The use of Electronic Health Records to Support Population Health: A Systematic Review of the Literature

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Abstract

Electronic health records (EHRs) have emerged among health information technology as “meaningful use” to improve the quality and efficiency of healthcare, and health disparities in population health. In other instances, they have also shown lack of interoperability, functionality and many medical errors. With proper implementation and training, are electronic health records a viable source in managing population health? The primary objective of this systematic review is to assess the relationship of electronic health records’ use on population health through the identification and analysis of facilitators and barriers to its adoption for this purpose. Authors searched Cumulative Index of Nursing and Allied Health Literature (CINAHL) and MEDLINE (PubMed), 10/02/2012–10/02/2017, core clinical/academic journals, MEDLINE full text, English only, human species and evaluated the articles that were germane to our research objective. Each article was analyzed by multiple reviewers. Group members recognized common facilitators and barriers associated with EHRs effect on population health. A final list of articles was selected by the group after three consensus meetings (n = 55). Among a total of 26 factors identified, 63% (147/232) of those were facilitators and 37% (85/232) barriers. About 70% of the facilitators consisted of productivity/efficiency in EHRs occurring 33 times, increased quality and data management each occurring 19 times, surveillance occurring 17 times, and preventative care occurring 15 times. About 70% of the barriers consisted of missing data occurring 24 times, no standards (interoperability) occurring 13 times, productivity loss occurring 12 times, and technology too complex occurring 10 times. The analysis identified more facilitators than barriers to the use of the EHR to support public health. Wider adoption of the EHR and more comprehensive standards for interoperability will only enhance the ability for the EHR to support this important area of surveillance and disease prevention. This review identifies more facilitators than barriers to using the EHR to support public health, which implies a certain level of usability and acceptance to use the EHR in this manner. The public-health industry should combine their efforts with the interoperability projects to make the EHR both fully adopted and fully interoperable. This will greatly increase the availability, accuracy, and comprehensiveness of data across the country, which will enhance benchmarking and disease surveillance/prevention capabilities.

Keywords: Electronic health records (EHR), Outcomes, Population health, Public health

Introduction

Background

Healthcare Information Technology (HIT) is changing how the healthcare industry operates and has already began to reduce waste and help improve health outcomes [1]. A major component of HIT is the Electronic Health Record (EHR). We used the definition of the EHR from the Center of Medicaid and Medicare Services (CMS): Electronic health records are digital forms of patient records that include patient information such as personal contact information, patient’s medical history, allergies, test results, and treatment plan [2]. Some benefits of EHRs include improving efficiency, increasing positive patient outcomes, and population health. 1 Potential improvements in population health include EHRs ability to organize and analyze a large amount of patient information. This is particularly pertinent since the Public Health Data Standards Consortium (PHDSC) and the Center for Disease Control (CDC) completed its project to standardize public health case reports in accordance with HL7 [3]. This project in 2012 is one example of many ongoing efforts to establish data standards in support of the public health and the EHR.

Population health is “the health outcomes of a group of individuals, including the distribution of such outcomes within a group” [4]. and EHRs provide access to public health data to survey the population for potential health improvements or act as a safety net for potential health threats. 5 A new program called “DiSTRIBuTE” that uses the EHRs in the surveillance of population health issues [5], and recent use found that electronic health records were better able to track “weekly influenza trends on an ongoing basis better than and in a “more timely than manual reporting from sentinel providers” [5]. Distributed Surveillance Taskforce for Realtime Influenza Burden Tracking and Evaluation (DiSTRIBuTE), run by the International Society for Disease Surveillance (ISDS), collects aggregated data by age group to improve decision making on public safety, cost, quality, and outcomes. This distributed-data is collected, analyzed, and interpreted in real time. Privacy of information is managed by the Fair Information Practice Principles (FIPPs), and the de-identified data is shared electronically to address specific population-health-related questions. The CDC in 2009 to support the tracking of the H1N1 pandemic, among other examples. EHRs can provide additional screening of health records beyond surveillance that can lead to additional research [5]. Public health surveillance observes a population and brings attention to various health threats or monitors the general health of the population [6]. There is even a positive correlation between the use of EHRs by primary care providers and the ability to accurately report to public health officials [7].

