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Original Paper

Adoption Factors of the Electronic Health Record: A Systematic
Review
Clemens Scott Kruse*, MBA, MSIT, MHA, PhD; Krysta Kothman*; Keshia Anerobi*; Lillian Abanaka*
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
Email: scottkruse@txstate.edu

Abstract
Background: The Health Information Technology for Economic and Clinical Health (HITECH) was a significant piece oflegislation 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 facilitatorand 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 adoptionof 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 maintainstrong adoption rates.
(JMIR Med Inform 2016;4(2):e19) doi: 10.2196/medinform.5525
KEYWORDS
electronic health record; information technology; HITECH Act; health information technology

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Introduction
Background
Currently in the United States, expenditures associated withhealth care average 17.5% of the gross domestic product (GDP)[1]. The Health Information Technology for Economic andClinical 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, andpotential unintended errors related to limited knowledge of theEHR [3]. There is plenty of literature that supports the idea that
adoption of Health Information Technology (HIT), specificallythe EHR, presents great potential value to the health careindustry 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 theyear 2014 [5]. However, only 55% of nationwide providers had
fulfilled the HITECH Act requests by the end of 2014 [5]. Withfinancial-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 anaffinity diagram to identify those facilitators and barriers to
adoption most often cited [7].

Objective
The purpose of this review is to compile a current andcomprehensive list of facilitators and barriers to the adoptionof the EHR in the United States, and create an affinity diagramthat orders these items by frequency of occurrence. Althoughfrequency of occurrence in the literature does not necessarily

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Kruse et al
identify the most important factors, it may help policy makers
prioritize levels of effort for maximum effectiveness and theresults of this review should enable future studies to explore
the significance and order of importance.

Methods
SearchWe searched for research on the topic of both facilitators andbarriers 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 areclosely associated with the electronic records: the electronic
health record, and the electronic medical record (EMR). Whilethese terms are distinct in the HIT field, they are often usedinterchangeably throughout the literature, so both were includedin 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.

Data
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 andbarriers 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 associatedinclusion and exclusion criteria. As depicted, we narrowed thefocus 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.

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Figure 1. The search process with inclusion and exclusion criteria.

Using the criteria agreed upon, we independently read abstractsof 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 theremaining 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 facilitatorsand barriers to adoption. Consensus was reached on all 31
articles (kappa=1.0, excellent).

data sources, and pertinent details on both facilitators andbarriers 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 correspondto 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 startof those called up in the review. From this matrix, multiple
affinity diagrams were created that illustrate the frequency offacilitators, barriers, study designs, and sources of data.

We decided to include systematic reviews in the sample because
the data in the reviews would help validate our review. A totalof three reviews were included and integrated into a literaturematrix with the other articles. The literature matrix consistedof date of publication, journal, authors, titles, study designs,

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 ourliterature matrix is included in Table 1. Many factors that somestudies listed as facilitators were listed by others as barriers.

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Results
Summary of Findings

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Table 1. Summarized facilitators and barriers.
Authors

Facilitators

Barriers

Kruse CS, et al [8]Access to information

Initial cost

Error reduction

User perceptions

Transfer of information

Implementation problems

Long-run cost savings

External factors

Clinical and administrative efficiency

Training

Project planning

Cultural change

Security

Future upgrades

Time savings

Necessary maintenanceStaff retention
Cucciniello M, et al [9]Commitment promotion

Change processes

Role defining
System impacts assessments
McCullough JM, et al [10]Availability of clinical data

Competition

Support from management
Competition
Tang, et al [11]Availability of RECs

none specified

Abramson EL, et al [12]Size of hospital (bed size)

Cost
Lack of incentive
Lack of interoperability
CompetitivenessOngoing cost of maintenance

Ben-Zion R et al [13]Executive management support

Cost-benefit asymmetry

Alignment with firm strategy

Lack of standard protocols for data exchange

Economic competivenessUncertainty over implementation cost

Knowledge management

User resistancePatient empowerment

Breaches in security
Patient privacy

D'Amore JD, et al [14]Continuity of care document

Omission 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 amountOmission 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

Health centers with large share of Hispanics and Blacks had lower
adoption rates

Improve population and public healthCenters located in rural areas

Quality recognition

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)

Patients’ age

Competiveness

Rural locations

Urban locations

Computer anxiety

Users cognitive ability
User attitude toward information
Workflow impact
Communication among users
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Authors

Facilitators

BarriersSamuel CA [2]Patients enrolled in Medicare or Medicaid

Health professional shortage areas

Metropolitan status

Minority concentration

Increased financial incentives
Sockolow PS, et al [16]Increase in productivity

Incomplete medication information

Improved clinical notes

Incomplete hospital-stay information

Reduced time to reimbursement
Improved communication among staff
Ancker JS, et al [17]Monetary incentives

Cost

Efficiency (fewer providers needed)

