Features predicting the success of computerized decision support for prescribing: a systematic review of randomized controlled trials
Open accessAbstract
BACKGROUND: Computerized decision support systems (CDSS) are believed to have the potential to improve the quality of health care delivery, although results from high quality studies have been mixed. We conducted a systematic review to evaluate whether certain features of prescribing decision support systems (RxCDSS) predict successful implementation, change in provider behaviour, and change in patient outcomes. METHODS: A literature search of Medline, EMBASE, CINAHL and INSPEC databases (earliest entry to June 2008) was conducted to identify randomized controlled trials involving RxCDSS. Each citation was independently assessed by two reviewers for outcomes and 28 predefined system features. Statistical analysis of associations between system features and success of outcomes was planned. RESULTS: Of 4534 citations returned by the search, 41 met the inclusion criteria. Of these, 37 reported successful system implementations, 25 reported success at changing health care provider behaviour, and 5 noted improvements in patient outcomes. A mean of 17 features per study were mentioned. The statistical analysis could not be completed due primarily to the small number of studies and lack of diversity of outcomes. Descriptive analysis did not confirm any feature to be more prevalent in successful trials relative to unsuccessful ones for implementation, provider behaviour or patient outcomes. CONCLUSION: While RxCDSSs have the potential to change health care provider behaviour, very few high quality studies show improvement in patient outcomes. Furthermore, the features of the RxCDSS associated with success (or failure) are poorly described, thus making it difficult for system design and implementation to improve.
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Interventions that demonstrated clear and consistent clinical evidence of benefit or good applicability relevant to setting were shown to facilitate implementation.
“interventions that demonstrated clear and consistent clinical evidence of benefit [3, 16, 34, 35, 51, 55, 56, 68, 74, 89] or good applicability relevant to setting [3, 35, 68] were shown to facilitate implementation.”
From Achieving change in primary care—causes of the evidence to practice gap: systematic reviews of reviews (2015) · cites this paper as [74]
The Results section (Association Between CDSS Features and Outcomes; no page available) says the review could not statistically evaluate which features were associated with success, and feature prevalence was generally similar in successful and unsuccessful implementation studies. The Discussion likewise says the descriptive comparison yielded no clear message. It therefore does not show that evidence of benefit or applicability to the setting facilitated implementation. (LLM, conf=0.88)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredWhen the fit between the intervention and existing workflow and systems was good, work process was improved.
“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 [74]
The Background reports that earlier reviews linked decision support synchronized with clinicians’ usual workflow to successful clinical decision support. But the Results (Association Between CDSS Features and Outcomes) found workflow and integration features similarly prevalent in successful and unsuccessful studies; this review does not establish that a good fit improved work processes. (LLM, conf=0.86)
Experimental — accuracy not yet measuredInadequate reporting of system or organizational features in intervention studies has hindered evaluation of what predicts successful CDSS implementation.
“However, insufficient attention to the reporting of these features in intervention studies has hampered these approaches.14”
From Computerized clinical decision support for prescribing: provision does not guarantee uptake (2010) · cites this paper as [14]
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- Authors
- Mollon, B., Chong, J. J., Holbrook, A. M., Sung, M., Thabane, L., & Foster, G.
- Affiliations
- McMaster University (CA)
- Corresponding author
- Holbrook, Anne M
- Venue
- BMC Medical Informatics and Decision Making
- Published
- 2009-02-11
- Volume
- 9
- Issue
- 1
- Pages
- 11-11
- ISSN
- 1472-6947 (ISSN-L)
- PMC
- PMC2667396
- OpenAlex
- W2017372316
- Cited by
- 124
- References
- 67
- Citations by year
- 2025: 4 · 2023: 8 · 2021: 1 · 2020: 7 · 2019: 3 · 2018: 5 · 2017: 9 · 2016: 8 · 2015: 8 · 2014: 11 · 2013: 12 · 2012: 12
- FWCI
- 2.46
- 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)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Technology and Human Factors in Education and Health
- Keywords
- computerized decision support systems, system characteristics, implementation success, patient outcomes, drug safety, system design
- MeSH terms
- Attitude of Health Personnel, Drug Therapy, Computer-Assisted, Humans, Quality Assurance, Health Care, Randomized Controlled Trials as Topic, Decision Support Systems, Clinical, Medical Order Entry Systems
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