Adverse Event Symptoms

For the 157 adverse event signals identified by the AE-L model, the symptoms were categorized according to the predefined guideline in our previous work [ 43 ]. “Pain or numbness” (n=57, 36.3%) accounted for the largest proportion followed by “fever” (n=46, 29.3%) and “nausea” (n=40, 25.5%; Table 6 ). Symptoms classified as “others” included chills, tinnitus, running tears, dry or peeling skin, and frequent urination. When comparing the proportion of the symptoms associated with or without interventions by health care professionals, a trend toward a greater proportion of interventions was observed in “fever,” “nausea,” “diarrhea,” “constipation,” “vomiting,” and “edema” ( Figure 3 , black boxes). On the other hand, a smaller proportion was observed in “pain or numbness,” “fatigue,” “appetite loss,” “rash or itchy,” “taste disorder,” and “dizziness” ( Figure 3 , gray boxes).

This study was designed to evaluate our deep learning models, previously constructed based on patient-authored texts posted in an online community, by applying them to pharmaceutical care records that contain both patients’ subjective concerns and medical information created by pharmacists. Based on the results, we discuss whether these deep learning models can extract clinically important adverse event signals that require medical intervention, and what characteristics they show when applied to data on patients’ concerns in pharmaceutical care records.

Performance for Adverse Event Signal Extraction

The first requirement for the deep learning models is to extract adverse event signals from patients’ narratives precisely. In this study, we evaluated the proportion of true adverse event signals in positive S records extracted by the HFS or AE-L model. True adverse event signals amounted to 152 (91%) and 157 (80.1%) for the HFS and AE-L models, respectively ( Tables 2 and 4 ). Given that the proportion of true adverse event signals in 200 randomly extracted S records without deep learning models was 54 (27%; categories other than “no adverse event” in Figures 1 and 2 ), the HFS and AE-L models were able to concentrate S records with adverse event mentions. Although 15 (9%) for the HFS model and 39 (19.9%) for the AE-L model were false-positives, it was confirmed all of the false-positive records described a lack of symptoms or confirmation of improving condition. We considered that such false-positives are due to the unique feature of pharmaceutical care records, where pharmacists might proactively interview patients about potential side effects of their medications. As the data set of blog articles we used to construct the deep learning models included few such cases (especially comments on lack of symptoms), our models seemed unable to exclude them correctly. Even though we confirmed that the proportion of true “adverse event” signals extracted from the S records by the HFS or AE-L model was more than 80%, the performance scores to extract true “HFS” or “AE-L” signals were not so high based on the performance check using 1000 randomly extracted S records ( F 1 -scores were 0.50 and 0.22 for true HFS and AE-L signals, respectively; Table S1 in Multimedia Appendix 1 ). It is considered that the performance to extract true HFS and AE-L signals was relatively low due to the short length of texts in the S records, providing less context to judge the impact on patients’ daily lives, especially for the AE-L model (the mean word number of the S records was 38.8 [SD 29.4; Table 1 ], similar to the sentence-level tasks in our previous work [ 42 , 43 ]). However, we consider a true adverse event signal proportion of more than 80% in this study represents a promising outcome, as this is the first attempt to apply our deep learning models to a different source of patients’ concern data, and the extracted positive cases would be worthy of evaluation by a medical professional, as the potential adverse events could be caused by drugs taken by the patients.

When the deep learning models were applied to DIPEx-Japan interview transcripts, including patients’ concerns, the proportion of true adverse event signals was also more than 80% (for All AE: n=73, 86.9% and for HFS and AE-L: n=18, 100%). The difference in the results between pharmaceutical care S records and DIPEx-Japan interview transcripts was the features of false-positives, descriptions about lack of symptoms or confirmation of improving condition in S records versus explanations about disease or its prognosis, stories about when their cancer was discovered, or emotional changes in interview transcripts. This is considered due to the difference in the nature of the data source; the pharmaceutical care records were generated in a real-time manner by pharmacists through their daily work, where adverse event signals are proactively monitored, while the interview transcripts were purely based on patients’ retrospective memories. Our deep learning models were able to extract true adverse event signals with an accuracy of more than 80% from both text data sources in spite of the difference in their nature. When looking at future implementation of the deep learning models in society (discussed in the Potential for Deep Learning Model Implementation in Society section), it may be desirable to further adjust deep learning models to reduce false-positives depending upon the features of the data source.

