Published on in Vol 4, No 2 (2016): Apr-Jun

Adoption Factors of the Electronic Health Record: A Systematic Review

Adoption Factors of the Electronic Health Record: A Systematic Review

Adoption Factors of the Electronic Health Record: A Systematic Review

Original Paper

Texas State University, School of Health Administration, San Marcos, TX, United States

*all authors contributed equally

Corresponding Author:

Clemens Scott Kruse, MBA, MSIT, MHA, PhD

Texas State University

School of Health Administration

601 University Drive

HPB, 250

San Marcos, TX, 78230

United States

Phone: 1 210 355 4742

Fax:1 512 245 8712


Background: The Health Information Technology for Economic and Clinical Health (HITECH) was a significant piece of legislation in America that served as a catalyst for the adoption of health information technology. Following implementation of the HITECH Act, Health Information Technology (HIT) experienced broad adoption of Electronic Health Records (EHR), despite skepticism exhibited by many providers for the transition to an electronic system. A thorough review of EHR adoption facilitator and barriers provides ongoing support for the continuation of EHR implementation across various health care structures, possibly leading to a reduction in associated economic expenditures.

Objective: The purpose of this review is to compile a current and comprehensive list of facilitators and barriers to the adoption of the EHR in the United States.

Methods: Authors searched Cumulative Index of Nursing and Allied Health Literature (CINAHL) and MEDLINE, 01/01/2012–09/01/2015, core clinical/academic journals, MEDLINE full text, and evaluated only articles germane to our research objective. Team members selected a final list of articles through consensus meetings (n=31). Multiple research team members thoroughly read each article to confirm applicability and study conclusions, thereby increasing validity.

Results: Group members identified common facilitators and barriers associated with the EHR adoption process. In total, 25 adoption facilitators were identified in the literature occurring 109 times; the majority of which were efficiency, hospital size, quality, access to data, perceived value, and ability to transfer information. A total of 23 barriers to adoption were identified in the literature, appearing 95 times; the majority of which were cost, time consuming, perception of uselessness, transition of data, facility location, and implementation issues.

Conclusions: The 25 facilitators and 23 barriers to the adoption of the EHR continue to reveal a preoccupation on cost, despite incentives in the HITECH Act. Limited financial backing and outdated technology were also common barriers frequently mentioned during data review. Future public policy should include incentives commensurate with those in the HITECH Act to maintain strong adoption rates.

JMIR Med Inform 2016;4(2):e19




Currently in the United States, expenditures associated with health care average 17.5% of the gross domestic product (GDP) [1]. The Health Information Technology for Economic and Clinical Health (HITECH) Act was initiated in 2009 and, as described by Samuel (2014), implemented goals of “widespread” adoption of Electronic Health Records (EHRs) that should realize nationwide savings in the health care industry [2]. Although much research exists in support of the policy makers’ agenda tied to the HITECH Act, the widespread adoption process leaves many providers reluctant to move forward due to concerns of financial pressures, technology limitations, and potential unintended errors related to limited knowledge of the EHR [3]. There is plenty of literature that supports the idea that adoption of Health Information Technology (HIT), specifically the EHR, presents great potential value to the health care industry in our nation [3]. Through the implementation of HIT, patients, providers, and intermediaries can expect “efficiency, effectiveness, and safety of health care” [4]. The potential for great savings, efficiency, and quality through the adoption of the EHR created high expectations from the federal government, and President Bush even expected ubiquitous adoption by the year 2014 [5]. However, only 55% of nationwide providers had fulfilled the HITECH Act requests by the end of 2014 [5]. With financial-savings estimates ranging from $77-$371 billion throughout the country following broad implementation, adoption of the EHR is essential for all who are involved [6]. A thorough review of EHR adoption facilitator and barrier factors provides ongoing support for the continuation of EHR implementation across various health care structures, possibly leading to a reduction in associated economic expenditures. Several researchers have examined adoption factors and barriers, but a gap in the literature exists that places these factors into an affinity diagram to identify those facilitators and barriers to adoption most often cited [7].


The purpose of this review is to compile a current and comprehensive list of facilitators and barriers to the adoption of the EHR in the United States, and create an affinity diagram that orders these items by frequency of occurrence. Although frequency of occurrence in the literature does not necessarily identify the most important factors, it may help policy makers prioritize levels of effort for maximum effectiveness and the results of this review should enable future studies to explore the significance and order of importance.


