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Barriers and facilitators to implementing electronic prescription: a systematic review of user groups' perceptions
Open accessAbstract
OBJECTIVE: We conducted a systematic review identifying users groups' perceptions of barriers and facilitators to implementing electronic prescription (e-prescribing) in primary care. METHODS: We included studies following these criteria: presence of an empirical design, focus on the users' experience of e-prescribing implementation, conducted in primary care, and providing data on barriers and facilitators to e-prescribing implementation. We used the Donabedian logical model of healthcare quality (adapted by Barber et al) to analyze our findings. RESULTS: We found 34 publications (related to 28 individual studies) eligible to be included in this review. These studies identified a total of 594 elements as barriers or facilitators to e-prescribing implementation. Most user groups perceived that e-prescribing was facilitated by design and technical concerns, interoperability, content appropriate for the users, attitude towards e-prescribing, productivity, and available resources. DISCUSSION: This review highlights the importance of technical and organizational support for the successful implementation of e-prescribing systems. It also shows that the same factor can be seen as a barrier or a facilitator depending on the project's own circumstances. Moreover, a factor can change in nature, from a barrier to a facilitator and vice versa, in the process of e-prescribing implementation. CONCLUSIONS: This review summarizes current knowledge on factors related to e-prescribing implementation in primary care that could support decision makers in their design of effective implementation strategies. Finally, future studies should emphasize on the perceptions of other user groups, such as pharmacists, managers, vendors, and patients, who remain neglected in the literature.
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“in relation to e-prescribing, clinicians did not want to solve implementation problems and believed this should be done by non-clinical staff [14].”
From Achieving change in primary care—causes of the evidence to practice gap: systematic reviews of reviews (2015) · cites this paper as [14]
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“Resistance to change caused by disagreement with the evidence, negative beliefs about the usefulness or added value of the intervention or belief that the intervention was not part of their role were commonly described as a barrier to implementation [3, 14, 35, 41, 67–70, 76, 77, 80].”
From Achieving change in primary care—causes of the evidence to practice gap: systematic reviews of reviews (2015) · cites this paper as [14]
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“Existing reviews tend to focus either on a particular type of complex intervention (e.g. introduction of new technologies [14] or promoting uptake and use of guidelines [15]) or on a particular health condition (e.g. mental health [16] or diabetes [17]).”
From Achieving change in primary care—causes of the evidence to practice gap: systematic reviews of reviews (2015) · cites this paper as [14]
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“When the fit was good [14, 73, 74], for example, when e-prescribing was sufficiently integrated as part of clinician workflow, work process was improved [14].”
From Achieving change in primary care—causes of the evidence to practice gap: systematic reviews of reviews (2015) · cites this paper as [14]
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“Contextual factors that are perceived as barriers at the beginning of implementation may become facilitators later on in the implementation process [14].”
From Achieving change in primary care—causes of the evidence to practice gap: systematic reviews of reviews (2015) · cites this paper as [14]
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“work processes were viewed as a barrier during the transition phase but became a facilitator when the intervention was formally integrated and work flow was improved [14].”
From Achieving change in primary care—causes of the evidence to practice gap: systematic reviews of reviews (2015) · cites this paper as [14]
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“While the users had a less positive view of the intervention during pre-implementation phase, their views became more positive with the increasing use of the intervention during the transition and post-implementation phases.”
From Achieving change in primary care—causes of the evidence to practice gap: systematic reviews of reviews (2015) · cites this paper as [14]
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“Relevant barriers and facilitators are dynamic and likely to change over time [14, 28].”
From Achieving change in primary care—causes of the evidence to practice gap: systematic reviews of reviews (2015) · cites this paper as [14]
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“The attitudes of colleagues [25, 41] and patients [25, 27, 41] were also reported to influence staff attitudes with regard to e-health acceptance as were staff demographic factors [23, 46, 56].”
From Factors that influence the implementation of e-health: a systematic review of systematic reviews (an update) (2016) · cites this paper as [27]
In the citing paper
“Specifically, fears over a loss of autonomy [16, 18, 27, 34, 37, 39, 43], concerns about liability [16, 18, 22, 37, 43, 51], concerns over patient privacy and security being compromised [16, 18, 20, 23, 24, 27, 28, 30, 31, 33, 39, 41, 43, 45, 47, 49, 51–53, 56, 57], and perceived threats to patient and health professional relationships [18, 25, 27, 28, 31, 34, 37–39, 41, 43, 54, 57] through the”
From Factors that influence the implementation of e-health: a systematic review of systematic reviews (an update) (2016) · cites this paper as [27]
In the citing paper
“Planning for implementation was important for success, whereas the lack of a strategic plan was reported as a barrier to e-health implementation [24, 27, 31, 44, 50].”
From Factors that influence the implementation of e-health: a systematic review of systematic reviews (an update) (2016) · cites this paper as [27]
In the citing paper
“Planning for implementation was important for success, whereas the lack of a strategic plan was reported as a barrier to e-health implementation [24, 27, 31, 44, 50].”
From Factors that influence the implementation of e-health: a systematic review of systematic reviews (an update) (2016) · cites this paper as [27]
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Metadata
- Authors
- Gagnon, M.-P., Nsangou, É.-R., Payne-Gagnon, J., Grenier, S., & Sicotte, C.
- Affiliations
- Université Laval (CA)
- Centre de recherche du CHU de Québec-Université Laval (CA)
- Centre hospitalier universitaire de Québec (CA)
- École des Hautes Études en Santé Publique (FR)
- Corresponding author
- Payne-Gagnon, Julie
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-05
- Volume
- 21
- Issue
- 3
- Pages
- 535-541
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC3994867
- OpenAlex
- W2114801561
- Cited by
- 132
- References
- 68
- Citations by year
- 2026: 8 · 2025: 12 · 2024: 13 · 2023: 10 · 2022: 10 · 2021: 12 · 2020: 8 · 2019: 18 · 2018: 12 · 2017: 3 · 2016: 15 · 2015: 9 · 2014: 2
- FWCI
- 1.46
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of the American Medical Informatics Association (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Health Professions › General Health Professions › Health Policy Implementation Science
- Social Sciences › Business, Management and Accounting › Management Information Systems › Information Systems Theories and Implementation
- Keywords
- electronic prescribing, barriers and facilitators, primary care, user perception, interoperability, technical support, organizational support, implementation strategies, Donabedian model
- MeSH terms
- Workforce, Attitude of Health Personnel, Attitude to Computers, Efficiency, Humans, Nurses, Pharmacists, Primary Health Care, Electronic Prescribing, Physicians, Primary Care
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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.
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