Identification of Important Adverse Events Requiring Medical Intervention

To assess whether the models could extract clinically important adverse event signals, we investigated interventions by health care professionals connected with the adverse event signals that are identified by our deep learning models. In the 200 randomly extracted S records, only 26 (13%) consisted of adverse event signals, leading to any intervention by health care professionals. On the other hand, the proportion of signals associated with interventions was increased to 107 (64.1%) and 91 (46.4%) in the S records extracted as positive by the HFS and AE-L models, respectively ( Figures 1 and 2 ). These results suggest that both deep learning models can screen clinically important adverse event signals that require intervention from health care professionals. The performance level in screening adverse event signals requiring medical intervention was higher in the HFS model than in the AE-L model (n=107, 64.1% vs n=91, 46.4%; Figures 1 and 2 ). Since the target events were specific and adverse event signals of HFS were narrowly defined, which is one of the typical side effects of some anticancer drugs, we consider that health care providers paid special attention to HFS-related signals and took action proactively. In both deep learning models, similar trends were observed in actions taken by health care professionals in response to extracted adverse event signals; common actions were attempts to manage adverse event symptoms by symptomatic treatment or other mild interventions, including educational guidance from pharmacists or recommendations for patients to visit a doctor. More direct interventions focused on the causative drugs (ie, “dose reduction or discontinuation of anticancer treatment”) amounted to less than 5%; 7 (4.2%) for the HFS model and 6 (3.1%) for the AE-L model ( Figures 1 and 2 ). Thus, it appears that our deep learning models can contribute to screening mild to moderate adverse event signals that require preventive actions such as symptomatic treatments or professional advice from health care providers, especially for patients with less sensitivity to adverse event signals or who have few opportunities to visit clinics and pharmacies.

Ability to Catch Real Side Effect Signals of Anticancer Drugs

Based on the drug prescription history associated with S records extracted as HFS or AE-L positive, the type and duration of anticancer drugs taken by patients experiencing the adverse event signals were investigated. For the HFS model, the most common MoA of anticancer drug was antimetabolite (fluoropyrimidine: n=59, 38.8%), followed by kinase inhibitors (n=49, 32.2%, of which EGFR inhibitors and multikinase inhibitors accounted for n=28, 18.4% and n=14, 9.2%, respectively) and aromatase inhibitors (n=24, 15.8%; Table 3 ). It is known that fluoropyrimidine and multikinase inhibitors are typical HFS-inducing drugs [ 55 - 58 ], suggesting that the HFS model accurately extracted HFS side effect signals derived from these drugs. Note that symptoms such as acneiform rash, xerosis, eczema, paronychia, changes in the nails, arthralgia, or stiffness of limb joints, which are common side effects of EGFR inhibitors or aromatase inhibitors [ 59 , 60 ], might be extracted as closely related expressions to those of HFS signals. When looking at the MoA of anticancer drugs for patients with adverse event signals identified by the AE-L model, antimetabolite (fluoropyrimidine) was the most common one (n=53, 33.8%), as in the case of those identified by the HFS model, followed by kinase inhibitors (n=31, 19.7%) and antiandrogens (n=27, 17.2%; Table 5 ). Since the AE-L model targets a broad range of adverse event symptoms, it is difficult to rationalize the relationship between the adverse event signals and types of anticancer drugs. However, the type of anticancer drugs would presumably closely correspond to the standard treatments of the cancer types of the patients. Based on the prescribed anticancer drugs, we can infer that a large percentage of the patients had breast or lung cancer, indicating that our study results were based on data from such a population. Thus, a possible direction for the expansion of this research would be adjusting the deep learning models by additional training with expressions for typical side effects associated with standard treatments of other cancer types. To interpret these results correctly, it should be noted that we could not investigate anticancer treatments conducted outside of the pharmacies (eg, the time-course relationship with intravenously administered drugs would be missed, as the administration will be done at hospitals). To further evaluate how useful this model is in side effect signal monitoring for patients with cancer, comprehensive medical information for the eligible patients would be required.

