Clinical Decision Support Systems to Improve Antibiotic Prescribing in Community and Clinical Pharmacy: A Systematic Review
DOI:
https://doi.org/10.22146/ijp.25049Keywords:
Clinical Decision Support System, Primary Care, Antibiotic Prescribing, COM-B Model, Systematic Literature Review (SLR)Abstract
Objectives:
Clinical Decision Support Systems (CDSS) improve healthcare providers' compliance with antibiotic therapy guidelines and reduce antimicrobial resistance. This study evaluated the effectiveness of CDSS and identified factors influencing its success using the Capability, Opportunity, and Motivation (COM-B Model) as an innovative analytical framework. This approach provides a new perspective in assessing CDSS implementation compared to previous study approaches.
Methods:
Literature reviews were conducted by finding articles from Scopus, PubMed, and ScienceDirect. The studies analyzed included RCTs and observational studies published in the last 10 years (2014–2024). The quality assessment used the Cochrane RoB Tool for RCTs and NOS for observational studies. The analysis was based on the COM-B Model to evaluate the capability, opportunity, and motivation factors in implementing CDSS.
Results:
A total of 12 studies (6 RCTs and 6 observational) were analyzed. The findings indicate that RCT studies primarily assess the causal impact of CDSS on antibiotic prescribing adherence. In contrast, observational studies explore real-world implementation factors, including system integration and clinician adoption. Most studies demonstrate that CDSS improves adherence to antibiotic prescribing guidelines, although variations exist depending on healthcare infrastructure, clinician workload, and policy support.
Conclusion:
CDSS can improve healthcare providers compliance, but its effectiveness depends on user capabilities, system support, and healthcare providers motivation. Key barriers include lack of training, limited integration, and resistance to technology, while policy support, incentives, and ongoing training can increase CDSS adoption. The implementation strategy must be user-based to make the system optimal in clinical practice.
References
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8. Wubishet BL, Merlo G, Ghahreman-Falconer N, Hall L, Comans T. Economic evaluation of antimicrobial stewardship in primary care: a systematic review and quality assessment. J Antimicrob Chemother [Internet]. 2022 Aug 25;77(9):2373–88. Available from: https://academic.oup.com/jac/article/77/9/2373/6611846
9. Yau JW, Thor SM, Tsai D, Speare T, Rissel C. Antimicrobial stewardship in rural and remote primary health care: a narrative review. Antimicrob Resist Infect Control [Internet]. 2021;10(1):1–33. Available from: https://doi.org/10.1186/s13756-021-00964-1
10. Gifford J, Vaeth E, Richards K, Siddiqui T, Gill C, Wilson L, et al. Decision support during electronic prescription to stem antibiotic overuse for acute respiratory infections: a long-term, quasi-experimental study. BMC Infect Dis [Internet]. 2017 Dec 31;17(1):528. Available from: https://bmcinfectdis.biomedcentral.com/articles/10.1186/s12879-017-2602-7
11. Acosta-García H, Ruano-Ruiz J, Gómez-García FJ, Sánchez-Fidalgo S, Santos-Ramos B, Molina-López T. Computerized clinical decision support systems for prescribing in primary care: Characteristics and implementation impact. Scoping review and evidence and gap maps. Heal Policy Technol [Internet]. 2025 Mar 1 [cited 2025 Feb 11];14(2):100976. Available from: https://linkinghub.elsevier.com/retrieve/pii/S2211883725000048
12. Yang J, Cui Z, Liao X, He X, Wang L, Wei D, et al. Effects of a feedback intervention on antibiotic prescription control in primary care institutions based on a Health Information System: a cluster randomized cross-over controlled trial. J Glob Antimicrob Resist [Internet]. 2023 Jun;33:51–60. Available from: https://www.sciencedirect.com/science/article/pii/S2213716523000231
13. Mann D, Hess R, McGinn T, Richardson S, Jones S, Palmisano J, et al. Impact of Clinical Decision Support on Antibiotic Prescribing for Acute Respiratory Infections: a Cluster Randomized Implementation Trial. J Gen Intern Med [Internet]. 2020 Nov 1;35(S2):788–95. Available from: https://link.springer.com/10.1007/s11606-020-06096-3
14. Neugebauer M, Ebert M, Vogelmann R. A clinical decision support system improves antibiotic therapy for upper urinary tract infection in a randomized single-blinded study. BMC Health Serv Res [Internet]. 2020 Dec 6;20(1):185. Available from: https://bmchealthservres.biomedcentral.com/articles/10.1186/s12913-020-5045-6
15. Gulliford MC, van Staa T, Dregan A, McDermott L, McCann G, Ashworth M, et al. Electronic Health Records for Intervention Research: A Cluster Randomized Trial to Reduce Antibiotic Prescribing in Primary Care (eCRT Study). Ann Fam Med [Internet]. 2014 Jul 1;12(4):344–51. Available from: http://www.annfammed.org/cgi/doi/10.1370/afm.1659
