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A knowledge-based taxonomy of critical factors for adopting electronic health record systems by physicians: a systematic literature review
Open accessAbstract
BACKGROUND: The health care sector is an area of social and economic interest in several countries; therefore, there have been lots of efforts in the use of electronic health records. Nevertheless, there is evidence suggesting that these systems have not been adopted as it was expected, and although there are some proposals to support their adoption, the proposed support is not by means of information and communication technology which can provide automatic tools of support. The aim of this study is to identify the critical adoption factors for electronic health records by physicians and to use them as a guide to support their adoption process automatically. METHODS: This paper presents, based on the PRISMA statement, a systematic literature review in electronic databases with adoption studies of electronic health records published in English. Software applications that manage and process the data in the electronic health record have been considered, i.e.: computerized physician prescription, electronic medical records, and electronic capture of clinical data. Our review was conducted with the purpose of obtaining a taxonomy of the physicians main barriers for adopting electronic health records, that can be addressed by means of information and communication technology; in particular with the information technology roles of the knowledge management processes. Which take us to the question that we want to address in this work: "What are the critical adoption factors of electronic health records that can be supported by information and communication technology?". Reports from eight databases covering electronic health records adoption studies in the medical domain, in particular those focused on physicians, were analyzed. RESULTS: The review identifies two main issues: 1) a knowledge-based classification of critical factors for adopting electronic health records by physicians; and 2) the definition of a base for the design of a conceptual framework for supporting the design of knowledge-based systems, to assist the adoption process of electronic health records in an automatic fashion. From our review, six critical adoption factors have been identified: user attitude towards information systems, workflow impact, interoperability, technical support, communication among users, and expert support. The main limitation of the taxonomy is the different impact of the adoption factors of electronic health records reported by some studies depending on the type of practice, setting, or attention level; however, these features are a determinant aspect with regard to the adoption rate for the latter rather than the presence of a specific critical adoption factor. CONCLUSIONS: The critical adoption factors established here provide a sound theoretical basis for research to understand, support, and facilitate the adoption of electronic health records to physicians in benefit of patients.
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![Figure 1 The innovation-diffusion process proposed by Rogers [9].](/paper-figures/f47dbfd0120f4af088c7512e73b7ad23/a-knowledge-based-taxonomy-of-critical-factors-for-adopting-electronic-health-figure-1.jpg)
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“Access to ongoing support to use systems was important for system use [21, 25, 28, 31, 37, 39, 43, 54] and a barrier to implementation when it was lacking [21].”
From Factors that influence the implementation of e-health: a systematic review of systematic reviews (an update) (2016) · cites this paper as [21]
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“Positive attitudes of practitioners toward e-health systems and their implementation increased acceptance and implementation [20, 21, 41, 44, 46, 49]”
From Factors that influence the implementation of e-health: a systematic review of systematic reviews (an update) (2016) · cites this paper as [21]
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“Positive staff attitudes were described as: beliefs that the new systems would benefit patients [56], interest in the technologies, perceived usefulness and motivation in working with the systems [21].”
From Factors that influence the implementation of e-health: a systematic review of systematic reviews (an update) (2016) · cites this paper as [21]
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Metadata
- Authors
- Castillo, V. H., Martínez-García, A. I., & Pulido, J.
- Affiliations
- Centro de Investigación Científica y de Educación Superior de Ensenada (MX)
- Universidad de Colima (MX)
- Corresponding author
- Castillo, Víctor H
- Venue
- BMC Medical Informatics and Decision Making
- Published
- 2010-10-15
- Volume
- 10
- Issue
- 1
- Pages
- 60-60
- ISSN
- 1472-6947 (ISSN-L)
- PMC
- PMC2970582
- OpenAlex
- W2107188450
- Cited by
- 155
- References
- 99
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 5 · 2023: 5 · 2022: 30 · 2021: 6 · 2020: 8 · 2019: 4 · 2018: 21 · 2017: 8 · 2016: 13 · 2015: 11 · 2014: 12 · 2013: 16 · 2012: 7
- FWCI
- 1.26
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Medical Informatics and Decision Making (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Universidad de Colima
- Consejo Nacional de Ciencia y Tecnología
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Business, Management and Accounting › Management Information Systems › Information Systems Theories and Implementation
- Keywords
- electronic health records, technology adoption, physicians, adoption factors, barriers to adoption, expert systems, knowledge management, interoperability, user attitudes, technical support, expert support, conceptual framework
- MeSH terms
- Attitude to Computers, Diffusion of Innovation, Practice Patterns, Physicians', Electronic Health Records
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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