Suitability of the Deep Learning Models for Specific Adverse Event Symptoms

Among the adverse event signals identified by the AE-L model, the type of symptom was categorized according to a predefined annotation guideline that we previously developed [ 43 ]. The most frequently recorded adverse event signals identified by the AE-L model were “pain or numbness” (n=57, 36.3%), “fever” (n=46, 29.3%), and “nausea” (n=40, 25.5%; Table 6 ). Since the pharmaceutical care records had information about interventions by health care professionals, the frequency of the presence or absence of the interventions for each symptom was examined. A trend toward a greater proportion of interventions was observed in “fever,” “nausea,” “diarrhea,” “constipation,” “vomiting,” and “edema” ( Figure 3 , black boxes). There seem to be 2 possible explanations for this: these symptoms are of high importance and require early medical intervention or effective symptomatic treatments are available for these symptoms in clinical practice so that medical intervention is an easy option. On the other hand, a trend for a smaller proportion of adverse event signals to result in interventions was observed for “pain or numbness,” “fatigue,” “appetite loss,” “rash or itchy,” “taste disorder,” and “dizziness” ( Figure 3 , gray boxes). The reason for this may be the lack of effective symptomatic treatments or the difficulty of judging whether the severity of these symptoms justifies medical intervention by health care providers. In either case, there may be room for improvement in the quality of medical care for these symptoms. We expect that our research will contribute to a quality improvement in safety monitoring in clinical practice by supporting adverse event signal detection in a cost-effective manner.

Potential for Deep Learning Model Implementation in Society

Although we evaluated our deep learning models using pharmaceutical care records in this study, the main target of future implementation of our deep learning models in society would be narrative texts that patients directly write to record their daily experiences. For example, the application of these deep learning models to electronic media where patients record their daily experiences in their lives with disease (eg, health care–related e-communities and disease diary applications) could enable information about adverse event signal onset that patients experience to be provided to health care providers in a timely manner. Adverse event signals can automatically be identified and shared with health care providers based on the concern texts that patients post to any platform. This system will have the advantage that health care providers can efficiently grasp safety-related events that patients experience outside of clinic visits so that they can conduct more focused or personalized interactions with patients at their clinic visits. However, consideration should be given to avoid an excessive burden on health care providers. For instance, limiting the sharing of adverse event signals to those of high severity or summarizing adverse event signals over a week rather than sharing each one in a real-time manner may be reasonable approaches for medical staff. We also need to think about how to encourage patients to record their daily experiences using electronic tools. Not only technical progress and support but also the establishment of an ecosystem where both patients and medical staff can feel benefit will be required. Prospective studies with deep learning models to follow up patients in the long term and evaluate outcomes will be needed. We primarily looked at patient-authored texts as targets of implementation, but our deep learning models may also be worth using medical data including patients’ subjective concerns, such as pharmaceutical care S records. As this study confirmed that our deep learning models are applicable to patients’ concern texts tracked by pharmacists, it should be possible to use them to analyze other “patient voice-like” medical text data that have not been actively investigated so far.

Limitations

First, the major limitation of this study was that we were not able to collect complete medical information of the patients. Although we designed this study to analyze patients’ concerns extracted by the deep learning models and their relationship with medical information contained in the pharmaceutical care records, some information could not be tracked (eg, missing history of medical interventions or anticancer treatment at hospitals as well as diagnosis of patients’ primary cancers). Second, there might be a data creation bias in S records for patients’ concerns by pharmacists. For example, symptoms that have little impact on intervention decisions might less likely be recorded by them. It should be also noted that the characteristics of S records may not be consistent at different community pharmacies.

Conclusions

Our deep learning models were able to screen clinically important adverse event signals that require intervention by health care professionals from patients’ concerns in pharmaceutical care records. Thus, these models have the potential to support real-time adverse event monitoring of individual patients taking anticancer treatments in an efficient manner. We also confirmed that these deep learning models constructed based on patient-authored texts could be applied to patients’ subjective information recorded by pharmacists through their daily work. Further research may help to expand the applicability of the deep learning models for implementation in society or for analysis of data on patients’ concerns accumulated in professional records at pharmacies or hospitals.

Acknowledgments

This work was supported by Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research (KAKENHI; grant 21H03170) and Japan Science and Technology Agency, Core Research for Evolutional Science and Technology (CREST; grant JPMJCR22N1), Japan. Mr Yuki Yokokawa and Ms Sakura Yokoyama at our laboratory advised SN about the structure of pharmaceutical care records. This study would not have been feasible without the high quality of pharmaceutical care records created by many individual pharmacists at Nakajima Pharmacy Group through their daily work.

Data Availability

The data sets generated and analyzed during this study are available from the corresponding author on reasonable request.