16. Gulliford MC, Prevost AT, Charlton J, Juszczyk D, Soames J, McDermott L, et al. Effectiveness and safety of electronically delivered prescribing feedback and decision support on antibiotic use for respiratory illness in primary care: REDUCE cluster randomised trial. BMJ [Internet]. 2019 Feb 12;364:l236. Available from: https://www.bmj.com/lookup/doi/10.1136/bmj.l236
17. Jeanmougin P, Larramendy S, Fournier JP, Gaultier A, Rat C. Effect of a Feedback Visit and a Clinical Decision Support System Based on Antibiotic Prescription Audit in Primary Care: Multiarm Cluster-Randomized Controlled Trial. J Med Internet Res [Internet]. 2024 Dec 18;26:e60535. Available from: https://www.jmir.org/2024/1/e60535
18. Delory T, Jeanmougin P, Lariven S, Aubert JP, Peiffer-Smadja N, Boëlle PY, et al. A computerized decision support system (CDSS) for antibiotic prescription in primary care—Antibioclic: implementation, adoption and sustainable use in the era of extended antimicrobial resistance. J Antimicrob Chemother [Internet]. 2020 May 1;75(8):2353–62. Available from: https://pubmed.ncbi.nlm.nih.gov/32357226/
19. Engel ME, Cohen K, Gounden R, Kengne AP, Barth DD, Whitelaw AC, et al. The Cape Town Clinical Decision Rule for Streptococcal Pharyngitis in Children. Pediatr Infect Dis J [Internet]. 2017 Mar;36(3):250–5. Available from: https://journals.lww.com/00006454-201703000-00002
20. Herter WE, Khuc J, Cinà G, Knottnerus BJ, Numans ME, Wiewel MA, et al. Impact of a Machine Learning–Based Decision Support System for Urinary Tract Infections: Prospective Observational Study in 36 Primary Care Practices. JMIR Med Informatics [Internet]. 2022 May 4;10(5):e27795. Available from: https://medinform.jmir.org/2022/5/e27795
21. Delory T, Maillard A, Tubach F, Böelle PY, Bouvet E, Lariven S, et al. Appropriateness of intended antibiotic prescribing using clinical case vignettes in primary care, and related factors. Eur J Gen Pract [Internet]. 2024 Dec 31;30(1). Available from: https://doi.org/10.1080/13814788.2024.2351811
22. Hürlimann E, Landi M, Miikkulainen A, Renner C, Musard C, Mohamed HH, et al. Impact of digital clinical decision support on quality of care and antibiotic stewardship for children under five in South-Central Somalia. Oxford Open Digit Heal [Internet]. 2024 Nov 28;2(Supplement_2):ii32–44. Available from: https://academic.oup.com/oodh/article/2/Supplement_2/ii32/7911913
23. Neto V, Estrela M, Ribeiro AF, Novais A, Neves C, Zapata-Cachafeiro M, et al. Tackling antibiotic resistance—insights from eHealthResp’s educational interventions. npj Prim Care Respir Med [Internet]. 2024 Nov 13;34(1):37. Available from: http://dx.doi.org/10.1038/s41533-024-00388-5
24. Serin O, Akbasli IT, Bocutcu Cetin S, Koseoglu B, Deveci AF, Ugur MZ, et al. Advancing primary care for childhood pneumonia: a machine learning-based approach to prognosis and case management [Internet]. Ankara; 2024. p. 0–3. Available from: http://medrxiv.org/lookup/doi/10.1101/2024.02.22.24303209
25. Gomez C, Smith BL, Zayas A, Unberath M, Canares T. Explainable AI decision support improves accuracy during telehealth strep throat screening. Commun Med [Internet]. 2024 Jul 24;4(1):149. Available from: http://dx.doi.org/10.1038/s43856-024-00568-x
26. Zikos D, DeLellis N. CDSS-RM: a clinical decision support system reference model. BMC Med Res Methodol [Internet]. 2018 Dec 16;18(1):137. Available from: https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-018-0587-6
27. Liberati EG, Ruggiero F, Galuppo L, Gorli M, González-Lorenzo M, Maraldi M, et al. What hinders the uptake of computerized decision support systems in hospitals? A qualitative study and framework for implementation. Implement Sci [Internet]. 2017 Dec 15;12(1):113. Available from: http://implementationscience.biomedcentral.com/articles/10.1186/s13012-017-0644-2
28. Ford E, Edelman N, Somers L, Shrewsbury D, Lopez Levy M, van Marwijk H, et al. Barriers and facilitators to the adoption of electronic clinical decision support systems: a qualitative interview study with UK general practitioners. BMC Med Inform Decis Mak [Internet]. 2021 Dec 21;21(1):193. Available from: https://doi.org/10.1186/s12911-021-01557-z
29. Frisinger A, Papachristou P. The voice of healthcare: introducing digital decision support systems into clinical practice - a qualitative study. BMC Prim Care [Internet]. 2023 Mar 13;24(1):67. Available from: https://doi.org/10.1186/s12875-023-02024-6
30. Tegenaw GS, Amenu D, Ketema G, Verbeke F, Cornelis J, Jansen B. Evaluating a clinical decision support point of care instrument in low resource setting. BMC Med Inform Decis Mak [Internet]. 2023 Mar 30;23(1):51. Available from: https://doi.org/10.1186/s12911-023-02144-0
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Published
2026-04-16
How to Cite
Nita, Y., Rokayah, R., & Alfian, R. (2026). Clinical Decision Support Systems to Improve Antibiotic Prescribing in Community and Clinical Pharmacy: A Systematic Review. Indonesian Journal of Pharmacy. https://doi.org/10.22146/ijp.25049
Issue
Section
Review Article
