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Journal of Medical Systems

, 42:214 | Cite as

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

  • Clemens Scott KruseEmail author
  • Anna Stein
  • Heather Thomas
  • Harmander Kaur
Open Access
Transactional Processing Systems
Part of the following topical collections:
  1. Transactional Processing Systems

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).
Fig. 1

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 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 care

Preventative Care

- None identified

None identified

- Limited sample from Oregon which means the results are not generalizable

- Disease prevention

Preventative Care

- Utilization can be captured through EHR, even during dramatic upturns

Data Management

- Improved data quality

Quality

- Improved workflow

Productivity/ Efficiency

- Disease surveillance

Surveillance

- Improved timeliness

Quality

Houser, et al. [19]

- Interoperability

Interoperability

- Lack of funding

Cost

- Limited sample from Alabama conference

- Surveillance across all registries and all states

Surveillance

- Lack of medical staff support

Limited staff support

- Response bias

- Advancing epidemiologic research

Data Management

- Changing data standards

No standards

- Lack of resources

- Quality reporting

Quality

- Lack of full-time commitments

Critical thinking/treatment decisions

 

- Clinical decision support

Decision support

- Lack of standardized data exchange

No standards

 

Metroka, et al. [20]

- Improved efficiency

Producivity/ Efficiency

- Records may be missing data

Missing data

- Limited external validity: This study was restricted to immunizations

- Ease of use

Ease of use

  

- Data sharing

Data Management

  

Blecker, et al. [21]

- Improved quality

Quality

- Data contains errors

 

- Limited external validity: Data only collected at one institution.

- Ease of data collection

Data Management

Missing data / data error

- Ability to measure intensity of care

Productivity/ Efficiency

 

Flood, et al. [22]

- Surveillance

Surveillance

- Missing data

Missing data / data error

- Measurement error can be mitigated with training.

- Disease prevention

Preventative Care

- Human error in measurement

Missing 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 accessibility

Productivity/ Efficiency

- Few incentives

Cost

- Small sample leads to low statistical power which reduces the external validity.

- Improved quality of care

Quality

- Few inmates have email which reduces the demand for a patient portal.

Technology complex

- External validity limited: Study conducted in the correctional setting.

- Financial assistance

Financial Assistance

   

- Interoperability

Interoperability

   

Chambers, et al. [24]

- Improvement to quality

Quality

- None identified

None identified

- Selection bias

- Surveillance

Surveillance

- Gender bias

- Access to primary care information provides tailored quality improvement initiatives

Productivity/ 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 care

Quality

- No independent method for determining the quality of data

No standards

- Quasi-experimental

- Intervention effective in lowering the prevalence of tobacco

Health Outcomes

- Patient-reported behavior on bad behavior can be tempered to avoid uncomfortable discussions.

- Disease prevention

Preventative Care

- No similar group exists for comparison of results.

- Surveillance

Surveillance

 

Vogel, et al. [26]

- Sustainability and generalizability

Quality

- Human error

Human 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 outcomes

Health Outcomes

- Data is typically missing or incomplete

Missing data / data error

- Data management

Data Management

- Data error

Missing data / data error

- Productivity

Productivity/ Efficiency

  

Calman, et al. [7]

- Improve surveillance and management of chronic disease

Surveillance

- Cost

Cost

- Not all primary-care entities cooperate and share with public health entities.

- Efficiency

Productivity/ Efficiency

- No central agency mandating cooperation of public health with primary care entities.

No standards

- Interoperability

Interoperability

- Decisions about treatment

Decision support

- Disease prevention

Preventative care

Duan, et al. [27]

- None identified

None identified

- Electronic system failures

Productivity loss

- Information bias caused by misclassification of errors.

 

- Inaccurate data (data errors)

Missing data / data error

 

- Complexity

Technology complex

Kawamoto, et al. [28]

- Disease prevention

Preventative care

- None identified

 

- Quasi experimental

- Improved productivity

Productivity/ efficiency

None identified

- Control group comparison data were created using a model.

- Improved efficiency

Productivity/ efficiency

 

Behrens, et al. [29]

- Surveillance

Surveillance

- None identified

None 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 coordination

Communication

- Interoperability

No standards

- Sample restricted to the state of Michigan.

 

- Increased productivity

Productivity/ efficiency

- Cost

Cost

 

- Data management

Data 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 date

Current technology

- EHRs can often obscure relevant information.

Missing data / data error

Tanner, et al. [31]

- Patient safety for medications

Quality

- Fear of unintended consequences from EHRs.

Missing data / data error

- Selection bias: Only pre-meaningful use era adopters were queried.

- Interoperability

Interoperability

  

- Does not address causation

- Improved productivity

Productivity/ efficiency

   

Emani, et al. [32]

- Decrease medical errors

Quality

- Resistance to change

Resistance to change

- Study was limited to two academic medical centers in one region.

- Physician satisfaction

Satisfaction

  

- Did not include factors such as practice size.

- Self-efficiency

Productivity/ efficiency

   

Benkert, et al. [33]

- Overall positive impact overtime

Satisfaction

- Data failures/ challenges

Productivity loss

- Factors beyond the EHR that can affect poor outcomes were not measured.

- Improved productivity

Productivity/ efficiency

  

- Data quality bias with level of user experience with the EHR.

- Improved data collection

Data management

  

- Neither time lags nor staggered time points were measured or controlled.

Merrill, et al. [34]

- Improved efficiency

Productivity/ efficiency

- Structural limitation

Limited staff support

- The registry database limited comparison of EHR-submitted vs non-EHR submitted data.