Lack of tech assistance

Efficiency (practice sites)
Effectiveness (fewer patients)
Practice size
Audet AM, et al [18]Size of practice

Cost

Ability to search for patients by diagnosislack of experience

Ability to list patients overdue for preventative Lack of tech-support infrastructure
care
Sort patients by specific laboratory results
Baillie CA, et al [19]Reduce readmission rates

Existing data may not serve well in a predictive model

Cheung SK, et al [20]Efficiency

Patient unfriendliness

Reduction of medical errors

Limited consultant time

Ability to share patient information in public
sectorCost concerns

Eliminate need to store paper records

Concerns on data migrations from paper to system

Eliminate illegibility of practice partners

Insufficient space for computer installation

Laboratory order forms contained bar codes
for easier ordering

EMR test order problems

A unique bar code for patient details

Order number problem

Unique bar codes for each test

Multiple forms

A test order episode barcode

EMR order incorrect

Georgiou A, et al [21]Computer use more time consuming

Handwritten request on an EMR order

Change of test
Add-on test
No information provided
Longer data entry time
Hamid F, Cline TW [5]EHR satisfaction increased when users under- Cost
stood the benefits
Perceived lack of usefulness and provider autonomy
Supportive management
Time consuming
Training programs

Iqbual U, et al [22]Perceived usefulness

Clinics with high number of outpatient visits

Perceived ease to use

Subjective norm

Computer self-efficacy
Security
Intention to use
Kirkendall ES, et al [23]Communication

Transition of data

Job satisfaction
Quality and patient data
Quality and safety of patient care
Employee understanding and support
Organizational support
The “Rights” of patient care

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Authors

Facilitators

Barriers

Middleton B, et al [24]Monetary incentives

Increased training burden

Improve effectiveness

Alert fatigue

Improve efficiency
Patel V, et al [25]Financial incentives

Lack of interoperability standards

Size of practiceShen X, et al [26]Size of practice

Cost
Lack of integration with other systems
Lack of national guidelines for implementation

Xierali IM, et al [27]Health maintenance organizations more likely Medically underserved locations less likely to adopt EHR
to adopt EHR
Geographic health professional shortage areas less likely to adopt
Those with faculty status more likely to adopt EHR
EHRInternational medical graduates less likely to adopt EHRGroup practice/solo practice and small practice physicians less likely
to adopt EHR

Menachemi N, et al [28]HMO penetration into market

Competition
Low income patients

DesRoches CM, et al [29]Size of facility

Cost

Incentives

Size of facility

Decker SL, et al [30]Size of organization

Age

Hudson JS, et al [31]Hospital setting

Cost

Improved outcomes
Reduce duplicative tests
Integrate levels of care
Improve communication
Greater readability
Jamoom E, et al [32]Agenone specified

Size of practiceEnhanced patient care
Leu MG, et al [33]Size of practice

Cost
Productivity
Customizability (right fit)

Linder JA et al [34]Better for structured documenters

Decrease in quality of care for dictator note takers

Better for free text documenters
Ramaiah M, et al [35]Workflow can be optimized

Workflow often ad-hoc in nature

Access to electronic information

Check-backs of scripts still time consuming

e-prescriptions

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

Privacy and security

Natural language processing
Ronquillo JG [37]Genome-associated care

Privacy and security

Reduce error
More efficient care
More effective care
Control costs

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Authors

Facilitators

Barriers

Wang T, Biederman S [38]Reduce error

Cost

Improve quality of care
Deliver more effective care
Soares N, et al [39]Improve clinician satisfaction

Cost

Improve clinical efficiency

Technical assistance

Improve parent satisfaction

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

Facilitators
As depicted in Table 1, various articles used similar, but notexact 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 withuser attitude toward information [7,22,23,36]. Table 2 isorganized to rank order each factor that serves as a facilitator
for EHR adoption. The center column identifies the article inwhich the factor was observed–the numbers correspond to thenumber assigned in order of mention (Introduction), followedby 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, andimproved 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

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identified in the literature 7%, 6%, 6%, and 5%, respectively.
Error reduction, time savings, and competitiveness were alllisted 4% of all occurrences. The rest of the barriers werementioned three or less times, so we grouped them into a
category of miscellaneous.

Barriers
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 fordata exchange [12,22,26,40]. Training was combined withmaintenance 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, andomission of result reference range [14,16,21]. There were atotal of 23 barriers, and they were found a total of 95 times in
the literature.