We searched for research on the topic of both facilitators and barriers to adoption of the EHR. A quick look at the Medical Subject Heading (MeSH) in PubMed terms shows no clear association with the term “adoption” in the sense of “selection”. As a result, a combination of Boolean operators and several similar terms were employed in a manner that would be likely to capture of the desired articles. Additionally, two terms are closely associated with the electronic records: the electronic health record, and the electronic medical record (EMR). While these terms are distinct in the HIT field, they are often used interchangeably throughout the literature, so both were included in the search terms. We also accepted studies and reviews on the topic, but only if they were published in academic journals or indexed in MEDLINE.


Articles for this review were gathered from two separate databases: Cumulative Index of Nursing and Allied Health Literature (CINAHL) Academic Search Complete via Ebson B Stephens Company (EBSCO Host), and PubMed (MEDLINE Complete). Search criteria were not limited to any specific focus. Instead, we searched for EHR or EMR adoption factors and barriers to adoption in patient care facilities in general. An iterative, nonlinear search string was created through PubMed and a similar string was used with Boolean operators in CINAHL.

Figure 1 illustrates the search process, with the associated inclusion and exclusion criteria. As depicted, we narrowed the focus of the review to 1/1/2012–9/1/2015, core clinical/academic journals, full text. From this process, 60 articles were identified. The beginning of 2012 was chosen because it is one year after incentives for Meaningful Use incentives became available. The entire process of article selection is illustrated in Figure 1 (Literature review process). Authors agreed ahead of time on acceptable criteria for articles included in the review in an effort to increase the inter-rater reliability.

Figure 1. The search process with inclusion and exclusion criteria.
View this figure

Using the criteria agreed upon, we independently read abstracts of these articles to determine if the research was germane to our topic, then we discussed our findings to reach consensus. Once consensus was reached, we examined the references in the remaining 30 articles to identify additional research that was not captured with our search string; one additional article was identified for the sample through this process. The final sample included 31 articles. The inter-rater reliability for the initial selection of titles was very good (kappa=.789). Our group of five divided the articles into sets that overlapped. We met again to discuss the merits of these articles, and through this meeting, we identified common themes in the literature of both facilitators and barriers to adoption. Consensus was reached on all 31 articles (kappa=1.0, excellent).

We decided to include systematic reviews in the sample because the data in the reviews would help validate our review. A total of three reviews were included and integrated into a literature matrix with the other articles. The literature matrix consisted of date of publication, journal, authors, titles, study designs, data sources, and pertinent details on both facilitators and barriers to the adoption of the EHR. Studies and reviews were sorted by date of publication (newest to oldest), by author (alphabetical), and they were assigned numbers that correspond to those in the references. The numbers are not sequential in Table 1 because several of the articles were used in the background section, so their numbers are lower than the start of those called up in the review. From this matrix, multiple affinity diagrams were created that illustrate the frequency of facilitators, barriers, study designs, and sources of data.

Summary of Findings

We identified 31 unique publications that addressed facilitators and/or barriers to adoption of the EHR. Our analysis identified 25 facilitators for and 23 barriers to adoption. A portion of our literature matrix is included in Table 1. Many factors that some studies listed as facilitators were listed by others as barriers.