Authors' Contributions

SN and SH designed the study. SN retrieved the subjective records of patients with cancer from the data source for the application of deep learning models and organized other data for subsequent evaluations. SN ran the deep learning models with the support of SW. SN, YY, and KS checked the adverse event signals for each subjective record that was extracted as positive by the models for hand-foot syndrome or adverse events limiting patients’ daily lives and evaluated the adverse event signal symptoms, details of interventions taken by health care professionals, and types of anticancer drugs prescribed for patients based on available data from the data source. HK and SI advised on the study concept and process. MS and RT provided pharmaceutical records at their community pharmacies along with advice on how to use and interpret them. SY and EA supervised the natural language processing research as specialists. SH supervised the study overall. SN drafted and finalized the paper. All authors reviewed and approved the paper.

Conflicts of Interest

SN is an employee of Daiichi Sankyo Co, Ltd. All other authors declare no conflicts of interest.

Performance evaluation of deep learning models.

Examples of S records and sample interview transcripts.

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Abbreviations

Edited by G Eysenbach; submitted 25.12.23; peer-reviewed by CY Wang, L Guo; comments to author 24.01.24; revised version received 14.02.24; accepted 09.03.24; published 16.04.24.

©Satoshi Nishioka, Satoshi Watabe, Yuki Yanagisawa, Kyoko Sayama, Hayato Kizaki, Shungo Imai, Mitsuhiro Someya, Ryoo Taniguchi, Shuntaro Yada, Eiji Aramaki, Satoko Hori. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 16.04.2024.

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

22 Big Data in Healthcare Examples and Applications

Companies are turning to big data to reimagine every aspect of healthcare.

Big data is being utilized more and more in every industry , but the role it’s playing in healthcare may end up having the greatest impact on our lives.  

Researchers, hospitals and physicians are turning to a vast network of healthcare data to understand clinical context, prevent future health issues and even find new treatment options. While there are many ways data is being used to impact healthcare, we’ve rounded up five areas — along with a few examples of companies and organizations working within each area — where big data is taking on some of the major challenges in healthcare.

Big Data in Healthcare Applications and Examples

Big data in healthcare applications.

• Cancer Research
• Disease Detection
• Population Health
• Pharmaceutical Research and Development
• Health Insurance Risk Assessment

Big Data and Cancer Research

Nearly all of us have been impacted by cancer in some way, and the search for new ways to combat the disease is in high gear.  Researchers in both the private and public sector are dedicated to everything from researching a cure to finding more effective treatment options. Big data has changed the way these researchers understand the disease, providing access to patient information, trends and patterns never accessible before. The following are just a few of the companies using big data to make headway in the fight against cancer.

Location: Chicago, Illinois

Tempus is building the largest library of molecular and clinical data in the world with the goal of providing medical professionals with more clinical context for each patient’s cancer case. The Tempus platform collects and organizes data from lab reports, clinical notes, radiology scans and pathology images, accelerating oncology research and helping physicians make more personalized and informed treatment plans.

Flatiron Health

Location: New York, New York

Flatiron Health utilizes billions of data points from cancer patients to enhance research and gain new insights for patient care. Their solutions connect all players in the treatment of cancer, from oncologists and hospitals to academics and life science researchers, enabling them to learn from each patient.

Oncora Medical

Location: Philadelphia, Pennsylvania

Oncora Medical is simplifying workflows for oncologists by blending machine learning, automation and big data into a single platform. With the company’s data analysis tools, oncologists can compile data and quickly add information to a patient’s health records. As a result, oncologists can review a patient’s radiology and pathology history faster, delivering more timely and personalized care to cancer patients. 

Related Reading Big Data in Education: 9 Companies Delivering Insights to the Classroom

Big Data and Early Disease Detection

Early detection for diseases and complications is crucial for successful treatment. Whether it’s cancer, multiple sclerosis or a number of other conditions, screenings and other exams are often vital in staying ahead of disease. Here are a few examples of companies leveraging big data to improve early detection of disease and complications in patients.

Location: Irving, Texas

Pieces is a cloud-based software company that collects data throughout the entire patient journey to improve both the quality and cost of care. The company’s flagship product, Pieces Decision Sciences, is a clinical engine that makes decisions and recommendations based on a variety of data such as lab results, vitals, and structured and unstructured data. The platform consistently works to identify possible interventions while also learning from clinical outcomes.