- Improved productivity

Productivity/ efficiency

- Missing data

Missing data / data error

- Improved compliance

Decision support

  

- Disease prevention

Preventative care

  

Glicksberg, et al. [35]

- Disease prevention

Preventative care

- None identified

None identified

- External validity: One study group was not representative of the population.

McAlearney, et al. [36]

- Consistent communication

Communication

- Productivity loss during implementation

Productivity loss

- Small sample size greatly reduces statistical power and external validy.

 

- Careful planning

Productivity/ efficiency

- Resistance to change

Resistance to change

   

- System failure

Productivity loss

   

- Poor computer skills

Limited staff support

   

- Slow queries

Productivity loss

Polling, et al. [37]

- Data collection

Data management

- Missing data

Missing 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 prevention

Preventative care

Zhao, et al. [38]

- Data collection

Data management

- Interoperability

No standards

- Limited validity and reliability

Roth, et al. [39]

- Data collection

Data management

- Interoperability

No 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.

 

- Surveillance

Surveillance

- Prone to data-entry error

Missing 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 data

Missing data / data error

- Without a time-series or longitudinal study, it is difficult to generalize the results.

Barnett, et al. [40]

- None identified

None identified

- none identified

None 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 performance

Productivity/ efficiency

- Limited functionality

Technology complex

- The lack of nationwide data eliminates comparisons to a national benchmark.

 

- Interoperability

Interoperability

  
 

- Improve measuring data (data collection)

Data management

  

Thirukumaran, et al. [42]

- None identified

None identified

- Temporary decrease in quality

Productivity loss

- Limited generalizability

Adler-Milstein J, Everson J, Lee SY. [43]

- Increased quality

Quality

- None identified

None identified

- Adherence to process measurers was high across hospitals which reduces the opportunity to observe EHR-driven improvements.

 

- Increased efficiency for hospital care

Productivity/ efficiency

- This study only analyzed the Medicare arm of the Meaningful Use program.

 

- Patient satisfaction

Satisfaction

 
 

- positive relationship between EHR adoption and performance

Interoperability

 

Ananthakrishnan, et al. [44]

- Health outcomes

Health 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 documentation

Quality

- Interoperability

No standards

- All provider notes may not have been captured if a participant saw a physician through a private setting.

- Lends generalizability to findings

Productivity/ efficiency

   

Carayon, et al. [45]

- Increased productivity

Productivity/ efficiency

- Increased amount of time spent on documentation and clinical review

Productivity loss

- Not generalizable nationwide because data were collected at only one location.

- Efficiency gains

Productivity/ 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 identified

None identified

- Negative impact on productivity and efficiency

Productivity loss

- Face validity: Clinical volume is not an exact match for provider productivity, but other studies have used this measure.

[46]

- Time consuming

Technology complex

- Construct validity: Due to the lack of baseline data available, it is difficult to discern that the intended measure is accurate.

 

- Missing data

Missing data / data error

 

Jones & Wittie [47]

- Widespread adoption

Current technology

- Lacked functionality

Accessibility/ utilization

- Limited external validity due to the uncertainty that Beacon communities across the country are homogeneous.

 

- Improved quality

Quality

- Complexity

Technology 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 incentives

Financial assistance

   
 

- Technical assistance

Communication

   

Hammermeister, et al. [48]

- Data collection

Data management

- Missing data

Missing 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 systems

Interoperability

- Potential missing data

Missing data / data error

- Infrequency of visits creates missing data.

- Efficient comparison of patients

Productivity/ efficiency

- Some privacy concerns

Privacy 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 providers

Communication

- Complex analysis

Technology complex

- The measure of interrater reliability is confounded because some providers served on many teams.

- Preventative care

Preventative care

  

Burke, et al. [51]

- Improved over quality of outpatient clinical notes

Quality

- Standards across EHRs

 

- Not generalizable to all EHR systems because only one was studied.

- Accessibility

Ease of use

No standards

- Improved efficiency

Productivity/ efficiency

 

Keck, et al. [52]

- Surveillance

Surveillance

- Limited design, deployment and function (complexity)

 

- Construct validity is questionable due to lack of baseline data.

- Increased time availability (productivity)

Productivity/ efficiency

Technology complex

- Generalizability limited because only the Indian Health System was studied.

- Improved data validity and reliability

Quality

  

Roth, et al. [53]

- Surveillance

Surveillance

- 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 errors

Quality

- Regional diversity in languages.

No standards

- None identified

- Data collection

Data management

- Interoperability

No standards

- Improved efficiency

Productivity/ efficiency

- Inconsistent documentation

Missing data / data error

  

- Data quality

Decreased quality

Chang, et al. [55]

- None identified

None identified

- Missing data

Missing 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 skills

Decision support

- None identified

None 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 satisfaction

Satisfaction

Decreased quality

- The Hawthorne effect may have increased bias toward the new EMR.

- Increased use of information

Decision support

 

- External validity may be questionable because only one EMR was studied.

- Organizational impact

Productivity/ 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 referrals

Communication

Cost

- Data were not complete

- Supplies rapid feedback to providers

Decision support

  

- Track patient outcomes

Health outcomes

  

- Monetary/financial incentive

Financial assistance

  

Zera, et al. [59]

- None identified

 

- No effect on the rates of diabetes screening

Disease 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 outcomes

Decreased quality

 

Baus, et al. [60]

- Surveillance

Surveillance

- The EHR is designed for patient care, not for research.

Accessibility/ utilization

- Not generalizable, sample bias: Clinics were chosen through purposive sampling.

- Preventative care

Preventative 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 management

Quality

   

Baus, et al. [61]

- Preventative care

Preventative care

- Difficulty of extracting necessary data (technical challenges).

Technology complex

- Limited variability in participants limits external validity.