Utilizing and incorporating Electronic Health Records in surveillance and care interventions can help aid the health of the population it serves. Many of these studies have shown significant positive effects of EHRs interaction with public health. Previous research shows how EHRs are being used to surveil various populations, and some review other countries’ use of EHRs for surveillance [8]. Some positive effects that were observed included better surveillance of infectious diseases, improved management of patients with chronic diseases, and identify populations with higher risk factors [8]. The recent shifts in healthcare policy such as The ACA have recommended health practices to focus on preventive care to improve the overall health of the population [1]. Shih and De Leon discovered that physicians who implemented EHRs were better able to deliver recommended preventive care into their practices for low-income populations [9]. Electronic health records have been implemented to provide more coordinated and patient-centered care. EHR implementation in the ICU significantly reduces the central line associated bloodstream infections and surgical intensive care unit mortality rates [10]. EHRs provide secure access to patient information resulting in positive outcomes in relations to quality of care and productivity [11]. EHR systems have been used to manage chronic disease like diabetes, and it has been found that regular use of the EHR can reduce fragmentation of data and increase continuity of care between providers if the providers participate in health information exchanges [12]. EHRs in the emergency department (ED) improve medical decision making when using a decision tree; It increases the patient’s quality of life, and it was found to be cost-effective [13]. Another cost benefit assessment for using electronic health records for data showed promising results [14]. The European Electronic Health Records for Clinical Research (EHR4CR) has developed an innovative platform that is capable of transforming traditional research processes appeared to be highly beneficial by reducing the actual person-time, operational costs, or average cycle time for Phase II-III clinical trials when compared to current practices in a pre-launch environment [14].

Other studies have illuminated possible barriers to the success of EHRs. Some of these barriers include lack of interoperability, errors in medical information, and the financial resources that are required to accommodate HIT. Medical errors may still occur despite the increase of information being gathered from patients with the use of EHR [15]. Patients who received medical and surgical care showed same outcomes in six diverse states independent of the use of EHRs. No specific benefits in patient outcomes were related to EHRs [16]. Patient satisfaction can be adversely affected by the EHR due to a decrease in attention that a physician exhibits while making notes in the system [17]. Adoption of the EHRs is not without obstacles; however, results of the research is mixed on whether a proper implementation of an EHR could improve the operations of population health.

Objectives

The purpose of this study is to review the literature previously published on the effects of EHRs on population health. Health Information Technology is becoming more widely utilized, however, the industry has still not been able to achieve its overall accessibility. It is our goal to answer whether the use of electronic health information can play a vital role in improving the health of populations, as well as identify key inhibitors to its adoption and/or key use.

Methods

The articles used for this systematic review were gathered and compiled using PubMed (MEDLINE complete) and The Cumulative Index to Nursing and Allied Health Literature (CINAHL). The search process is illustrated in Fig. 1 . The United States National Library of Medicine’s Medical Subject Headings (MeSH) was used to find the key terms related to our topic in PubMed. With the help of MeSH, we were able to identify the appropriate sub-headings under the key terms. Our final key terms in the search process for both databases were “EHR” “electronic health record” “EMR” “electronic medical record” and “population health” or “public health”. While these terms have distinct definitions from each other, they are often used synonymously. We included both so that the search would be more exhaustive. In accordance with good research practice, we also included Boolean operators and quotation marks in the search string. The initial search in PubMed and CINAHL resulted in 1491 and nine items, respectively. We chose a timeframe of five years to keep the grouping small enough for reasonable analysis. After filtering relevant time frame academic journals, English only, and other peer review selection processes, we were left with 420 articles. Our process was to divide up these 420 abstracts between reviewers in a way that ensured each abstract was read by at least 2 reviewers. We independently assessed the relevance of each abstract in an Excel workbook and then combined the assessments during a consensus meeting. During this meeting we resolved any conflict in the assessments (germane or not germane to our research) to reach a final grouping of 55 articles for full analysis. A Kappa statistic of .83 was calculated, which demonstrates strong agreement among the reviewers, as well as consistency in reading and initial analysis of suitability. The same process was repeated for analysis of the articles that was used for analysis of the abstracts. Independent observations were recorded and later combined for a consensus meeting. During this second round, reviewers were also asked to pay attention to the references of each article to identify salient resources that may not have been caught by our search. This search did not result in any additional articles added to the group analyzed (n = 55).