PeraHealth: The Rothman Index

Location: Charlotte, North Carolina

PeraHealth is the creator of the Rothman Index, a peer-reviewed, universal scoring system for the overall health of a patient. The score takes the data within electronic health records, vitals, lab results and nursing assessments to assign a score. The scores are provided in a visual graph and updated in real time to identify changes and keep track of the details, helping patients avoid complications.

Prognos applies artificial intelligence to clinical data and manages Prognos Factor — a hub for multi-sourced diagnostic data. Their AI platform helps physicians apply treatments earlier, displays clinical trial opportunities, suggests therapy options and exposes care gaps for more than 30 conditions.

Big Data and Population Health

Different from public health, which focuses on how society can ensure healthier people, population health studies the  patterns and conditions that affect the overall health of groups . Big data is an essential part of understanding population health because without data, patterns are difficult to pinpoint. The following are just a few examples of companies that are aggregating and organizing data to help healthcare organizations and researchers identify the patterns that can improve health conditions.

Location: Fully Remote

Arcadia ’s big data platform provides organizations throughout the healthcare landscape with actionable insights that enable them to “make more strategic decisions in support of their financial, clinical, and operational objectives.” In the area of population health management, for example, Arcadia’s analytics capabilities make it possible to identify and overcome care gaps.

Amitech Solutions

Location: Creve Coeur, Missouri

Amitech Solutions applies data to the health field in multiple ways, from modern data management to healthcare analytics. Specifically, Amitech utilizes data for population health management solutions, combining physical and behavioral health data to identify risks and engage patients in their own healthcare.

Linguamatics

Location: Marlborough, Massachusetts

Linguamatics mines the untapped, unstructured data in electronic health records for research and solutions in population health. By using natural language processing , Linguamatics can use unstructured patient data to identify lifestyle factors, build predictive models and detect high-risk patients.

Socially Determined

Location: Washington, D.C.

Socially Determined takes a more holistic approach to population health by supplying healthcare organizations with social risk intelligence. The company’s platform SocialScape measures factors such as patients’ access to housing, transportation and food. Healthcare groups can then craft their strategies around these variables to deliver tailored care to specific populations.

Big Data and Pharmaceutical Research

Whether it be vaccines, synthetic insulin or simple antihistamines, medicines produced by the pharmaceutical industry play an important role in the treatment of disease. New drug discovery and creation depends on data to assess the viability and effectiveness of treatments. The following companies are using big data to help enhance pharmaceutical companies with research and development .

Location: San Mateo, California

Evidation has a mobile app that rewards users for healthy behaviors, provides access to health insights and offers opportunities to contribute to health research. Through Evidation, researchers can access everyday health data that informs their work and enables them to discover new ways of diagnosing, treating and managing various medical conditions. The company prioritizes giving app users control over their data, asking them for consent before their data can be accessed.

Location: Durham, North Carolina

IQVIA builds links between analytics, data and technology, so pharmacy leaders can complete faster and more effective clinical research. Besides boasting a dense healthcare database , the company leverages AI and machine learning to pinpoint the ideal patients for specific trials. Pharmacists can then run decentralized trials, compile data with IQVIA’s devices and jumpstart the research and development process.

Kalderos is challenging the cost of pharmaceuticals through its drug discount management platform, which collects data from multiple sources and stakeholders to improve transparency among patients. Drug manufacturers, covered entities and payers can use the platform to collaborate too. The company hopes to promote trust and equity within the healthcare industry.

Location: Santa Clara, California

Although now part of the Cloudera family due to a merger , the Hortonworks data platform continues to help pharmaceutical companies and researchers gain a better view of pharmaceutical data. Because billions of records are integrated and made accessible, companies can answer questions that weren’t possible before. This sparks more effective research for clinical trials, improved safety, faster time to market and better health outcomes .

Location: Iselin, New Jersey (U.S. office)

Innoplexus is the creator of the iPlexus discovery tool that organizes millions of publications, articles, dissertations, thousands of clinical trials, drug profiles and congress articles into a concept-based research platform. The tool helps pharmaceutical companies find the relevant information needed for research and new drug discovery.

Related Reading 9 Examples of Big Data in Media and Entertainment

Big Data and Health Records

When it comes to healthcare and specifically health insurance , risk is often a large contributing factor in how patients access care. The following are a few examples of companies using big data to gain more insight into risk and ensure accuracy in adjustments.