Table 1. Summarized facilitators and barriers.
Kruse CS, et al [8]Access to information
Error reduction
Transfer of information
Long-run cost savings
Clinical and administrative efficiency
Project planning
Time savings
Staff retention
Initial cost
User perceptions
Implementation problems
External factors
Cultural change
Future upgrades
Necessary maintenance
Cucciniello M, et al [9]Commitment promotion
Role defining
System impacts assessments
Change processes
McCullough JM, et al [10]Availability of clinical data
Support from management
Tang, et al [11]Availability of RECsnone specified
Abramson EL, et al [12]Size of hospital (bed size)Cost
Lack of incentive
Lack of interoperability
Ongoing cost of maintenance
Ben-Zion R et al [13]Executive management support
Alignment with firm strategy
Economic competiveness
Knowledge management
Patient empowerment
Cost-benefit asymmetry
Lack of standard protocols for data exchange
Uncertainty over implementation cost
User resistance
Breaches in security
Patient privacy
D\'Amore JD, et al [14]Continuity of care documentOmission or misuse of LOINC
Excess precision in timestamps
Omission or misuse of UCUM in meds
Omission or misuse of RxNorm
Omission or misuse of dose amount
Omission or misuse of allergic reactions
Omission or misuse of allergy severity
Omission or misuse of dose frequency
Omission of result interpretation
Omission of result reference range
Jones EB, Furukawa MF [15]Engage patients and family in their care
Improve care coordination
Improve population and public health
Quality recognition
Health centers with large share of Hispanics and Blacks had lower adoption rates
Centers located in rural areas
Health center size, income status and region
Health centers with larger share of patients whose family incomes were below poverty level had lower rate of EHR adoption
Kruse CS, et al [7]Size of hospital (bed size)
Urban locations
Users cognitive ability
User attitude toward information
Workflow impact
Communication among users
Patients’ age
Rural locations
Computer anxiety
Samuel CA [2]Patients enrolled in Medicare or Medicaid
Metropolitan status
Increased financial incentives
Health professional shortage areas
Minority concentration
Sockolow PS, et al [16]Increase in productivity
Improved clinical notes
Reduced time to reimbursement
Improved communication among staff
Incomplete medication information
Incomplete hospital-stay information
Ancker JS, et al [17]Monetary incentives
Efficiency (fewer providers needed)
Efficiency (practice sites)
Effectiveness (fewer patients)
Practice size
Lack of tech assistance
Audet AM, et al [18]Size of practice
Ability to search for patients by diagnosis
Ability to list patients overdue for preventative care
Sort patients by specific laboratory results
lack of experience
Lack of tech-support infrastructure
Baillie CA, et al [19]Reduce readmission ratesExisting data may not serve well in a predictive model
Cheung SK, et al [20]Efficiency
Reduction of medical errors
Ability to share patient information in public sector
Eliminate need to store paper records
Eliminate illegibility of practice partners
Patient unfriendliness
Limited consultant time
Cost concerns
Computer use more time consuming
Concerns on data migrations from paper to system
Insufficient space for computer installation
Georgiou A, et al [21]Laboratory order forms contained bar codes for easier ordering
A unique bar code for patient details
Unique bar codes for each test
A test order episode barcode
EMR test order problems
Handwritten request on an EMR order
Order number problem
Multiple forms
EMR order incorrect
Change of test
Add-on test
No information provided
Longer data entry time
Hamid F, Cline TW [5]EHR satisfaction increased when users understood the benefits
Supportive management
Training programs
Perceived lack of usefulness and provider autonomy
Time consuming
Iqbual U, et al [22]Perceived usefulness
Perceived ease to use
Computer self-efficacy
Intention to use
Clinics with high number of outpatient visits
Subjective norm
Kirkendall ES, et al [23]Communication
Job satisfaction
Quality and patient data
Quality and safety of patient care
Employee understanding and support
Organizational support
The “Rights” of patient care
Transition of data
Middleton B, et al [24]Monetary incentives
Improve effectiveness
Improve efficiency
Increased training burden
Alert fatigue
Patel V, et al [25]Financial incentives
Size of practice
Lack of interoperability standards
Shen X, et al [26]Size of practiceCost
Lack of integration with other systems
Lack of national guidelines for implementation
Xierali IM, et al [27]Health maintenance organizations more likely to adopt EHR
Those with faculty status more likely to adopt EHR
Medically underserved locations less likely to adopt EHR
Geographic health professional shortage areas less likely to adopt EHR
International medical graduates less likely to adopt EHR
Group practice/solo practice and small practice physicians less likely to adopt EHR
Menachemi N, et al [28]HMO penetration into marketCompetition
Low income patients
DesRoches CM, et al [29]Size of facility
Size of facility
Decker SL, et al [30]Size of organizationAge
Hudson JS, et al [31]Hospital setting
Improved outcomes
Reduce duplicative tests
Integrate levels of care
Improve communication
Greater readability
Jamoom E, et al [32]Age
Size of practice
Enhanced patient care
none specified
Leu MG, et al [33]Size of practiceCost
Customizability (right fit)
Linder JA et al [34]Better for structured documenters
Better for free text documenters
Decrease in quality of care for dictator note takers
Ramaiah M, et al [35]Workflow can be optimized
Access to electronic information
Workflow often ad-hoc in nature
Check-backs of scripts still time consuming
Medical literacy of clerks inhibits smooth scheduling
Information must still be verified
Lack of IT experience of staff
Uncertainty of time
Uncertainty of cost
Rea S, et al [36]Secondary use of data
Natural language processing
Privacy and security
Ronquillo JG [37]Genome-associated care
Reduce error
More efficient care
More effective care
Control costs
Privacy and security
Wang T, Biederman S [38]Reduce error
Improve quality of care
Deliver more effective care
Soares N, et al [39]Improve clinician satisfaction
Improve clinical efficiency
Improve parent satisfaction
Technical assistance
Organizational barriers
No consensus among peer organizations
Hacker K, et al [40] Disruption of care
Lack of interoperability
Disruption of workflow
Increased patient-cycle time
Breakdown in communication
Fragmentation of information
Inflexible processes
Physician overload


