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The effects of clinical decision support system for prescribing medication on patient outcomes and physician practice performance: a systematic review and meta-analysis

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Taheri Moghadam, Sharare, Sadoughi, Farahnaz, Velayati, Farnia, Ehsanzadeh, Seyed Jafar, Poursharif, Shayan·2021·BMC Medical Informatics and Decision Making
DOI 10.1186/s12911-020-01376-8PMC PMC7944637

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Abstract

Background: Clinical Decision Support Systems (CDSSs) for Prescribing are one of the innovations designed to improve physician practice performance and patient outcomes by reducing prescription errors. This study was therefore conducted to examine the effects of various CDSSs on physician practice performance and patient outcomes. Methods: This systematic review was carried out by searching PubMed, Embase, Web of Science, Scopus, and Cochrane Library from 2005 to 2019. The studies were independently reviewed by two researchers. Any discrepancies in the eligibility of the studies between the two researchers were then resolved by consulting the third researcher. In the next step, we performed a meta-analysis based on medication subgroups, CDSS-type subgroups, and outcome categories. Also, we provided the narrative style of the findings. In the meantime, we used a random-effects model to estimate the effects of CDSS on patient outcomes and physician practice performance with a 95% confidence interval. Q statistics and I 2 were then used to calculate heterogeneity. Results: On the basis of the inclusion criteria, 45 studies were qualified for analysis in this study. CDSS for prescription drugs/COPE has been used for various diseases such as cardiovascular diseases, hypertension, diabetes, gastrointestinal and respiratory diseases, AIDS, appendicitis, kidney disease, malaria, high blood potassium, and mental diseases. In the meantime, other cases such as concurrent prescribing of multiple medications for patients and their effects on the above-mentioned results have been analyzed. The study shows that in some cases the use of CDSS has beneficial effects on patient outcomes and physician practice performance (std diff in means = 0.084, 95% CI 0.067 to 0.102). It was also statistically significant for outcome categories such as those demonstrating better results for physician practice performance and patient outcomes or both. However, there was no significant difference between some other cases and traditional approaches. We assume that this may be due to the disease type, the quantity, and the type of CDSS criteria that affected the comparison. Overall, the results of this study show positive effects on performance for all forms of CDSSs. Conclusions: Our results indicate that the positive effects of the CDSS can be due to factors such as user-friendliness, compliance with clinical guidelines, patient and physician cooperation, integration of electronic health records, CDSS, and pharmaceutical systems, consideration of the views of physicians in assessing the importance of CDSS alerts, and the real-time alerts in the prescription.

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Cited by

Sorted by citations

Authors
Taheri Moghadam, S., Sadoughi, F., Velayati, F., Ehsanzadeh, S. J., & Poursharif, S.
Affiliations
  • Iran University of Medical Sciences (IR)
  • University of Alberta (CA)
Corresponding author
Sadoughi, Farahnaz
ORCID
Venue
BMC Medical Informatics and Decision Making
Publisher
Springer Science and Business Media LLC
Published
2021-03-10
Volume
21
Issue
1
Pages
98-98
ISSN
1472-6947 (ISSN-L)
DOI
10.1186/s12911-020-01376-8
PMC
PMC7944637
OpenAlex
W3117705198
Cited by
136
References
9
Citations by year
2026: 29 · 2025: 39 · 2024: 20 · 2023: 26 · 2022: 15 · 2021: 6 · 2020: 1
FWCI
2.97
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Medical Informatics and Decision Making (publishedVersion, cc-by)
  • Research Square (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Iran University of Medical Science (IUMS / SHMIS-96-04-136-32465)
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
clinical decision support systems, drug prescribing, medication errors, patient outcomes, electronic prescribing, clinical guidelines adherence, electronic health records, medication alerts, polypharmacy
MeSH terms
Humans, Physicians, Decision Support Systems, Clinical, Prescriptions, Electronic Health Records

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