- Surveillance

Surveillance

- Cost

Cost

- Improved efficiency

Productivity/ efficiency

- Interroperability

No standards

- Improve decision support

Decision support

  

- Increase the application of patient data to care

Data management

  

- Improve health outcomes

Health outcomes

  

Haskew, et al. [62]

- Real time access (efficiency)

Productivity/ efficiency

- Cost

Cost

- Limited external validity due to short time studied and difficulty of implementation model.

- Sharing data (communication)

Communication

- Limited staff

Limited staff support

Puttkammer, et al., [63]

- Preventative care

Preventative care

- Missing data

 

- Self-report data is questionable and subject to ability to recall or social desirability.

- Data/information accessibility

Ease of use

Missing data / data error

- Missing data that could have skewed the results.

   

- External validity limited because only two organizations studied.

Zheng, et al. 66]

- Surveillance

Surveillance

- Difficulty combining information from EHR with structured data

Technology complex

- External validity limited

- Data collection

Data management

 

Wu, et al. [64]

- Data collection

Data management

- Missing data on smoking status

 

- Citation bias

- Smoking surveillance

Surveillance

Missing 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 identification

Communication

  

Nguyen & Yehia [65]

- Data collection

Data management

- Different documentation rates at Different healthcare systems

No standards

- External validity limited because only one health system in one region of the U.S. was studied.

- Preventative care

Preventative care

 

Tomayko, et al. [66]

- Data collection

Data management

- None identified

None identified

- External validity limited because the demographics do not match that of the U.S.

- Preventative care

Preventative care

- Self-report data can be questionable and subject to bias due to recall and social desirability.

- Disease management/monitoring (child obesity)

Surveillance

 

- Quality improvement

Quality

 

- Greater surveillance of a population

Surveillance

 

- Cost effective

Financial assistance

 

Romo, et al. [67]

- Surveillance

Surveillance

- 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.

- Generalizability

Productivity/ 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 collection

Data management

- None identified

None identified

- Quasi experimental.

- Selection bias.

Wang, et al. [69]

- Improved quality

Quality

- None identified

None 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 providers

Satisfaction

- Initial decrease in clinical volume

Productivity loss

- Interrater reliability was controlled by using a stable group of providers.

- Longer notes

Communication

- Increased time expenditure and documentation times

Technology 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 volume

Productivity 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, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71]. The incorporation of new technology is expected to have some flaws associated with its integration into the healthcare field [7, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 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 2.
Table 2

Affinity matrix of facilitators and barriers

Facilitators

Reference

Occurrences

Reference

Barriers

Productivity/ efficiency

19,21,22,24,25,27,28,30*,32,33–36*,38,43,45–47*,51,53-56,59*,60, 63,64,70,72,73

33

24

21,22,23*,27*,29,32,33,36,39,41*,46,48,50,51,55,56,57,61,65,67,70

Missing data / data error

Quality

19*,20,22,24-27,33,34,45,46,49, 53,54,56,62,69,72

19

13

20*,26,28,32,40,41,46,53,56*,63,68

No standards

Data management

19–22,27,28,32,35,39, 40,41,43,50,56,66-69,71

19

12

29,35,38*,44,47,48,51,55,73*

Productivity loss

Surveillance

19,20,23,25,26,28,31,41,54,55, 62,63,66,67,69*,70,

17

10

24,29,43,48,49,52,54,63,66,73

Technology complex

Preventative care

19*,23,26,28,30,36,37,39,52,62, 63,65,68,69

15

7

20,24,28,31,60,63,64

Cost

Communication

32,38,49,52,60,64,67,73

8

4

47,56,59,61

Decreased quality

Interoperability

20,24,28,33,43,45,49,51

8

4

20,36,38,64

Limited staff support

Decision support

20,28,36,58-60,63

7

3

32,34,38

Resistance to change

Health outcomes

26,27,46,55,60,63

6

3

27,62,73

Human error

Satisfaction

34,35,45,59,73

5

2

49,62

Accessibility/ utilization

Financial assistance

24,49,50,60,69

5

1

61

Disease management

Ease of use

21,53,65

3

1

20

Critical thinking/treatment decisions

Current technology

32,49

2

1

51

Privacy concerns

* more than one occurrence

147

85

  

None identified

29,42,44,48,57,61

  

19,25,30,31,37,42,45,58,69,71,72

 
A visual representation of these factors can also be seen in fig. 2.
Fig. 2

Charts of the frequency of facilitators and barriers

Thirteen facilitators and 13 barriers were identified. The occurrences of facilitators outweighed those of the barriers 3:2. Several facilitators and barriers were similar and were combined, for instance productivity and efficiency. Articles that mentioned both facilitators are marked in the tables with an asterisk. Facilitators identified are productivity/efficiency [19,21,22,24,25,27,28,30*,32,33–36*,38,43,45–47*,51,53-56,59*,60,63,64,70,72,73],quality [19*,20,22,24-27,33,34,45,46,49,53,54,56,62,69,72],data management [7, 18, 19, 20, 21, 26, 30, 33, 37, 38, 39, 41, 48, 54, 64, 65, 66, 68, 71], surveillance [19,20,23,25,26,28,31,41,54,55,62,63,66,67,69*,70], preventative care [19*,23,26,28,30,36,37,39,52,62,63,65,68,69], communication [30, 36, 47, 50, 58, 62, 64, 70], interoperability [7, 19, 23, 31, 41, 43, 47, 49], decision support [7, 19, 34, 56, 57, 58, 61], health outcomes [25, 26, 44, 53, 58, 61], satisfaction [32, 33, 43, 57, 70], financial assistance [23, 47, 48, 58, 66], ease of use [20, 51, 63], and current technology [30, 47], Barriers identified are missing data/data error [21–23*,27*,29,32,33,36,39,41*,46,48,50,51,55-57,61,65,67,70], no standards (of data or interoperability) [20*,26,28,32,40,41,46,53,56*,63,68], productivity loss [29,35,38*,44,47,48,51,55,73*], technology (too) complex [23, 27, 41, 46, 47, 50, 52, 61, 70, 71], cost [7, 19, 23, 29, 58, 61, 62],decreased quality (of data or care) [45, 54, 57, 59], limited staff support [19, 34, 36, 62], resistance to change [30, 32, 36], human error [26, 60, 70], accessibility/utilization [47, 60], disease management [59], critical thinking/treatment decisions [19], privacy concerns [49]. The top five facilitators and top four barriers make up about 70% of the factors observed.

