Electronic Health Record and Translation Science


Translation science is focused on the process of translating evidence-supported interventions into practice; translation science both tests said interventions and studies the barriers, facilitators, and other aspects of their adoption (Titler, 2014; Titler, Adams, & Cameron, 2014). The current paper suggests focusing on the barriers and facilitators relevant to the adoption of electronic health records (EHRs), as well as the role of a Doctor of Nursing Practice (DNP) in the process.

EHRs are a relatively well-established practice that is adopted by entire healthcare institutions, as well as individual providers, in the US, as well as other countries (Jawhari et al., 2016), to improve the quality of care nowadays (Palabindala, Pamarthy, & Jonnalagadda, 2016). Thus, the topic is appropriate for translation science, which can be proven through the analysis of pertinent literature.

Description of Selected Advanced Nursing Practice Translation of Evidence into Practice

In the process of conducting the systematic review for this paper, a table of reviewed articles was prepared (see Appendix A). The model described by Spruce, Wicklin, Hicks, Conner, and Dunn (2014) was employed for evaluation purposes. Only recent, high-quality, peer-reviewed articles were selected; in addition, some studies were excluded because they represented only very specific settings or conditions. The results of the review are integrated below.

The majority of the considered sources are systematic literature reviews, but there is also one non-systematic literature review, one quantitative survey, and one qualitative descriptive study. Almost all sources (except for the non-systematic literature review) have level III of evidence because the employed model views qualitative studies and reviews of qualitative studies as third-level evidence. However, the topic of practice adoption procedures can be studied more extensively through qualitative methods, which explains this issue.

Not all the studies focus on both facilitators and barriers to adoption; Jawhari et al. (2016), Kruse, Kristof, Jones, Mitchell, and Martinez (2016), and Palabindala et al. (2016) consider only the barriers. Also, some of the articles have specific focuses like long-term facilities or disadvantaged urban slums. However, the majority of the findings are either similar or complementary, which allows integrating them.

All the authors find that financial concerns are a major barrier. Resources are highlighted by four authors; they are very explicitly considered in detail by Jawhari et al. (2016), who describe disadvantaged regions. Human resource issues are another category that has been mentioned by most authors, and this factor includes the need for training, managerial support, use of incentives, motivation, perception management, and some other considerations. Kruse et al. (2016), as well as Kruse, Mileski, Alaytsev, Carol, and Williams (2015), also specifically discuss the issues related to change, especially resistance. The problems are also indirectly mentioned by most authors in the analysis of the possible temporary loss of productivity. Finally, the topic of legal issues, especially privacy concerns, has also been considered.

Regarding facilitators, only three authors provided data on the topic. Jamoom, Patel, Furukawa, and King (2014) suggest that the availability of technical support is important. Kruse, Kothman, Anerobi, and Abanaka (2016) report multiple facilitators, but they can be united into the perceived or witnessed benefits of EHR adoption. Finally, Kruse et al. (2015) repeat the idea but also focus on leadership aspects, including managerial support.

In summary, the authors do not appear to provide opposing evidence; rather, their findings are complementary. Also, their results can be found to support each other’s conclusions. It is noteworthy that barriers have been researched to a greater extent, which may indicate the interest of the researchers in the potential risks of EHR adoption. Still, the presented review demonstrates that the chosen topic is extensively studied by modern translation science, which reflects its significance for practice.

Presentation of Selected Advanced Practice Translation of Evidence Analysis with Proposed Management (Structure-Process-Outcomes) for Information Systems Change

By applying the Structure-Process-Outcome (SPO) model, one can describe the chosen translation science topic and propose some guidelines for the management of a related information systems adoption or change in specific settings (Brosnan, 2016). From the perspective of structure, the facilitators and barriers that are related to organizational support, culture, and staff management can be noted, and the resource issues can be connected to the process of care. Therefore, to manage and monitor change, it is important to develop measures that would focus on the mentioned aspects of the model. For example, to enhance organizational support, the establishment of feedback from the adopters is required (Hanrahan et al., 2015), which should also provide the data on the availability and need for resources.

Apart from that, certain aspects of the process element can be employed to monitor progress. In particular, the expected outcomes of EHR adoption include cost-effectiveness, reduced incidence of errors, and improved time management (Kruse et al., 2015; Westra et al., 2015), all of which are measurable and can be used for the management and evaluation of the effort. Eventually, all the mentioned issues and advantages can affect patient outcomes, which is why the latter should be monitored. EHRs have been evidenced to affect clinical outcomes (Westra et al., 2015), so this measurement option should also be considered. Thus, the management and monitoring of the three elements of the model can assist in the process of handing the described information systems change.

