Journal:Factors associated with adoption of health information technology: A conceptual model based on a systematic review
| Full article title | Factors associated with adoption of health information technology: A conceptual model based on a systematic review |
|---|---|
| Journal | JMIR Medical Informatics |
| Author(s) | Kruse1, Clemens Scott; DeShazo, Jonathan; Forest, Kim; Fulton, Lawrence |
| Author affiliation(s) | Texas State University, Virginia Commonwealth University, Baylor University |
| Primary contact | Email: scottkruse@txstate.edu; Phone: 1.210.355.4742 |
| Editors | Eysenbach, G. |
| Year published | 2014 |
| Volume and issue | 2 (1) |
| Page(s) | e9 |
| DOI | 10.2196/medinform.3106 |
| ISSN | 2291-9694 |
| Distribution license | Creative Commons Attribution 2.0 |
| Website | http://medinform.jmir.org/2014/1/e9/ |
| Download | http://medinform.jmir.org/article/download/medinform_v2i1e9/2 (PDF) |
Abstract
Background: The Health Information Technology for Economic and Clinical Health Act (HITECH) allocated $19.2 billion to incentivize adoption of the electronic health record (EHR). Since 2009, Meaningful Use Criteria have dominated information technology (IT) strategy. Health care organizations have struggled to meet expectations and avoid penalties to reimbursements from the Center for Medicare and Medicaid Services (CMS). Organizational theories attempt to explain factors that influence organizational change, and many theories address changes in organizational strategy. However, due to the complexities of the health care industry, existing organizational theories fall short of demonstrating association with significant health care IT implementations. There is no organizational theory for health care that identifies, groups, and analyzes both internal and external factors of influence for large health care IT implementations like adoption of the EHR.
Objective: The purpose of this systematic review is to identify a full-spectrum of both internal organizational and external environmental factors associated with the adoption of health information technology (HIT), specifically the EHR. The result is a conceptual model that is commensurate with the complexity of with the health care sector.
Methods: We performed a systematic literature search in PubMed (restricted to English), EBSCO Host, and Google Scholar for both empirical studies and theory-based writing from 1993-2013 that demonstrated association between influential factors and three modes of HIT: EHR, electronic medical record (EMR), and computerized provider order entry (CPOE). We also looked at published books on organizational theories. We made notes and noted trends on adoption factors. These factors were grouped as adoption factors associated with various versions of EHR adoption.
Results: The resulting conceptual model summarizes the diversity of independent variables (IVs) and dependent variables (DVs) used in articles, editorials, books, as well as quantitative and qualitative studies (n=83). As of 2009, only 16.30% (815/4999) of nonfederal, acute-care hospitals had adopted a fully interoperable EHR. From the 83 articles reviewed in this study, 16/83 (19%) identified internal organizational factors and 9/83 (11%) identified external environmental factors associated with adoption of the EHR, EMR, or CPOE. The conceptual model for EHR adoption associates each variable with the work that identified it.
Conclusions: Commonalities exist in the literature for internal organizational and external environmental factors associated with the adoption of the EHR and/or CPOE. The conceptual model for EHR adoption associates internal and external factors, specific to the health care industry, associated with adoption of the EHR. It becomes apparent that these factors have some level of association, but the association is not consistently calculated individually or in combination. To better understand effective adoption strategies, empirical studies should be performed from this conceptual model to quantify the positive or negative effect of each factor.
Keywords: electronic health record (EHR); electronic medical record (EMR); health information technology (HIT); medical information systems; computerized provider order entry (CPOE); adoption
Introduction
Background
The US Government passed the Health Information Technology for Economic and Clinical Health (HITECH) act[1] to incentivize adoption of the electronic health record (EHR) and to assuage the short run (SR) effects of cost to the health care organization in the adoption process. The three phases of Meaningful Use consume information technology (IT) strategies in the SR because of the HITECH act’s timeline for health care organizations to qualify for monetary incentives.[2][3]
Adoption of the EHR is a significant goal. International vernacular for the EHR varies; for example, electronic patient record, computerized patient records, electronic medical records (EMRs), and digital medical record. The defining difference, as defined by the Institute of Medicine, the health arm of the US National Academy of Sciences, focuses on the longitudinal and interoperable nature of the electronic patient record.[4] Without these capabilities, the patient record is greatly limited in scope. The longitudinal and interoperable nuances of the EHR are not the only significant advantages; there are eventual cost savings as well.