Blubyrd is designed to help surgical facilities and clinical practices compile and exchange data efficiently and securely. This data includes appointment schedules, procedure codes and equipment inventory.

Avaneer Health

Avaneer Health works to improve the efficiency of data flow in the healthcare industry by giving network participants access to administrative help and secured transactions. Founded in 2020 by a collective of top healthcare industry leaders — including CVS, Anthem, Cleveland Clinic and more — the company’s platform relies on blockchain.

Particle Health

Particle Health makes an API platform that brings together patient records into a single secure place. With a simple query, developers can access clean and actionable data sets to use. The goal is for healthcare providers to use the data to make more meaningful recommendations to their patients.

Upfront Healthcare

Upfront Healthcare ’s software platform uses data-driven personalization to improve communications between healthcare professionals and patients. For example, Upfront collects data — such as patient-reported outcomes, behavioral patterns, psychographic segments — and uses it to deliver relevant and timely messages to patients, whether it’s a reminder or a call to action.

Human API streamlines the underwriting process by allowing teams to sift through detailed electronic health records. A health intelligence platform reviews patients’ health backgrounds with automated features and pinpoints any underlying conditions. This workflow reduces the time it takes to complete each application, leading to higher placement rates, larger volumes of applicants and improved customer experiences.

Apixio ’s data acquisition technology wrangles medical data from millions of files, claims, PDFs and other health records. With this information, Apixio’s coding application provides more accurate risk adjustment for healthcare providers.

Health Fidelity

Health Fidelity helps healthcare providers and institutions find risks normally concealed in clinical charts. Their technology uses natural language processing to extract 100 percent of data within clinical charts and identify problems in care, assessment and documentation, which provides improved visibility for risk adjustment.

Rose Velazquez contributed reporting to this story.

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New AMA report card reveals uncomfortable truths about Australia’s health system

Planned surgery wait times in Australia’s public hospitals are now the longest on record, a new AMA report card has found.

Australia’s health ministers have been presented with grim evidence of the country’s public hospital logjam ahead of their crucial meeting today.

The AMA has released its annual Public Hospital Report Card, showing planned surgery wait times in Australia’s public hospitals are now the longest on record, and emergency departments remain strangled by access block.

The AMA wrote to all health ministers ahead of their meeting, saying the findings highlight the need for all governments to take further action against the logjam.

AMA President Professor Steve Robson said the annual report card, first published in 2007, clearly demonstrated the need for urgent action on top of existing planned changes to the National Health Reform Agreement (NHRA).

“Last year we welcomed the federal government’s announcement of a significant public funding boost to the hospital funding agreement and the decision to replace the 6.5 per cent funding growth cap with a more generous approach,” Professor Robson said.

“This announcement followed tireless AMA advocacy for funding reform, but it doesn’t come into effect until 2025. Urgent action is needed now.”

The report card found Australians are now waiting almost twice as long on average for planned surgery than they were 20 years ago, while the national proportion of individuals receiving category two planned surgeries on time has again fallen to the lowest point of record.

Emergency departments, too, remain under stress as they are strangled by access block.

Read the Public Hospital Report Card

Read the AMA’s public hospital federal budget submission

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• J Maxillofac Oral Surg
• v.10(3); 2011 Sep

Management of Medical Records: Facts and Figures for Surgeons

1 Department of Oral and Maxillofacial Surgery, M.M. College of Dental Sciences & Research, M.M. University, Mullana, Ambala, Haryana India

Deepika Bali

2 Department of Periodontics, DAV (C) Dental College, Yamuna Nagar, Haryana India

Nageshwar Iyer

Meenakshi iyer.

3 Department of Periodontics, M.M. College of Dental Sciences and Research, M.M. University, Mullana, Ambala, Haryana India

Medical records are the document that explains all detail about the patient’s history, clinical findings, diagnostic test results, pre and postoperative care, patient’s progress and medication. If written correctly, notes will support the doctor about the correctness of treatment. Inspite of knowing the importance of proper record keeping in India, it is still in the initial stages. Medical records are the one of the most important aspect on which practically almost every medico-legal battle is won or lost. This article discusses the various aspect of record maintenance.

Introduction

A good medical record serves the interest of the medical practitioner as well as his patients. It is very important for the treating doctor to properly document the management of the patient under his care. Medical record keeping has evolved into a science. The key to dispensability of most of the medical negligence claim rest with the quality of the medical records. Record maintenance is the only way for the doctor to prove that the treatment was carried out properly. Medical records are often the only source of the truth. They are likely to be far more reliable than memory.