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Literature Search with inclusion and exclusion criteria

During the second consensus meeting, reviewers shared their observations of facilitators and barriers to adoption of the EHR for managing public health. Through this process, reviewers categorized and grouped their observations in logical manner. An additional read of the articles took place to identify bias and limitations. These were shared in a third and final consensus meeting.

Results

The results of our analysis are listed in Table ​ Table1. 1 . This table includes the source article, the facilitators, barriers, bias, and limitations of the articles analyzed.

Table 1

Summary of articles analyzed

Author Facilitator / enabler for adoption Themes Barrier to adoption Themes Bias or limitation
Bailey, et al. [18]- Increased utilization of prevention / primary carePreventative Care- None identifiedNone identified- Limited sample from Oregon which means the results are not generalizable
- Disease preventionPreventative Care
- Utilization can be captured through EHR, even during dramatic upturnsData Management
- Improved data qualityQuality
- Improved workflowProductivity/ Efficiency
- Disease surveillanceSurveillance
- Improved timelinessQuality
Houser, et al. [19]- InteroperabilityInteroperability- Lack of fundingCost- Limited sample from Alabama conference
- Surveillance across all registries and all statesSurveillance- Lack of medical staff supportLimited staff support- Response bias
- Advancing epidemiologic researchData Management- Changing data standardsNo standards- Lack of resources
- Quality reportingQuality- Lack of full-time commitmentsCritical thinking/treatment decisions
- Clinical decision supportDecision support- Lack of standardized data exchangeNo standards
Metroka, et al. [20]- Improved efficiencyProducivity/ Efficiency- Records may be missing dataMissing data- Limited external validity: This study was restricted to immunizations
- Ease of useEase of use
- Data sharingData Management
Blecker, et al. [21]- Improved qualityQuality- Data contains errors - Limited external validity: Data only collected at one institution.
- Ease of data collectionData ManagementMissing data / data error
- Ability to measure intensity of careProductivity/ Efficiency
Flood, et al. [22]- SurveillanceSurveillance- Missing dataMissing data / data error- Measurement error can be mitigated with training.
- Disease preventionPreventative Care- Human error in measurementMissing data / data error- Interrater reliability between systems needs to be measured and controlled.
- EHR samples are convenience samples which may not be representative of the population.
Martelle, et al. [23]- Improved accessibilityProductivity/ Efficiency- Few incentivesCost- Small sample leads to low statistical power which reduces the external validity.
- Improved quality of careQuality- Few inmates have email which reduces the demand for a patient portal.Technology complex- External validity limited: Study conducted in the correctional setting.
- Financial assistanceFinancial Assistance
- InteroperabilityInteroperability
Chambers, et al. [24]- Improvement to qualityQuality- None identifiedNone identified- Selection bias
- SurveillanceSurveillance- Gender bias
- Access to primary care information provides tailored quality improvement initiativesProductivity/ Efficiency- External validity limited because the gender/race demographics of the sample are not representative of the U.S.
Moody-Thomas, et al. [25]- Improved primary careQuality- No independent method for determining the quality of dataNo standards- Quasi-experimental
- Intervention effective in lowering the prevalence of tobaccoHealth Outcomes- Patient-reported behavior on bad behavior can be tempered to avoid uncomfortable discussions.
- Disease preventionPreventative Care- No similar group exists for comparison of results.
- SurveillanceSurveillance
Vogel, et al. [26]- Sustainability and generalizabilityQuality- Human errorHuman error- The voluntary nature of the Massachusetts League of Community Health Centers can create a fluid status of participating offices, which can also create orphaned data for queries.
- Health outcomesHealth Outcomes- Data is typically missing or incompleteMissing data / data error
- Data managementData Management- Data errorMissing data / data error
- ProductivityProductivity/ Efficiency
Calman, et al. [7]- Improve surveillance and management of chronic diseaseSurveillance- CostCost- Not all primary-care entities cooperate and share with public health entities.
- EfficiencyProductivity/ Efficiency- No central agency mandating cooperation of public health with primary care entities.No standards
- InteroperabilityInteroperability
- Decisions about treatmentDecision support
- Disease preventionPreventative care
Duan, et al. [27]- None identifiedNone identified- Electronic system failuresProductivity loss- Information bias caused by misclassification of errors.
- Inaccurate data (data errors)Missing data / data error
- ComplexityTechnology complex
Kawamoto, et al. [28]- Disease preventionPreventative care- None identified - Quasi experimental