Discussion

Summary of evidence

In this systematic review the authors reviewed 55 articles. The analysis identified 13 facilitators and 13 barriers, and facilitators outweighed barriers 3:2. The top three facilitators were an increase in productivity/efficiency (greater capacity, more efficient procedures and processes, etc.), an increase in the quality of data or care (data that was more accurate, more precise, and contained less error; care that produced higher quality outcomes as a result of more accurate data), and various aspects of data management (users were able to access patient data in a more efficient manner). The top three barriers were missing data (some data was missing or was not filled in) / data error (incorrect data was entered), no standards for interoperability (data could not easily be shared between providers), and a loss of productivity (teaching users how to use the EHR and data-entry requirements were time consuming and took users away from other duties in the office, which made the office less productive). The results of this review show more positive than negative factors for the use of the EHR to manage public health and surveillance.

The facilitator most often found in the literature is the increase of either productivity, efficiency, or both. Organizations were maximized time with patients instead of writing documentation. These articles said that EHRs improved the workflow in organizations. Other organizations identified a loss in productivity for the same reason. This could have been due to the stage of implementation in which the organizations were.

With the ability to access a greater number of records in a more productive way, it was not surprising that surveillance accounted for the third most recorded facilitator. Surveillance can utilize information from EHRs to make population and public health predictions as well as track occurrences of infectious diseases and other public health functions to have a better overall review of a population’s health.

Limitations

To control for selection bias, reviewers agreed on definitions and concepts prior to the search and analysis of articles. Each article was reviewed and analyzed by multiple reviewers. A series of consensus meetings was held to share observations and agree on next steps. The team calculated a Kappa statistic of 0.83 which in fact shows a high level of agreement.

Publication bias is likely to occur because publishers tend to publish articles with significant relationships, and therefore articles that did not result in significant findings were not able to be selected for this review [72]. Our search was limited to PubMed and CINAHL, which may have impacted the scope of our results. These databases were chosen for their comprehensive scope and positive reputation in research.

Comparison to other research

Contrary to studies on the adoption of the EHR, the authors found that cost was not as prevalent a barrier in using EHRs in support of public health. This could be a result of sufficient time passing for financial incentives to alleviate the concern. The articles reviewed intimated that EHRs were cost effective, enhance productivity/efficiency and quality, and they are conducive for data collection when missing data is analyzed. Standards for interoperability need to continue to progress: Until all EHR solutions reach the same level of interoperability, data sharing cannot be assured.

Conclusion

Additional research should follow from this review. Productivity was both a facilitator and a barrier. It would be interesting to know if the latter is during implementation and the former is after. As nationwide adoption of a fully interoperable EHR progresses, many barriers identified in this review such as standards, and resistance to change could be mitigated. As more data becomes available through the EHR, relationships to outcomes should appear. Appropriate training on EHRs use, may help with the level of complexity among health care providers and their staff.

The EHR can improve health care productivity and efficiency to better serve public health. An abundance of health care information can be managed through databases by using electronic medical records, and this makes data more easily shared between providers and organizations.

Notes

Compliance with ethical standards

Conflicts of interest

The authors declare that they have no conflict of interest.