During this project, the role of a DNP-prepared nurse can be multifaceted. On the one hand, as a leader, the nurse would be able to lead the information system change (possibly, collectively). In the process, the nurse would be required to pay particular attention to the potential barriers and facilitators, especially those related to human resources, for example, in improving motivation and managing perceptions (Hussey, Adams, & Shaffer, 2015; Sherrod & Goda, 2016).

From this perspective, the nurse will contribute to the sustainability of change (Hanrahan et al., 2015; Spear, 2016). On the other hand, as a scholar, a DNP-prepared nurse can be engaged in the process of project evaluation, which, in turn, can be employed to contribute to translation science (Udlis & Mancuso, 2015). By monitoring and evaluating the process of adoption and change with the help of their informatics and research skills, a DNP-prepared nurse will be able to contribute to the growing bulk of evidence on the discussed topic.


In summary, the consideration of the barriers and facilitators of EHR adoption is an important topic in translation science that can facilitate the process of EHR integration in a particular setting. The review of the pertinent and recent research proves this idea and also demonstrates that the barriers to the process may be more extensively represented in modern literature. Also, the studies do not present opposing findings and tend to complement each other, but none of them claims to have exhausted the topic.

The SPO model can help to determine the aspects of the management and evaluation of a project devoted to the topic, and the role of a DNP-prepared nurse in it can be concerned with leadership and scholarship. The application of the translation science approach to the subject of EHR adoption ensures the provision of the data on the process, which can assist future adopters. Given the complexity of the process of information systems change, the contribution of the discussed projects can be notable, and the DNP-prepared nurses can play major parts in them.

Appendix A

Source and Database Variables of Interest
Literature Type and Research Tools Research Design and Sample Size Theoretical Foundation # References and SWOT Critique Key Findings
(Jamoom, Patel, Furukawa, & King, 2014), US, Elsevier BV
  • electronic health records,
  • health information technology,
  • adopters,
  • non-adopters,
  • barriers
Level of Evidence=III
-National Ambulatory Medical Care Surveys
Quantitative survey with multivariable logistic regression models for their analysis.
None #=20

  • S= big sample, recent sources
  • W= self-reported data
  • O= understudied topic, potential for future research and practice
  • T= no empiric support for the claims (explained by the design)


  • Both adopters and non-adopters may believe EHRs to be beneficial to practice (clinically and financially).
  • Most of the participants find EHRs beneficial.
  • Key barriers are financial; also, productivity loss is a concern.
  • Influences: financial (both incentives and penalties), potential benefits (for example, information exchange), and assistance with technical issues.
(Jawhari et al., 2016), Ireland, Elsevier BV
  • electronic medical records,
  • urban slums,
  • disadvantaged populations,
  • barriers to adoption
Level of Evidence=III,
Semi-structured interview questionnaires
Descriptive qualitative study with a content analysis of the interview data.
N=10 (saturation reached).
None #=47

  • S= reached saturation, used predominantly recent sources
  • W= only the representatives of two clinics in Kibera, Nairobi were interviewed
  • O=important conclusions regarding an understudied topic and basis for future research
  • T= prior relationship between researcher and interviewed