- Improved productivityProductivity/ efficiencyNone identified- Control group comparison data were created using a model.
- Improved efficiencyProductivity/ efficiency
Behrens, et al. [29]- SurveillanceSurveillance- None identifiedNone identified- Binning, as is common in Monte Carlo simulations, can cause bias in data.
- Machine logic was used for best fit.
Cross, et al. [30]- Support care coordinationCommunication- InteroperabilityNo standards- Sample restricted to the state of Michigan.
- Increased productivityProductivity/ efficiency- CostCost
- Data managementData management- Total adoption is a barrier because some physicians don’t want to adopt unless referrals will have the technology.Resistance to change
- Technology is up to dateCurrent technology- EHRs can often obscure relevant information.Missing data / data error
Tanner, et al. [31]- Patient safety for medicationsQuality- Fear of unintended consequences from EHRs.Missing data / data error- Selection bias: Only pre-meaningful use era adopters were queried.
- InteroperabilityInteroperability - Does not address causation
- Improved productivityProductivity/ efficiency
Emani, et al. [32]- Decrease medical errorsQuality- Resistance to changeResistance to change- Study was limited to two academic medical centers in one region.
- Physician satisfactionSatisfaction - Did not include factors such as practice size.
- Self-efficiencyProductivity/ efficiency
Benkert, et al. [33]- Overall positive impact overtimeSatisfaction- Data failures/ challengesProductivity loss- Factors beyond the EHR that can affect poor outcomes were not measured.
- Improved productivityProductivity/ efficiency - Data quality bias with level of user experience with the EHR.
- Improved data collectionData management - Neither time lags nor staggered time points were measured or controlled.
Merrill, et al. [34]- Improved efficiencyProductivity/ efficiency- Structural limitationLimited staff support- The registry database limited comparison of EHR-submitted vs non-EHR submitted data.
- Improved productivityProductivity/ efficiency- Missing dataMissing data / data error
- Improved complianceDecision support
- Disease preventionPreventative care
Glicksberg, et al. [35]- Disease preventionPreventative care- None identifiedNone identified- External validity: One study group was not representative of the population.
McAlearney, et al. [36]- Consistent communicationCommunication- Productivity loss during implementationProductivity loss- Small sample size greatly reduces statistical power and external validy.
- Careful planningProductivity/ efficiency- Resistance to changeResistance to change
- System failureProductivity loss
- Poor computer skillsLimited staff support
- Slow queriesProductivity loss
Polling, et al. [37]- Data collectionData management- Missing dataMissing data / data error- Not all data in the set could be matched with a record due to anonymity requirements. This limited the ability to compare data between records, and therefore limited the number of data points that were analyzed. These data points could have been dramatically different than those in the comparison.
- Disease preventionPreventative care
Zhao, et al. [38]- Data collectionData management- InteroperabilityNo standards- Limited validity and reliability
Roth, et al. [39]- Data collectionData management- InteroperabilityNo standards- Data error was controlled by removing records that contained implausible values. This may have skewed the data because, while implausible, the data could have described an unusually sick population.
- SurveillanceSurveillance- Prone to data-entry errorMissing data / data error- Free-text fields are inherently difficult to include in analysis. The data contained within free-text fields may have skewed the results differently.
- Missing dataMissing data / data error- Without a time-series or longitudinal study, it is difficult to generalize the results.
Barnett, et al. [40]- None identifiedNone identified- none identifiedNone identified- The small sample of 17 hospitals reduces statistical power which may limit the generalizability of the results.
- Researchers unable to explore the associataion of EHR implementation with inpatient outcomes stratified by implementation context, hospital, or EHR characteristics.
Drawz, et al. [41]- Improved performanceProductivity/ efficiency- Limited functionalityTechnology complex- The lack of nationwide data eliminates comparisons to a national benchmark.
- InteroperabilityInteroperability
- Improve measuring data (data collection)Data management
Thirukumaran, et al. [42]- None identifiedNone identified- Temporary decrease in qualityProductivity loss- Limited generalizability
Adler-Milstein J, Everson J, Lee SY. [43]- Increased qualityQuality- None identifiedNone identified- Adherence to process measurers was high across hospitals which reduces the opportunity to observe EHR-driven improvements.
- Increased efficiency for hospital careProductivity/ efficiency- This study only analyzed the Medicare arm of the Meaningful Use program.
- Patient satisfactionSatisfaction
- positive relationship between EHR adoption and performanceInteroperability
Ananthakrishnan, et al. [44]- Health outcomesHealth outcomes- Misclassification (data error)Missing data / data error- The cohort studied represents a small population: Therefore, the external validity of results are limited.