Ethical approval

For this type of review, formal consent is not required. This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. 1.
    Wager, K. A., Lee, F. W., Glaser, J. P., Health care information systems: a practical approach for health care management. John Wiley & Sons; Mar 27, 2017.Google Scholar
  2. 2.
    U.S Department of Health and Human Services URL: https://www.healthit.gov/providers-professionals/faqs/what-electronic-health-record-ehr. Accessed: 2017-10-03. (Archived by WebCite® at http://www.webcitation.org/6twtffZwS)
  3. 3.
    Orlova, A., and Salyards, K., Understanding Information in EHR Systems: Paving the Road for Semantic Interoperability through Standards. Journal of AHIMA. 87(9):44–47, Sep 2016.PubMedGoogle Scholar
  4. 4.
    Kindig, D., and Stoddart, G., What is population health? American Journal of Public Health. 93(3):380–383, 2003 Mar.CrossRefGoogle Scholar
  5. 5.
    Diamond, C. C., Mostashari, F., and Shirky, C., Collecting and sharing data for population health: a new paradigm. Health Affairs. 28(2):454–466, 2009 Mar 1.CrossRefGoogle Scholar
  6. 6.
    Public health surveillance. World Health Organization. URL: http://www.who.int/topics/public_health_surveillance/en/. Accessed: 2018-01-03. (Archived by WebCite® at http://www.webcitation.org/6wCf3xH19)
  7. 7.
    Calman, N., Hauser, D., Lurio, J., Wu, W. Y., and Pichardo, M., Strengthening public health and primary care collaboration through electronic health records. American Journal of Public Health. 102(11):e13–e18, 2012 Nov.CrossRefGoogle Scholar
  8. 8.
    Paul, M. M., Greene, C. M., Newton-Dame, R., Thorpe, L. E., Perlman, S. E., McVeigh, K. H., and Gourevitch, M. N., The state of population health surveillance using electronic health records: a narrative review. Population Health Management. 18(3):209–216, 2015 Jun 1.CrossRefGoogle Scholar
  9. 9.
    De Leon, S. F., and Shih, S. C., Tracking the delivery of prevention-oriented care among primary care providers who have adopted electronic health records. Journal of the American Medical Informatics Association. 18(Supplement_1):i91–i95, 2011 Aug 19.CrossRefGoogle Scholar
  10. 10.
    Flatow, V. H., Ibragimova, N., Divino, C. M., Eshak, D. S., Twohig, B. C., Bassily-Marcus, A. M., and Kohli-Seth, R., Quality outcomes in the surgical intensive care unit after electronic health record implementation. Applied Clinical Informatics. 6(04):611–618, 2015 Apr.CrossRefGoogle Scholar
  11. 11.
    Tharmalingam, S., Hagens, S., and Zelmer, J., The value of connected health information: perceptions of electronic health record users in Canada. BMC Medical Informatics and Decision Making. 16(1):93, 2016 Jul 16.CrossRefGoogle Scholar
  12. 12.
    Rinner, C., Sauter, S. K., Endel, G., Heinze, G., Thurner, S., Klimek, P., and Duftschmid, G., Improving the informational continuity of care in diabetes mellitus treatment with a nationwide Shared EHR system: Estimates from Austrian claims data. International Journal of Medical Informatics. 92:44–53, 2016 Aug 31.CrossRefGoogle Scholar
  13. 13.
    Ben-Assuli, O., Ziv, A., Sagi, D., Ironi, A., and Leshno, M., Cost-effectiveness evaluation of EHR: simulation of an abdominal aortic aneurysm in the emergency department. Journal of Medical Systems. 40(6):141, 2016 Jun 1.CrossRefGoogle Scholar
  14. 14.
    Beresniak, A., Schmidt, A., Proeve, J., Bolanos, E., Patel, N., Ammour, N., Sundgren, M., Ericson, M., Karakoyun, T., Coorevits, P., and Kalra, D., Cost-benefit assessment of using electronic health records data for clinical research versus current practices: Contribution of the Electronic Health Records for Clinical Research (EHR4CR) European Project. Contemporary Clinical Trials. 46:85–91, 2016 Jan 31.CrossRefGoogle Scholar
  15. 15.
    Chan, K. S., Fowles, J. B., and Weiner, J. P., Electronic health records and the reliability and validity of quality measures: a review of the literature. Medical Care Research and Review. 67(5):503–527, 2010 Oct.CrossRefGoogle Scholar
  16. 16.
    Yanamadala, S., Morrison, D., Curtin, C., McDonald, K., Hernandez-Boussard, T., Electronic health records and quality of care: An observational study modeling impact on mortality, readmissions, and complications. Medicine. May;95(19), 2016.CrossRefGoogle Scholar
  17. 17.
    Al-Jafar, E., Exploring patient satisfaction before and after electronic health record (EHR) implementation: the Kuwait experience. Perspectives in Health Information Management/AHIMA, American Health Information Management Association; 10(Spring), 2013.Google Scholar
  18. 18.
    Bailey, S. R., Marino, M., Hoopes, M., Heintzman, J., Gold, R., Angier, H., O’Malley, J. P., and DeVoe, J. E., Healthcare Utilization After a Children’s Health Insurance Program Expansion in Oregon. Maternal and Child Health Journal. 20(5):946–954, 2016 May 1.CrossRefGoogle Scholar
  19. 19.
    Houser, S. H., Colquitt, S., Clements, K., Hart-Hester, S., The impact of electronic health record usage on cancer registry systems in Alabama. Perspectives in Health Information Management/AHIMA, American Health Information Management Association. 9(Spring), 2012.Google Scholar
  20. 20.
    Metroka, A. E., Papadouka, V., Ternier, A., and Zucker, J. R., Effects of Health Level 7 Messaging on Data Quality in New York City’s Immunization Information System, 2014. Public Health Reports. 131(4):583–587, 2016 Jul.CrossRefGoogle Scholar
  21. 21.