Studies estimate that adoption of the EHR could eventually save more than $813 billion annually, prevent 200,000 adverse drug events, and enhance the doctor-patient relationship through increased communication.[5] Unfortunately, these benefits are realized in the long run (LR), while the investment to adopt the EHR is expended in the SR. A large deficit in the SR could inhibit a health care organization’s ability to compete or survive in heavily competitive environment.
The environment of health care is unique in a competitive environment. The health care organization develops an organizational strategy based on the local environment. To increase an organization’s ability to compete, its strategy might also include cost reduction, and EHR adoption runs counter to this goal in the SR. The health care environment faces many sources of influence, including a reluctance to accept technology.
There has been a tremendous amount of research dedicated to the study of acceptance of technology, specifically the Technology Acceptance Model (TAM).[6] More recent work has suggested modifications to the TAM that explain a perception of usefulness and intentions from the aspect of social influence and the cognitive instrumental process.[7][8] Several organizational theories have been developed. These focus on the sources of influence and the reason for their existence.
Conflicts of interest
None declared.
References
- ↑ "Title XIII: Health Information Technology for Economic and Clinical Health Act" (PDF). American Recovery and Reinvestment Act of 2009. ONC. 19 February 2009. https://www.healthit.gov/sites/default/files/hitech_act_excerpt_from_arra_with_index.pdf. Retrieved 18 March 2014.
- ↑ Elnahal, S.M.; Joynt, K.E.; Bristol, S.J.; Jha, A.K. (2011). "Electronic health record functions differ between best and worst hospitals". American Journal of Managed Care 17 (4): e121–e147. PMC PMC3335431. PMID 21774097. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3335431.
- ↑ Song, P.H.; McAlearney, A.S.; Robbins, J.; McCullough, J.S. (2011). "Exploring the business case for ambulatory electronic health record system adoption". Journal of Healthcare Management 56 (3): 169-80; discussion 181-2. PMID 21714372.
- ↑ "Key Capabilities of an Electronic Health Record System". National Academies Institute of Medicine. 31 July 2003. http://iom.nationalacademies.org/Reports/2003/Key-Capabilities-of-an-Electronic-Health-Record-System.aspx. Retrieved 20 March 2014.
- ↑ Hillestad, Richard; Bigelow, James; Bower, Anthony; Girosi, Federico; Meili, Robin; Scoville, Richard; Taylor, Roger (2005). "Can electronic medical record systems transform health care? Potential health benefits, savings, and costs". Health Affairs 24 (5): 1103–1117. doi:10.1377/hlthaff.24.5.1103. PMID 16162551.
- ↑ Davis, F. (1989). "Perceived usefulness, perceived ease of use, and user acceptance of information technology". MIS Quarterly 13 (3): 319–340. doi:10.2307/249008.
- ↑ Venkatesh, V; Davis, F.D. (2000). "A theoretical extension of the Technology Acceptance Model: four longitudinal field studies". Management Science 46 (2): 186–204. doi:10.1287/mnsc.46.2.186.11926.
- ↑ Venkatesh, V; Morris, M.; Davis, G.; Davis, F. (2003). "User acceptance of information technology: Toward a unified view". MIS Quarterly 27 (3): 425–478. doi:10.2307/30036540.
Abbreviations
Notes
This presentation is faithful to the original, with only a few minor changes to presentation. In several cases the PubMed ID was missing and was added to make the reference more useful.
Per the distribution agreement, the following copyright information is also being added:
©Clemens Scott Kruse, Jonathan DeShazo, Forest Kim, Lawrence Fulton. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 23.05.2014.