- Quality in documentationQuality- InteroperabilityNo standards- All provider notes may not have been captured if a participant saw a physician through a private setting.
- Lends generalizability to findingsProductivity/ efficiency
Carayon, et al. [45]- Increased productivityProductivity/ efficiency- Increased amount of time spent on documentation and clinical reviewProductivity loss- Not generalizable nationwide because data were collected at only one location.
- Efficiency gainsProductivity/ efficiency- Decreased direct patient care (quality)Decreased quality- Physicians were not identified, and therefore their contributions may have occurred both pre and post treatment. Having this information would have made analysis easier. This can introduce observer bias that has not been controlled for.
Redd, et al.- None identifiedNone identified- Negative impact on productivity and efficiencyProductivity loss- Face validity: Clinical volume is not an exact match for provider productivity, but other studies have used this measure.
[46]- Time consumingTechnology complex- Construct validity: Due to the lack of baseline data available, it is difficult to discern that the intended measure is accurate.
- Missing dataMissing data / data error
Jones & Wittie [47]- Widespread adoptionCurrent technology- Lacked functionalityAccessibility/ utilization- Limited external validity due to the uncertainty that Beacon communities across the country are homogeneous.
- Improved qualityQuality- ComplexityTechnology complex- Self-report data can be questionable, but sufficient research has been conducted using similar data, researchers felt comfortable.
- Care coordination (communication with data exchange)Interoperability
- Layering of financial incentivesFinancial assistance
- Technical assistanceCommunication
Hammermeister, et al. [48]- Data collectionData management- Missing dataMissing data / data error- Limited clinic-level data precludes comparison characteristics between hih and low outlier clinics.
- Inexpensive data collection (cost)Financial assistance- External validity is limited because the sample is not representative of the national population.
Benson, et al. [49]- Interoperability between EHR and primary care systemsInteroperability- Potential missing dataMissing data / data error- Infrequency of visits creates missing data.
- Efficient comparison of patientsProductivity/ efficiency- Some privacy concernsPrivacy concerns- Standardized measures for risk factors do not exist.
- Inability to conduct certain logistic functions (lack of functionality)Productivity loss- Self-report data can be unreliable.
Soulakis, et al. [50]- Communication between patients and providersCommunication- Complex analysisTechnology complex- The measure of interrater reliability is confounded because some providers served on many teams.
- Preventative carePreventative care
Burke, et al. [51]- Improved over quality of outpatient clinical notesQuality- Standards across EHRs - Not generalizable to all EHR systems because only one was studied.
- AccessibilityEase of useNo standards
- Improved efficiencyProductivity/ efficiency
Keck, et al. [52]- SurveillanceSurveillance- Limited design, deployment and function (complexity) - Construct validity is questionable due to lack of baseline data.
- Increased time availability (productivity)Productivity/ efficiencyTechnology complex- Generalizability limited because only the Indian Health System was studied.
- Improved data validity and reliabilityQuality
Roth, et al. [53]- SurveillanceSurveillance- Fail to capture important discrete necessary data (missing data)Missing data / data error- Selection bias due to a convenience sample.
- Reduce health disparities (health outcomes)Health outcomes- Lack of workflow integration paradigms(productivity)Productivity loss- Smoking data is inherently underreported, so the effects of this study are understated.
De Moor, et al. [54]- Reduce duplication and errorsQuality- Regional diversity in languages.No standards- None identified
- Data collectionData management- InteroperabilityNo standards
- Improved efficiencyProductivity/ efficiency- Inconsistent documentationMissing data / data error
- Data qualityDecreased quality
Chang, et al. [55]- None identifiedNone identified- Missing dataMissing data / data error- External validity limited: While the computed algorithm satisfactorily predicted one behavior, it is uncertain if such models can be developed for all.
Reed, et al. [56]- Increased/positive impact on critical thinking skillsDecision support- None identifiedNone identified- Response bias decreased the number of participants.
Inokuchi, et al. [57]- Productivity (reduced time)Productivity/ efficiency- No patient outcomes - Need larger sample size
- Increased physician satisfactionSatisfactionDecreased quality- The Hawthorne effect may have increased bias toward the new EMR.
- Increased use of informationDecision support - External validity may be questionable because only one EMR was studied.
- Organizational impactProductivity/ efficiency