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Table 2. Facilitators identified in the literature.

a

Facilitators

Occurrences by article reference number

Total occurrences

Efficiency

2,7,8,15,16,17,19,20,23,25,29,31,33

13

Hospital sizea

7,12,16,24,25,26,28,29,31,32

11

Improved quality

15,18,21,22,23,26,30,31,32,33

10

Access to patient data

8,10,15,19,20,22,28,29

8

User perception/perceived usefulness

5,7,9,21,22,26,30

7

Ability to transfer information

8,9,19,28,29,30

6Communication

7,8,15,22,30

5

Executive management support

1,5,9,10,13

6

Incentives

2,16,21,23

5

Error reduction

8,19,31,32

4Time savings

5,8,15,20

4Competivenessa

7,10,13,27

4

Security

8,21,223

Improved population health

2,15,22

3

Continuity of care document

2,15,40

3

Urban/more developed locations/statusa

2,7,263

Knowledge/IT management

11,13,15

3

Staff retention

8,16

2

Long run cost savings

8,31

2

Alignment with strategy

1,13

2

Project planning

8

1

Patient empowerment

1

1

Patient engagement

141

Effectiveness

32

1

Genome associated care

31

1

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

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Table 3. Barriers identified in the literature.

aBarriers

Occurrences by article reference number

Total occurrences

Cost

5,8,12,13,16,17,19,25,28,30,32, 33,34,37,38

16

Time consuming

5,19,20,32,34,39

6

User perception/perceived lack of usefulness

5,8,13,17,19,34

6

Transition of data

13,19,20,22,28,34

6

Facility location (rural areas)/characteristicsa

2,7,14,21,28

6

Implementation issues

8,13,19,20,25

5

User/patient resistance

7,9,13,19,20

5

Lack of tech assistance/experience

13,16,29,33,38

5Interoperability/no standard protocols for data exchange

12,21,25,39

4

Medical error

15,20,23,40

4

Training, maintenance, upgrades

8,12,20,23

4

Lack of agility to make changes

20,32,39

3

Staff shortages/overworked

2,26,39

3

Privacy and/or security

13,35,363

Missing data

15,20,40

3

External factorsa

8,26,38

3

Competiveness

12,10,27

3

Provider or patient agea

7,29

2

Race & income disparitiesa

2,15

2

Lack of infrastructure and/or space for systems

17,19

2

Need organizational cultural change

8,38

2

Lack of incentives

12

1

IMGs less likely to adapt

26

1

Statistical 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 lackof usefulness, transition of data, and facility location were eachidentified 6% of the time (6/95). Implementation issues,
user/patient resistance and lack of technical assistance orexperience, were listed 5% of all occurrences (5/95). Lack ofinteroperability, medical error, training, maintenance, andupgrades were all listed 4% of all occurrences (4/95). The restof the barriers were mentioned three or less times, so we grouped
them into a category of miscellaneous.

From the 31 articles included in the review, 3 (10%) werereviews, 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 primarydata, and 4 (13%) used a mixture of sources. Thirteen (42%) of
the articles in the review collected primary data through asurvey, interview, or combination of both.

As depicted in Tables 2 and 3, two facilitating factors and fourbarriers 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 easilychanged; therefore, they do not offer administrators and policy
makers much actionable information.

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 exampleof that phenomenon. It is listed as a facilitator (mentioned 4%of the time), using the EHR to prevent error [8,20,32,33] andas a barrier (mentioned 4% of the time), use of the EHR cancause error [14,16,21,24]. User perceptions were also listed on
both sides for monetary factors: the cost-related facilitator was

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Discussion
Principal Findings

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incentives (mentioned 5% of the time), and the cost-related
barrier was cost (mentioned 17% of the time). One moredichotomy 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, whileothers listed interoperability/no standard protocols for dataexchange (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 theadoptionofthe
EHR[5,8,12,13,17,18,20,26,
28,31,33,34,35,38,39]. Several factors were reinforced by thisreview that highlight organizational characteristics such as sizeand 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, anduser/patient acceptance, a point supported by the literature[2,7,15,22,29]. Unfortunately, very few solutions are offered tothis group; at a minimum policy should look to assist those wholag 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 toa 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 notintended to establish causality, but instead, it was designed to
identify the frequency with which facilitators and barriers are

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discussed in the literature. It is hoped that by this review,data-driven studies can be developed to strengthen the validityof the factors listed.

Limitations
This paper provides a review of the factors associated withadoption of EHR systems. Interrater reliability was calculated
for both the search terms and titles selected, as well as theconsensus-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 thoseof 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.

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

Acknowledgments
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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Abbreviations
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
Please cite as:
Kruse CS, Kothman K, Anerobi K, Abanaka L
Adoption Factors of the Electronic Health Record: A Systematic Review
JMIR Med Inform 2016;4(2):e19
URL: http://medinform.jmir.org/2016/2/e19/
doi: 10.2196/medinform.5525
PMID: 27251559©Clemens Scott Kruse, Krysta Kothman, Keshia Anerobi, Lillian Abanaka. Originally published in JMIR Medical Informatics(http://medinform.jmir.org), 01.06.2016. This is an open-access article distributed under the terms of the Creative Commons
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