The management and preservation of the hospital records in Indian context present a very gloomy picture. Despite the intensive effort at national and international level, the fundamental health care needs of the population of the developing countries are still unmet. The lack of basic health data renders difficulties in formulating and applying a rational for the allocation of limited resources that are available for patient care and disease prevention.

It is recommended that more efforts should be made by the institutions/hospital managements, all clinicians and medical record officer to improve the standard of maintenance and preservation of medical records. In this article, we are discussing the various aspects of the medical record management.

Objectives of Maintaining Medical Records

• Monitoring of the actual patient
• Medical research
• Medical/dental or paramedical education
• For insurance cases, personal injury suits, workmen’s compensation case, criminal cases, and will cases
• For malpractice suits
• For medical audit and statistical studies

Altering Medical Records

• While writing the medical notes, as far as possible do not overwrite. If the change is needed, strike the whole sentence. Do not leave ambiguity. Make a habit of signing if change is made. Preferably put the date and time below the signature. Attempting to obliterate the erroneous entry by applying the whitener or scratching through the entry in such a way that the person cannot determine what was written originally written raises the suspicion of someone looking for negligent or inappropriate care [ 1 ].
• Do not alter the notes retrospectively. If something written was inaccurate, misleading or incomplete then insert an additional note as a correction [ 2 ].
• Entries in a medical record should be made on every line. Skipping lines leave the room for tampering with the records [ 1 ].
• Amend on electric record by striking through rather than deleting and overwriting the original entry. After inserting the new note, add date, time and doctor name [ 3 ].
• Correction of the personal identification data of the patient like name, age, father/husband name, and address should only be made on the basis of affidavit attested by notary or 1st class magistrate [ 3 ].

Who has Access to Medical Records?

• Medical records are the property of the hospital or patient’s medical practitioner. It is a confidential communication of the patient and cannot be released without his permission [ 1 ].
• All patients have right to access their records and obtain copy of those records [ 1 ].
• Patient’s legal representative has the right to those records as long as patient has signed a release of records to accompany any request from the legal representative [ 4 ].
• Other health care providers have the right to the records of the patient, if they are directly involved in the care and treatment of the patient [ 4 ].
• Parents of a minor also have access to patient’s medical records [ 4 ].
• Medical records are usually summoned in a court of law in certain cases like-road traffic accident, medical negligence, insurance claim etc. [ 2 ].
• The impersonal documents have been used for research purposes as the identity of the patient is not revealed. Though the identity is not revealed, the research team is privy to patient records and a cause of concern about the confidentiality of the information. Recently a need has been felt to regulate the need of medical research, effectively restricting the manner in which this type of research is conducting. An ethical review is required for using the patient’s data [ 3 ].

Release of Records

• Request for medical records by patient or authorized attendant should be acknowledged and documents should be issued within 72 h [ 3 ].
• Maintain the register of certificates with the detail of medical records issued with at least one identification mark of the patient and his signature [ 5 ].
• Effort should be made to computerize the records for quick retrieval [ 2 ].
• Certain document must be given to the patient as a matter of right. Discharge summary, referral notes, or death summary are important document for the patient. Therefore, these documents must be given without any charge for all including patients who discharge themselves against medical advice [ 3 ].
• Doctors are not under any obligation to produce or surrender their medical records to the police in the absence of valid court warrant [ 6 ].
• A subpoena to produce clinical records is a form of court order. Failure to comply is in contempt of court and may be punished. Medical records which are subpoenaed are to be made over to the court and not to the solicitor who sought the subpoena [ 6 ].

Care while Issuing certain Medical Records

Prescription.

The prescription should be preferably on the OPD slip of the institution or on the letter pad of the doctor. Drug company or chemist prescription pad should never be used. Prescription must contain—patient’s name, age, sex, address and institution/hospital name. Prescribed drug should be preferably in capital letter or else clearly visible. One should mention its strength (especially in paediatric age group), its dose frequency, duration in days, and total quantity (number of tablets and capsules). Below the main drug, also mention other instructions of precautions and what to avoid. If any investigation is advised, do not forget to mention it on the prescription slip and call the patient after the investigation. If patient fails to keep follow up date and if then some complication occurs, then patient is also considered negligent (contributory negligence) [ 1 ].