Silfen, et al. [58]- Prompt healthcare providers to screen for chronic health issues (preventative care)Productivity/ efficiency- No return on investment - External validity limited because data were not available for all organizations and anything outside of New York City.
- Facilitate provider referralsCommunicationCost- Data were not complete
- Supplies rapid feedback to providersDecision support
- Track patient outcomesHealth outcomes
- Monetary/financial incentiveFinancial assistance
Zera, et al. [59]- None identified - No effect on the rates of diabetes screeningDisease management- Data bias may have skewed results toward the null result.
None identified- No access to screening responses (structural limitation)Missing data / data error- External validity limited: Small numbers in the control group reduces the statistical power.
- No patient outcomesDecreased quality
Baus, et al. [60]- SurveillanceSurveillance- The EHR is designed for patient care, not for research.Accessibility/ utilization- Not generalizable, sample bias: Clinics were chosen through purposive sampling.
- Preventative carePreventative care- Human error in recording data in the EHR.Human error- Unable to combine data for extrinsic information (structural limitation).
- Data quality for population health managementQuality
Baus, et al. [61]- Preventative carePreventative care- Difficulty of extracting necessary data (technical challenges).Technology complex- Limited variability in participants limits external validity.
- SurveillanceSurveillance- CostCost
- Improved efficiencyProductivity/ efficiency- InterroperabilityNo standards
- Improve decision supportDecision support
- Increase the application of patient data to careData management
- Improve health outcomesHealth outcomes
Haskew, et al. [62]- Real time access (efficiency)Productivity/ efficiency- CostCost- Limited external validity due to short time studied and difficulty of implementation model.
- Sharing data (communication)Communication- Limited staffLimited staff support
Puttkammer, et al., [63]- Preventative carePreventative care- Missing data - Self-report data is questionable and subject to ability to recall or social desirability.
- Data/information accessibilityEase of useMissing data / data error- Missing data that could have skewed the results.
- External validity limited because only two organizations studied.
Zheng, et al. 66]- SurveillanceSurveillance- Difficulty combining information from EHR with structured dataTechnology complex- External validity limited
- Data collectionData management
Wu, et al. [64]- Data collectionData management- Missing data on smoking status - Citation bias
- Smoking surveillanceSurveillanceMissing data / data error- Self-report data on smoking is limited due to social desirability, therefore the results of this study may be understated.
- Facilitating care identificationCommunication
Nguyen & Yehia [65]- Data collectionData management- Different documentation rates at Different healthcare systemsNo standards- External validity limited because only one health system in one region of the U.S. was studied.
- Preventative carePreventative care
Tomayko, et al. [66]- Data collectionData management- None identifiedNone identified- External validity limited because the demographics do not match that of the U.S.
- Preventative carePreventative care- Self-report data can be questionable and subject to bias due to recall and social desirability.
- Disease management/monitoring (child obesity)Surveillance
- Quality improvementQuality
- Greater surveillance of a populationSurveillance
- Cost effectiveFinancial assistance
Romo, et al. [67]- SurveillanceSurveillance- Data is often skewed toward those who seek care.Missing data / data error- Data bias: Missing values were filled with estimates which may skew the results.
- GeneralizabilityProductivity/ efficiency- Self-report data is questionable and subject to bias due to recall and social desirability.
- External validity limited to U.S. only.
Chambers, et al. [68]- Data collectionData management- None identifiedNone identified- Quasi experimental.
- Selection bias.
Wang, et al. [69]- Improved qualityQuality- None identifiedNone identified- External validity limited: Only 151 organizations studied, therefore generalizing outside those practices may be limited.
- Work flow variability (productivity)Productivity/ efficiency- Structural limitation
- Selection bias: Early adopters were selected for the study.
Chiang, et al. [70]- Increased faculty providersSatisfaction- Initial decrease in clinical volumeProductivity loss- Interrater reliability was controlled by using a stable group of providers.
- Longer notesCommunication- Increased time expenditure and documentation timesTechnology complex- Baseline data was established during a three-month period (Nov-Jan).
- More automatically generated texts (efficiency)Productivity/ efficiency- Increased reliance on textual descriptions and interpretations (human error)Human error- Construct validity limited because clinical volume may not be an equal measure of productivity.
- Little to no increase in clinical volumeProductivity loss- External validity limited: The only EHR studied was at a large academic medical center which may not be representative of all organizations in the U.S.