    Blecker, S., Goldfeld, K., Park, N., Shine, D., Austrian, J. S., Braithwaite, R. S., Radford, M. J., and Gourevitch, M. N., Electronic health record use, intensity of hospital care, and patient outcomes. The American Journal of Medicine. 127(3):216–221, 2014 Mar 31.CrossRefGoogle Scholar
  22. 22.
    Flood, T. L., Zhao, Y. Q., Tomayko, E. J., Tandias, A., Carrel, A. L., and Hanrahan, L. P., Electronic health records and community health surveillance of childhood obesity. American Journal of Preventive Medicine. 48(2):234–240, 2015 Feb 28.CrossRefGoogle Scholar
  23. 23.
    Martelle, M., Farber, B., Stazesky, R., Dickey, N., Parsons, A., Venters, H., Meaningful Use of an Electronic Health Record in the New York City Jail System. Journal Information. Sep;105(9), 2015.CrossRefGoogle Scholar
  24. 24.
    Chambers, E. C., Wong, B. C., Riley, R. W., Hollingsworth, N., Blank, A. E., Myers, C., Bedell, J., Selwyn, P. A., Combining clinical and population-level data to understand the health of neighborhoods. American Journal of Public Health (AJPH). Mar 3, 2015.Google Scholar
  25. 25.
    Moody-Thomas, S., Nasuti, L., Yi, Y., Celestin, Jr., M. D., Horswell, R., and Land, T. G., Effect of systems change and use of electronic health records on quit rates among tobacco users in a public hospital system. American Journal of Public Health. 105(S2):e1–e7, 2015 Apr.CrossRefGoogle Scholar
  26. 26.
    Vogel, J., Brown, J. S., Land, T., Platt, R., and Klompas, M., MDPHnet: secure, distributed sharing of electronic health record data for public health surveillance, evaluation, and planning. American Journal of Public Health. 104(12):2265–2270, 2014 Dec.CrossRefGoogle Scholar
  27. 27.
    Duan, R., Cao, M., Wu, Y., Huang, J., Denny, J. C., Xu, H., Chen, Y., An Empirical Study for Impacts of Measurement Errors on EHR based Association Studies. In AMIA Annual Symposium Proceedings (Vol. 2016, p. 1764). American Medical Informatics Association, 2016.Google Scholar
  28. 28.
    Kawamoto, K., Anstrom, K. J., Anderson, J. B., Bosworth, H. B., Lobach, D. F., McAdam-Marx, C., Ferranti, J. M., Shang, H., Yarnall, K. S., Long-Term Impact of an Electronic Health Record-Enabled, Team-Based, and Scalable Population Health Strategy Based on the Chronic Care Model. In AMIA Annual Symposium Proceedings 2016 (Vol. 2016, p. 686). American Medical Informatics Association.Google Scholar
  29. 29.
    Behrens, J. J., Wen, X., Goel, S., Zhou, J., Fu, L., Kho, A. N., Using Monte Carlo/Gaussian Based Small Area Estimates to Predict Where Medicaid Patients Reside. In AMIA Annual Symposium Proceedings 2016 (Vol. 2016, p. 305). American Medical Informatics Association.Google Scholar
  30. 30.
    Cross, D. A., Cohen, G.R., Nong, P., Day, A.V., Vibbert, D., Naraharisetti, R., Adler-Milstein, J., Improving EHR Capabilities to Facilitate Stage 3 Meaningful Use Care Coordination Criteria. In AMIA Annual Symposium Proceedings 2015 (Vol. 2015, p. 448). American Medical Informatics Association.Google Scholar
  31. 31.
    Tanner, C., Gans, D., White, J., Nath, R., and Pohl, J., Electronic health records and patient safety: co-occurrence of early EHR implementation with patient safety practices in primary care settings. Applied Clinical Informatics. 6(1):136, 2015.CrossRefGoogle Scholar
  32. 32.
    Emani, S., Ting, D. Y., Healey, M., Lipsitz, S. R., Karson, A. S., Einbinder, J. S., Leinen, L., Suric, V., and Bates, D. W., Physician beliefs about the impact of meaningful use of the EHR: a cross-sectional study. Applied Clinical Informatics. 5(3):789–801, 2014.CrossRefGoogle Scholar
  33. 33.
    Benkert, R., Dennehy, P., White, J., Hamilton, A., Tanner, C., and Pohl, J. M., Diabetes and hypertension quality measurement in four safety-net sites: lessons learned after implementation of the same commercial electronic health record. Applied Clinical Informatics. 5(3):757–772, 2014.CrossRefGoogle Scholar
  34. 34.
    Merrill, J., Phillips, A., Keeling, J., Kaushal, R., and Senathirajah, Y., Effects of automated immunization registry reporting via an electronic health record deployed in community practice settings. Applied Clinical Informatics. 4(2):267, 2013.CrossRefGoogle Scholar
  35. 35.
    Glicksberg, B. S., Li, L., Badgeley, M. A., Shameer, K., Kosoy, R., Beckmann, N. D., Pho, N., Hakenberg, J., Ma, M., Ayers, K. L., and Hoffman, G. E., Comparative analyses of population-scale phenomic data in electronic medical records reveal race-specific disease networks. Bioinformatics. 32(12):i101–i110, 2016 Jun 11.CrossRefGoogle Scholar
  36. 36.
    McAlearney, A. S., Sieck, C., Hefner, J., Robbins, J., and Huerta, T. R., Facilitating ambulatory electronic health record system implementation: evidence from a qualitative study. Biomed Research International. 20:2013, 2013 Oct.Google Scholar
  37. 37.
    Polling, C., Tulloch, A., Banerjee, S., Cross, S., Dutta, R., Wood, D. M., Dargan, P. I., and Hotopf, M., Using routine clinical and administrative data to produce a dataset of attendances at Emergency Departments following self-harm. BMC Emergency Medicine. 15(1):15, 2015 Jul 16.CrossRefGoogle Scholar
  38. 38.
    Zhao, J., Henriksson, A., Asker, L., and Boström, H., Predictive modeling of structured electronic health records for adverse drug event detection. BMC Medical Informatics and Decision Making. 15(4):S1, 2015 Nov 25.CrossRefGoogle Scholar
  39. 39.