  • In disadvantaged regions with increased healthcare burdens, there may be specific barriers and concerns to EHR use.
  • Barriers include: systems-related ones (power, hardware, and network availability), software-related ones (software functionality, confidentiality concerns, support), human resource issues (motivation and training), and financial issues.
(Kruse, Kothman, Anerobi, & Abanaka, 2016), US, PMC
  • electronic health records,
  • adoption,
  • adoption factors
Level of Evidence=III Systematic literature review of various types of research (including qualitative), N=31 None #=40
S= uses mostly recent sources, triangulation
W=limited to the United States
O=provides systematic review evidence on facilitators and barriers to EHR adoption
T= searched only two databases
  • Key facilitators include: the positive outcomes of adoption (efficiency, cost reduction, error prevention), perceptions, motivational factors.
  • Key barriers include: resource shortage (for example, staff); the lack of infrastructure, training, maintenance, and support; outdated technology; financial concerns.
(Kruse, Kristof, Jones, Mitchell, & Martinez, 2016), US, Springer Nature
  • electronic health records,
  • adoption,
  • adoption factors,
Level of Evidence=III Systematic literature review of various types of research (including qualitative), N=27 None #=30
S= only recent sources, thorough prevention of bias
W=inclusion of only recent studies (can be explained by the design choices and aims)
O=provides systematic review evidence on barriers to EHR adoption
T= publication bias
  • Key barriers include: financial concerns (especially initial costs), legal concerns (for example, privacy), resource concerns (time, equipment), maintenance concerns (support), training, perceptions, change resistance, loss of productivity, and organizational culture specifics.
(Kruse, Mileski, Alaytsev, Carol, & Williams, 2015), US, BMJ
  • electronic health records,
  • adoption,
  • adoption factors,
  • long-term care facilities
Level of Evidence=III Systematic literature review of various types of research (including qualitative), N=22 None #=31
S=review of current sources on an understudied topic; thorough triangulation
W= relatively few sources (can be explained by the lack of research on the topic).
O= can be an exhaustive review of modern evidence on the topic
T=generalized all long-term facilities (can be justified by legislative coverage)
  • Key facilitators include: positive outcomes of adoption (reduction of costs, time, and errors; improved efficiency; positive response of patients) and other motivational factors, the support of management; empowerment and engagement of patient.
  • Key barriers include: financial concerns (especially initial costs), resource concerns (especially time); human resources issues (especially perceptions and resistance to change), training, cultural specifics, difficulties in implementation, and legal concerns (privacy issues).
(Palabindala, Pamarthy, & Jonnalagadda, 2016), UK, Informa UK Limited
  • electronic health records,
  • adoption,
  • meaningful use,
  • barriers to adoption
Level of Evidence=V Non-systematic literature review, N=11 sources. None #=11
S= current sources (especially statistics), focuses on the topical issue of barriers to EHR implementation
W= no critical review of collected information
O=important conclusions on the minimization of EHR-related risks
T= few sources
  • There is a general agreement that EHRs are beneficial to practice.
  • Barriers and drawbacks include resource use (financial concerns among other things), communication with the vendor, transition phase and increased error risks during it, consequent legal considerations.

Note. Manuscripts reviewed total 6, which incorporated the review of an additional 179 citations.


Brosnan, C. (2016). Conceptual models for evaluation in advanced nursing practice. In J. Hickey & C. Brosnan (Eds.), Evaluation of health care quality for DNPs (pp. 61-86). New York, NY: Springer Publishing Company.

Hanrahan, K., Wagner, M., Matthews, G., Stewart, S., Dawson, C., Greiner, J.,… Williamson, A. (2015). . Worldviews on Evidence-Based Nursing, 12(1), 3-11. Web.

Hussey, P., Adams, E., & Shaffer, F. (2015). . Nurse Leader, 13(5), 52-57. Web.

Jamoom, E., Patel, V., Furukawa, M., & King, J. (2014). . Healthcare, 2(1), 33-39. Web.

Jawhari, B., Keenan, L., Zakus, D., Ludwick, D., Isaac, A., Saleh, A., & Hayward, R. (2016). . International Journal of Medical Informatics, 94, 246-254. Web.

Kruse, C., Kothman, K., Anerobi, K., & Abanaka, L. (2016). . JMIR Medical Informatics, 4(2), e19. Web.

Kruse, C., Kristof, C., Jones, B., Mitchell, E., & Martinez, A. (2016). . Journal of Medical Systems, 40(12), 252. Web.

Kruse, C., Mileski, M., Alaytsev, V., Carol, E., & Williams, A. (2015). . BMJ Open, 5(1), e006615. Web.

Palabindala, V., Pamarthy, A., & Jonnalagadda, N. (2016). . Journal of Community Hospital Internal Medicine Perspectives, 6(5), 32643. Web.

Sherrod, B., & Goda, T. (2016). . Nursing Management (Springhouse), 47(9), 13-16. Web.

Spear, M. (2016). . Plastic Surgical Nursing, 36(2), 58-61. Web.

Spruce, L., Wicklin, S. A., Hicks, R. W., Conner, R., & Dunn, D. (2014). . AORN Journal, 99(2), 243-255. Web.

Titler, M. G. (2014). Overview of evidence-based practice and translation science. In D. Mark, M. G. Titler & R. W. Latimer (Eds.), Integrating Evidence into Practice for Impact (pp. 269-274). Amsterdam, Netherlands: Elsevier Health Sciences.

Titler, M. G., Adams, S., & Cameron, C. (2014). Developing and evidence-informed practice. In G. LoBiondo-Wood, G. Haber, J. Cameron & M. Singh (Eds.), Nursing research in Canada (pp. 479-512). Amsterdam, Netherlands: Elsevier Health Sciences.

Udlis, K., & Mancuso, J. (2015). . Journal of Professional Nursing, 31(4), 274-283. Web.

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