We examined the work of 414 authors and co-authors who published 55 works that discuss Electronic Health Records, Population and or Public Health. We identified a total of 232 factors, which consisted of 63% (147/232) facilitators and 37% (85/232) barriers. Utilizing EHRs resulted in a greater number of benefits than negative impacts to population health. During the review process, various aspects of electronic health records showed that the utilization of these HIT improves population and public health. Benefits of using electronic medical records describe how EHRs improved the productivity and efficiency of health organizations to better serve populations. Increased healthcare access to individuals provides more comprehensive documentation from the population from the surveillance of public health screening and preventative care. Electronic health records allow health professionals to share and incorporate more public health information among various providers. This improves the population’s ability to survey the populations for chronic disease, contagious infections, and allows for more rapid and uniform transference of patient information [7, 18–71]. The incorporation of new technology is expected to have some flaws associated with its integration into the healthcare field [7, 18–71]. Some of the major setbacks of EHRs and EMRs include a temporary decrease in productivity, while staff and medial personal incorporate and train employees to use an entirely new system. Alongside with new operational systems medical efforts, lack of functionality, system failures, and simple resistance to change by providers can occur. These can have negative impacts on public health as missing or incorrect information can be transmitted for surveillance. Other barriers include the inability to generalize one healthcare organization’s experience to others due to various types of EHRs and systems to the wide variety of populations and settings. Some healthcare populations have been found to be more accepting of EHRs while others have found it more difficult to incorporate them into a daily routine [1]. The authors were able to organize and examine these themes in the discussion section.

Additional analysis

Affinity matrices were created to further analyze facilitators and barriers. These matrices are illustrated in Table ​ Table2 2 .

Table 2

Affinity matrix of facilitators and barriers