    Roth, C., Foraker, R. E., Payne, P. R., and Embi, P. J., Community-level determinants of obesity: harnessing the power of electronic health records for retrospective data analysis. BMC Medical Informatics and Decision Making. 14(1):36, 2014 May 8.CrossRefGoogle Scholar
  40. 40.
    Barnett, M. L., Mehrotra, A., and Jena, A. B., Adverse inpatient outcomes during the transition to a new electronic health record system: observational study. BMJ. 354:i3835, 2016 Jul 28.CrossRefGoogle Scholar
  41. 41.
    Drawz, P. E., Archdeacon, P., McDonald, C. J., Powe, N. R., Smith, K. A., Norton, J., Williams, D. E., Patel, U. D., Narva, A., CKD as a model for improving chronic disease care through electronic health records. Clinical Journal of the American Society of Nephrology. Jun 25:CJN-0e0940115, 2015.Google Scholar
  42. 42.
    Thirukumaran, C. P., Dolan, J. G., Webster, P. R., Panzer, R. J., and Friedman, B., The impact of electronic health record implementation and use on performance of the Surgical Care Improvement Project measures. Health Services Research. 50(1):273–289, 2015 Feb 1.CrossRefGoogle Scholar
  43. 43.
    Adler-Milstein, J., Everson, J., and Lee, S. Y., EHR Adoption and Hospital Performance: Time-Related Effects. Health Services Research. 50(6):1751–1771, 2015 Dec 1.CrossRefGoogle Scholar
  44. 44.
    Ananthakrishnan, A. N., Cagan, A., Cai, T., Gainer, V. S., Shaw, S. Y., Savova, G., Churchill, S., Karlson, E. W., Murphy, S. N., Liao, K. P., and Kohane, I., Identification of nonresponse to treatment using narrative data in an electronic health record inflammatory bowel disease cohort. Inflammatory Bowel Diseases. 22(1):151–158, 2015 Aug 31.CrossRefGoogle Scholar
  45. 45.
    Carayon, P., Wetterneck, T. B., Alyousef, B., Brown, R. L., Cartmill, R. S., McGuire, K., Hoonakker, P. L., Slagle, J., Van Roy, K. S., Walker, J. M., and Weinger, M. B., Impact of electronic health record technology on the work and workflow of physicians in the intensive care unit. International Journal of Medical Informatics. 84(8):578–594, 2015 Aug 31.CrossRefGoogle Scholar
  46. 46.
    Redd, T. K., Read-Brown, S., Choi, D., Yackel, T. R., Tu, D. C., and Chiang, M. F., Electronic health record impact on productivity and efficiency in an academic pediatric ophthalmology practice. Journal of American Association for Pediatric Ophthalmology and Strabismus. 18(6):584–589, 2014 Dec 31.CrossRefGoogle Scholar
  47. 47.
    Jones, E., and Wittie, M., Accelerated adoption of advanced health information technology in Beacon community health centers. The Journal of the American Board of Family Medicine. 28(5):565–575, 2015 Sep 1.CrossRefGoogle Scholar
  48. 48.
    Hammermeister, K., Bronsert, M., Henderson, W. G., Coombs, L., Hosokawa, P., Brandt, E., Bryan, C., Valuck, R., West, D., Liaw, W., and Ho, M., Risk-adjusted comparison of blood pressure and low-density lipoprotein (LDL) noncontrol in primary care offices. The Journal of the American Board of Family Medicine. 26(6):658–668, 2013 Nov 1.CrossRefGoogle Scholar
  49. 49.
    Benson, G. A., Sidebottom, A., VanWormer, J. J., Boucher, J. L., Stephens, C., and Krikava, J., HeartBeat Connections: a rural community of solution for cardiovascular health. The Journal of the American Board of Family Medicine. 26(3):299–310, 2013 May 1.CrossRefGoogle Scholar
  50. 50.
    Soulakis, N. D., Carson, M. B., Lee, Y. J., Schneider, D. H., Skeehan, C. T., and Scholtens, D. M., Visualizing collaborative electronic health record usage for hospitalized patients with heart failure. Journal of the American Medical Informatics Association. 22(2):299–311, 2015 Feb 20.CrossRefGoogle Scholar
  51. 51.
    Burke, H. B., Sessums, L. L., Hoang, A., Becher, D. A., Fontelo, P., Liu, F., Stephens, M., Pangaro, L. N., O'malley, P. G., Baxi, N. S., and Bunt, C. W., Electronic health records improve clinical note quality. Journal of the American Medical Informatics Association. 22(1):199–205, 2014 Oct 23.PubMedPubMedCentralGoogle Scholar
  52. 52.
    Keck, J. W., Redd, J. T., Cheek, J. E., Layne, L. J., Groom, A. V., Kitka, S., Bruce, M. G., Suryaprasad, A., Amerson, N. L., Cullen, T., and Bryan, R. T., Influenza surveillance using electronic health records in the American Indian and Alaska Native population. Journal of the American Medical Informatics Association. 21(1):132–138, 2013 Jun 5.CrossRefGoogle Scholar
  53. 53.
    Roth, C., Payne, P. R., Weier, R. C., Shoben, A. B., Fletcher, E. N., Lai, A. M., Kelley, M. M., Plascak, J. J., and Foraker, R. E., The geographic distribution of cardiovascular health in the stroke prevention in healthcare delivery environments (SPHERE) study. Journal of Biomedical Informatics. 60:95–103, 2016 Apr 30.CrossRefGoogle Scholar
  54. 54.
    De Moor G, Sundgren M, Kalra D, Schmidt A, Dugas M, Claerhout B, Karakoyun T, Ohmann C, Lastic PY, Ammour N, Kush R. Using electronic health records for clinical research: the case of the EHR4CR project. Journal of Biomedical Informatics. 2015 Feb 28;53:162–173.Google Scholar
  55. 55.
    Chang, N. W., Dai, H. J., Jonnagaddala, J., Chen, C. W., Tsai, R. T., and Hsu, W. L., A context-aware approach for progression tracking of medical concepts in electronic medical records. Journal of Biomedical Informatics. 58:S150–S157, 2015 Dec 31.CrossRefGoogle Scholar
  56. 56.
    Reed, S. G., Adibi, S. S., Coover, M., Gellin, R. G., Wahlquist, A. E., AbdulRahiman, A., Hamil, L. H., Walji, M. F., O’Neill, P., and Kalenderian, E., Does Use of an Electronic Health Record with Dental Diagnostic System Terminology Promote Dental Students’ Critical Thinking? Journal of Dental Education. 79(6):686–696, 2015 Jun 1.PubMedPubMedCentralGoogle Scholar
