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Do computerised clinical decision support systems for prescribing change practice? A systematic review of the literature (1990-2007)
Open accessAbstract
BACKGROUND: Computerised clinical decision support systems (CDSSs) are used widely to improve quality of care and patient outcomes. This systematic review evaluated the impact of CDSSs in targeting specific aspects of prescribing, namely initiating, monitoring and stopping therapy. We also examined the influence of clinical setting (institutional vs ambulatory care), system- or user-initiation of CDSS, multi-faceted vs stand alone CDSS interventions and clinical target on practice changes in line with the intent of the CDSS. METHODS: We searched Medline, Embase and PsychINFO for publications from 1990-2007 detailing CDSS prescribing interventions. Pairs of independent reviewers extracted the key features and prescribing outcomes of methodologically adequate studies (experiments and strong quasi-experiments). RESULTS: 56 studies met our inclusion criteria, 38 addressing initiating, 23 monitoring and three stopping therapy. At the time of initiating therapy, CDSSs appear to be somewhat more effective after, rather than before, drug selection has occurred (7/12 versus 12/26 studies reporting statistically significant improvements in favour of CDSSs on = 50% of prescribing outcomes reported). CDSSs also appeared to be effective for monitoring therapy, particularly using laboratory test reminders (4/7 studies reporting significant improvements in favour of CDSSs on the majority of prescribing outcomes). None of the studies addressing stopping therapy demonstrated impacts in favour of CDSSs over comparators. The most consistently effective approaches used system-initiated advice to fine-tune existing therapy by making recommendations to improve patient safety, adjust the dose, duration or form of prescribed drugs or increase the laboratory testing rates for patients on long-term therapy. CDSSs appeared to perform better in institutional compared to ambulatory settings and when decision support was initiated automatically by the system as opposed to user initiation. CDSSs implemented with other strategies such as education were no more successful in improving prescribing than stand alone interventions. Cardiovascular disease was the most studied clinical target but few studies demonstrated significant improvements on the majority of prescribing outcomes. CONCLUSION: Our understanding of CDSS impacts on specific aspects of the prescribing process remains relatively limited. Future implementation should build on effective approaches including the use of system-initiated advice to address safety issues and improve the monitoring of therapy.
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“Despite these limitations, some reviews have demonstrated the benefits of computer-over paper-based decision support, 12 system-over user-initiated tools, 10 11 and integrated over stand-alone systems, 10 and the advantage of providing specific treatment recommendations or advice rather than a simple problem assessment requiring further consideration by end users.12”
From Computerized clinical decision support for prescribing: provision does not guarantee uptake (2010) · cites this paper as [10]
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“Despite these limitations, some reviews have demonstrated the benefits of computer-over paper-based decision support, 12 system-over user-initiated tools, 10 11 and integrated over stand-alone systems, 10 and the advantage of providing specific treatment recommendations or advice rather than a simple problem assessment requiring further consideration by end users.12”
From Computerized clinical decision support for prescribing: provision does not guarantee uptake (2010) · cites this paper as [10]
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Metadata
- Authors
- Pearson, S.-A., Moxey, A., Robertson, J., Hains, I., Williamson, M., Reeve, J., & Newby, D.
- Affiliations
- UNSW Sydney (AU)
- University of Newcastle Australia (AU)
- Corresponding author
- Pearson, Sallie-Anne
- Venue
- BMC Health Services Research
- Published
- 2009-08-28
- Volume
- 9
- Issue
- 1
- Pages
- 154-154
- ISSN
- 1472-6963 (ISSN-L)
- PMC
- PMC2744674
- OpenAlex
- W2080746135
- Cited by
- 193
- References
- 80
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 2 · 2023: 4 · 2022: 10 · 2021: 3 · 2020: 12 · 2019: 14 · 2018: 10 · 2017: 20 · 2016: 16 · 2015: 17 · 2014: 17 · 2013: 18 · 2012: 16
- FWCI
- 2.19
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Health Services Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- NOVA (University of Newcastle Australia) (submittedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Medicine › Geriatrics and Gerontology › Pharmaceutical Practices and Patient Outcomes
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- drug prescribing, drug initiation, treatment monitoring, patient safety, multicomponent intervention, cardiovascular disease
- MeSH terms
- Humans, Practice Patterns, Physicians', Drug Prescriptions, User-Computer Interface, Decision Support Systems, Clinical
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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