As depicted in Table 1, various articles used similar, but not exact terms. While compiling the results into Table 2, several factors were similar enough to be combined. User perception/perceived usefulness [5,9,27,31], was combined with user attitude toward information [7,22,23,36]. Table 2 is organized to rank order each factor that serves as a facilitator for EHR adoption. The center column identifies the article in which the factor was observed–the numbers correspond to the number assigned in order of mention (Introduction), followed by the order analyzed (Table 1), and the numbers match those assigned to these articles in the references. The last column numbers the occurrences. There were a total of 25 facilitators, and they were found a total of 109 times in the literature.

From the facilitators listed, efficiency, organization size, and improved quality were listed 12%, 9%, and 9% of the total occurrences of all facilitators mentioned in the literature, respectively. Access to patient care, user perception/perceived usefulness, ability to transfer information and incentives were identified in the literature 7%, 6%, 6%, and 5%, respectively. Error reduction, time savings, and competitiveness were all listed 4% of all occurrences. The rest of the barriers were mentioned three or less times, so we grouped them into a category of miscellaneous.


As depicted in Table 1, various articles used similar, but not the exact terms. While compiling the results into Table 3, several barriers were similar enough to be combined. This occurred more often in the barrier table than the facilitator table. Interoperability was combined with no standard protocol for data exchange [12,22,26,40]. Training was combined with maintenance and upgrades [8,12,21,24]. The barrier of Staff shortages was combined with overworked [2,27,40]. Privacy was combined with security [10,36,37]. Lack of infrastructure was combined with lack of space [18,20]. Finally, missing data was combined with omission of result, interpretation, and omission of result reference range [14,16,21]. There were a total of 23 barriers, and they were found a total of 95 times in the literature.

Table 2. Facilitators identified in the literature.
FacilitatorsOccurrences by article reference numberTotal occurrences
Hospital sizea7,12,16,24,25,26,28,29,31,3211
Improved quality15,18,21,22,23,26,30,31,32,3310
Access to patient data8,10,15,19,20,22,28,298
User perception/perceived usefulness5,7,9,21,22,26,307
Ability to transfer information8,9,19,28,29,306
Executive management support1,5,9,10,136
Error reduction8,19,31,324
Time savings5,8,15,204
Improved population health2,15,223
Continuity of care document2,15,403
Urban/more developed locations/statusa2,7,263
Knowledge/IT management11,13,153
Staff retention8,162
Long run cost savings8,312
Alignment with strategy1,132
Project planning81
Patient empowerment11
Patient engagement141
Genome associated care311

aStatistical association identified through retrospective studies, rather than answers to “why” in a survey or interview.

Table 3. Barriers identified in the literature.
BarriersOccurrences by article reference numberTotal occurrences
Cost5,8,12,13,16,17,19,25,28,30,32, 33,34,37,3816
Time consuming5,19,20,32,34,396
User perception/perceived lack of usefulness5,8,13,17,19,346
Transition of data13,19,20,22,28,346
Facility location (rural areas)/characteristicsa2,7,14,21,286
Implementation issues8,13,19,20,255
User/patient resistance7,9,13,19,205
Lack of tech assistance/experience13,16,29,33,385
Interoperability/no standard protocols for data exchange12,21,25,394
Medical error15,20,23,404
Training, maintenance, upgrades8,12,20,234
Lack of agility to make changes20,32,393
Staff shortages/overworked2,26,393
Privacy and/or security13,35,363
Missing data15,20,403
External factorsa8,26,383
Provider or patient agea7,292
Race & income disparitiesa2,152
Lack of infrastructure and/or space for systems17,192
Need organizational cultural change8,382
Lack of incentives121
IMGs less likely to adapt261

aStatistical association identified through retrospective studies, rather than answers to “why” in a survey or interview.

The barrier most often identified in the literature was cost (17%, 16/95). This factor included the following: initial cost, implementation cost, maintenance cost, and training cost. The barriers of too time consuming, user perception/perceived lack of usefulness, transition of data, and facility location were each identified 6% of the time (6/95). Implementation issues, user/patient resistance and lack of technical assistance or experience, were listed 5% of all occurrences (5/95). Lack of interoperability, medical error, training, maintenance, and upgrades were all listed 4% of all occurrences (4/95). The rest of the barriers were mentioned three or less times, so we grouped them into a category of miscellaneous.