  57. 57.
    Inokuchi, R., Sato, H., Iwagami, M., Komaru, Y., Iwai, S., Gunshin, M., Nakamura, K., Shinohara, K., Kitsuta, Y., Nakajima, S., Yahagi, N., Impact of a New Medical Record System for Emergency Departments Designed to Accelerate Clinical Documentation: A Crossover Study. Medicine. Jul;94(26), 2015.CrossRefGoogle Scholar
  58. 58.
    Silfen, S. L., Farley, S. M., Shih, S. C., Duquaine, D. C., Ricci, J. M., Kansagra, S. M., Edwards, S. M., Babb, S., and McAfee, T., Increases in smoking cessation interventions after a feedback and improvement initiative using electronic health records—19 community health centers, New York City, October 2010–March 2012. MMWR Morb Mortal Wkly Rep 63(41):921–924, 2014 Oct 17.PubMedPubMedCentralGoogle Scholar
  59. 59.
    Zera, C. A., Bates, D. W., Stuebe, A. M., Ecker, J. L., and Seely, E. W., Diabetes screening reminder for women with prior gestational diabetes: a randomized controlled trial. Obstetrics and Gynecology. 126(1):109, 2015 Jul.CrossRefGoogle Scholar
  60. 60.
    Baus, A., Zullig, K., Long, D., Mullett, C., Pollard, C., Taylor, H., Coben, J., Developing methods of repurposing electronic health record data for identification of older adults at risk of unintentional falls. Perspectives in Health Information Management.;13(Spring), 2016.Google Scholar
  61. 61.
    Baus, A., Wood, G., Pollard, C., Summerfield, B., White, E., Registry-based diabetes risk detection schema for the systematic identification of patients at risk for diabetes in West Virginia primary care centers. Perspectives in Health Information Management; 10(Fall), 2013.Google Scholar
  62. 62.
    Haskew, J., Rø, G., Turner, K., Kimanga, D., Sirengo, M., and Sharif, S., Implementation of a cloud-based electronic medical record to reduce gaps in the HIV treatment continuum in rural Kenya. PloS One. 10(8):e0135361, 2015 Aug 7.CrossRefGoogle Scholar
  63. 63.
    Puttkammer, N., Zeliadt, S., Balan, J. G., Baseman, J., Destine, R., Domerçant, J. W., France, G., Hyppolite, N., Pelletier, V., Raphael, N. A., and Sherr, K., Development of an electronic medical record based alert for risk of HIV treatment failure in a low-resource setting. PloS One. 9(11):e112261, 2014 Nov 12.CrossRefGoogle Scholar
  64. 64.
    Wu, C. Y., Chang, C. K., Robson, D., Jackson, R., Chen, S. J., Hayes, R. D., and Stewart, R., Evaluation of smoking status identification using electronic health records and open-text information in a large mental health case register. PloS One. 8(9):e74262, 2013 Sep 12.CrossRefGoogle Scholar
  65. 65.
    Nguyen, G. T., and Yehia, B. R., Documentation of Sexual Partner Gender Is Low in Electronic Health Records: Observations, Predictors, and Recommendations to Improve Population Health Management in Primary Care. Population Health Management. 18(3):217–222, 2015 Jun 1.CrossRefGoogle Scholar
  66. 66.
    Tomayko, E. J., Weinert, B. A., Godfrey, L., Adams, A. K., Hanrahan, L. P., Peer Reviewed: Using Electronic Health Records to Examine Disease Risk in Small Populations: Obesity Among American Indian Children, Wisconsin, 2007–2012. Preventing Chronic Disease. 13, 2016.Google Scholar
  67. 67.
    Romo, M. L., Chan, P. Y., Lurie-Moroni, E., Perlman, S. E., Newton-Dame, R., Thorpe, L. E., McVeigh, K. H., Peer Reviewed: Characterizing Adults Receiving Primary Medical Care in New York City: Implications for Using Electronic Health Records for Chronic Disease Surveillance. Preventing Chronic Disease. 13, 2016.Google Scholar
  68. 68.
    Chambers, E. C., Wylie-Rosett, J., Blank, A. E., Ouziel, J., Hollingsworth, N., Riley, R. W., Selwyn, P. A., Peer Reviewed: Increasing Referrals to a YMCA-Based Diabetes Prevention Program: Effects of Electronic Referral System Modification and Provider Education in Federally Qualified Health Centers. Preventing Chronic Disease. 12, 2015.Google Scholar
  69. 69.
    Wang, J. J., Sebek, K. M., McCullough, C. M., Amirfar, S. J., Parsons, A. S., Singer, J., Shih, S. C., Peer reviewed: sustained improvement in clinical preventive service delivery among independent primary care practices after implementing electronic health record systems. Preventing Chronic Disease. 10, 2013.Google Scholar
  70. 70.
    Chiang, M. F., Read-Brown, S., Tu, D. C., Choi, D., Sanders, D. S., Hwang, T. S., Bailey, S., Karr, D. J., Cottle, E., Morrison, J. C., and Wilson, D. J., Evaluation of electronic health record implementation in ophthalmology at an academic medical center (an American Ophthalmological Society thesis). Transactions of the American Ophthalmological Society. 111:70, 2013 Sep.PubMedPubMedCentralGoogle Scholar
  71. 71.
    Zheng, H., Gaff, H., Smith, G., and DeLisle, S., Epidemic surveillance using an electronic medical record: an empiric approach to performance improvement. PloS One. 9(7):e100845, 2014 Jul 9.CrossRefGoogle Scholar
  72. 72.
    Hopewell, S., Loudon, K., Clarke, M. J., Oxman, A. D., Dickersin, K., Publication bias in clinical trials due to statistical significance or direction of trial results. The Cochrane Library. Jan 1, 2009.Google Scholar

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Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Texas State UniversitySan MarcosUSA

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