All reports i.e. lab investigation, X-ray reports, ultrasound reports, computed tomography (CT-scan)/magnetic imaging resonance (MRI) reports, and histo-pathological reports should be issued by a qualified person. Biopsy report should preferably be issued in duplicate so that the referring doctor/hospital can keep the original copy. If the pathologist does not give a duplicate copy the referring doctor should get it xeroxed and should be handed over to the patient.

Referral Notes

Always keep the carbon copy of referral note especially in case of critically ill patient. Referral note should mention the date and time of writing the note. Also write the treatment given.

Discharge Card

Consultant in-charge should himself fill or supervise the discharge card. Condition of the patient on the admission, investigation done, the treatment given and detail advice on discharge should be written on discharge card. Operation notes if mentioned have to be correct otherwise just mention the name of the operation and give separate note in detail if asked for. If any complication is expected after discharge ask the patient to report immediately. Instructions while discharge must be very clear and elaborative. Keep in mind that abbreviations may not be understood by others. Also do not use code messages, sarcasm or poor opinion to the patient.

Certificates

A medical certificate is defined as a document of written evidence vouching for the truth of a fact as determined by the doctor issuing such a document. If medical certificate is admitted in a court of law as evidence and is proved to be false, the issuing doctor is liable for punishment. While issuing a medical certificate following things should be kept in mind,

• Medical certificate should be on institution/doctor letter pad.
• Date, time, and place should be mentioned.
• Issue it only for legitimate purpose and only when necessary.
• It has to be true and clear without any ambiguity.
• There should be an identification mark of the patient, preferably a thumb impression.
• Period of illness should be clearly mentioned.
• Diagnosis disclosure of the diagnosis should be only after the patient’s express consent, unless required by the law
• Doctor should maintain the duplicate copy of every certificate.

How Long to Maintain the Records

• Ideally records of adult patient are maintained for 3 year.
• 21 year for neonatal patient (3 + 18 year).
• For children 18 year of age + 3 year.
• For mentally retarded patient forever till hospital/institution is working.
• From income tax point of view for 7 years.

How to Destroy the Records

• Public notice of destroying the records in English news paper and in one vernacular paper mentioning the specific date up to which destruction will be sought [ 1 ].
• Give a time limit of 1 month for taking away records for those who want the records with written consent [ 1 ].
• Where litigation is going on.
• Where future trouble is expected.
• Mentally ill or retarded patient.
• Pre-litigation process of notice exchange is going on.

Hard Copy Only

Computers are now widely used in institution/hospitals for electronic patient records but still hard copy is required for following documents [ 1 ]

• Consent need to be on hard copy.
• Referral to doctor need hard copy.
• Police case need hard copy.
• Certificate of fitness should be on hard copy.

Problem of Record Management

There are many problems faced by institution/hospital for the proper maintenance of the records. 1. Constant revision of the outdated form is needed [ 2 ]. 2. Always trained personnel are needed for the maintenance [ 2 ]. 3. Inactive records need storage at appropriate place [ 7 ]. 4. There must be a need of determination of record retention [ 7 ]. 5. Unwanted records must be destroyed [ 8 ]. 6. Record storage entail into 2 stages. A. Moving the records from active to inactive file and from there to storage room. B. Destruction and disposal of the unimportant records [ 8 ].

There are various type of damage which may be found in paper documentation like-aged paper may become weak, colour alteration from white to yellow, dirt and dust may be present on the surface, insect and fungus is a big threat for the records, if paper is kept folded, it may become weak at the crease, dampness and water leakage in storage room also destroy the paper.

Proper Preservation of the Medical Records

Collect all the records and classify them according to the different section [ 7 ]. Protect the records from insect attack. Spray insecticide or place naphthalene balls over shelves to preserve the records. Plan a periodical checking for the records [ 3 ]. Proper care should be observed while handling the records. Fire extinguisher should be available in record room. Protect all records from dampness, water, and from hot and dry climate [ 8 ]. Records should be kept in dust free area. Windows and ventilators should be properly covered with frames as safeguard against sabotage. Destroy the records as per the regulation established for retention of records.

Medical records form an important part of a patient management. It is important for the doctor and medical establishment to properly maintain the records of the patient for 2 important reasons. First one is that it helps in proper evaluation of the patient and to plan treatment protocol. Second is that the legal system relies mainly on documentary evidence in cases of medical negligence. Therefore, medical records should be properly written and preserved to serve the interest of doctor as well as his patient.