As depicted in Tables 2 and 3, two facilitating factors and four barriers to EHR adoption are followed by a superscript letter. These factors appeared in the literature, but they were identified through statistical associations by researchers conducting retrospective studies. We included these factors in the review because the retrospective studies add value overall, but they are set apart because they are factors that really cannot be easily changed; therefore, they do not offer administrators and policy makers much actionable information.

From the 31 articles included in the review, 3 (10%) were reviews, and 9 (29%) were mixed methods. The remaining articles were a combination of retrospective, observational, cross-sectional, or descriptive. Of the articles reviewed, 17 (55%) analyzed secondary data, 12 (39%) collected primary data, and 4 (13%) used a mixture of sources. Thirteen (42%) of the articles in the review collected primary data through a survey, interview, or combination of both.

Principal Findings

We found it interesting how often perception plays into interviews and surveys, and in the case of this review, resulted in one or more factors appearing as both an enabler and a barrier, based on the perception of the interviewee. Error is one example of that phenomenon. It is listed as a facilitator (mentioned 4% of the time), using the EHR to prevent error [8,20,32,33] and as a barrier (mentioned 4% of the time), use of the EHR can cause error [14,16,21,24]. User perceptions were also listed on both sides for monetary factors: the cost-related facilitator was incentives (mentioned 5% of the time), and the cost-related barrier was cost (mentioned 17% of the time). One more dichotomy was time-related factors: the facilitator factor, efficiency (mentioned 12% of the time), and the barrier, time consuming (mentioned 6% of the time). Some interviewees listed ability to transfer information (6%) as a facilitator, while others listed interoperability/no standard protocols for data exchange (4%) as a barrier.

Results from this review are in line with others performed along the same lines. Cost is repeatedly a primary barrier to the adoption of the EHR [5,8,12,13,17,18,20,26, 28,31,33,34,35,38,39]. Several factors were reinforced by this review that highlight organizational characteristics such as size and location [7,8]. Location is a difficult barrier to overcome. It is not a mystery to anyone that rural communities often struggle to overcome barriers such as cost, bandwidth, and user/patient acceptance, a point supported by the literature [2,7,15,22,29]. Unfortunately, very few solutions are offered to this group; at a minimum policy should look to assist those who lag behind the rest of the adopters [29]. Small, rural communities are the slowest to adopt, and their size is a major disadvantage in terms of budget and technical agility. Policy should look to a range of factors to lever, such as organizational, cultural, technological, and financial considerations [9].

Many factors play a role in establishing an environment conducive to the adoption of the EHR. This review was not intended to establish causality, but instead, it was designed to identify the frequency with which facilitators and barriers are discussed in the literature. It is hoped that by this review, data-driven studies can be developed to strengthen the validity of the factors listed.


This paper provides a review of the factors associated with adoption of EHR systems. Interrater reliability was calculated for both the search terms and titles selected, as well as the consensus-building activity surrounding the final selection of the 31 articles. In that regard, reliability of the results are strong.

Validity was strengthened by these results aligning with those of previous reviews. This addresses internal validity, but external validity would be limited to the United States because articles that focused on other countries were excluded from the review. Another limitation is that EHR adoption and usage were often self-reported by physicians, and social-desirability bias may have led physicians to overestimate actual usage.


Users and nonusers alike are concerned about similar topics such as efficiency, quality, and interoperability. This review supports the findings of other reviews. Additional research remains necessary to assess the EHR system adoption factors in health care organizations in future years. Within the constantly changing environment of health care in the United States, health care decision makers are gradually adopting the EHRs, but adoption is far from ubiquitous. Country-level advantages will likely not emerge until everyone adopts a fully interoperable EHR.


We would like to acknowledge Texas State University for using their library database for our research.

Conflicts of Interest

None declared.

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CINAHL: Cumulative Index of Nursing and Allied Health Literature
EBSCO Host: Ebson B Stephens Company
EHR: electronic health records
EMR: electronic medical records
GDP: gross domestic product
HITECH: The Health Information Technology for Economic and Clinical Health
MeSH: Medical subject headings from the American National Library of Medicine

Edited by G Eysenbach; submitted 14.01.16; peer-reviewed by S Cutrona, S Emani; comments to author 02.03.16; revised version received 03.03.16; accepted 21.03.16; published 01.06.16


©Clemens Scott Kruse, Krysta Kothman, Keshia Anerobi, Lillian Abanaka. Originally published in JMIR Medical Informatics (, 01.06.2016.

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