The use of artificial intelligence to optimize medication alerts generated by clinical decision support systems: a scoping review

Author:

Graafsma Jetske1ORCID,Murphy Rachel M23ORCID,van de Garde Ewoudt M W45,Karapinar-Çarkit Fatma67,Derijks Hieronymus J8,Hoge Rien H L9,Klopotowska Joanna E23,van den Bemt Patricia M L A1

Affiliation:

1. Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, University of Groningen , Groningen, 9713GZ, The Netherlands

2. Department of Medical Informatics Amsterdam UMC, University of Amsterdam , Amsterdam, 1000GG, The Netherlands

3. Amsterdam Public Health Institute, Digital Health and Quality of Care , Amsterdam, 1105AZ, The Netherlands

4. Department of Pharmacy, St Antonius Hospital , Utrecht, 3430AM, The Netherlands

5. Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht University , Utrecht, 3584CS, The Netherlands

6. Department of Clinical Pharmacy and Toxicology, Maastricht University Medical Center , Maastricht, 6229HX, The Netherlands

7. Department of Clinical Pharmacy, CARIM, Cardiovascular Research Institute Maastricht, Maastricht University , Maastricht, 6229ER, The Netherlands

8. Department of Pharmacy, Jeroen Bosch Hospital , Den Bosch, 5200ME, The Netherlands

9. Department of Pharmacy, Wilhelmina Hospital , Assen, 9401RK, The Netherlands

Abstract

Abstract Objective Current Clinical Decision Support Systems (CDSSs) generate medication alerts that are of limited clinical value, causing alert fatigue. Artificial Intelligence (AI)-based methods may help in optimizing medication alerts. Therefore, we conducted a scoping review on the current state of the use of AI to optimize medication alerts in a hospital setting. Specifically, we aimed to identify the applied AI methods used together with their performance measures and main outcome measures. Materials and Methods We searched Medline, Embase, and Cochrane Library database on May 25, 2023 for studies of any quantitative design, in which the use of AI-based methods was investigated to optimize medication alerts generated by CDSSs in a hospital setting. The screening process was supported by ASReview software. Results Out of 5625 citations screened for eligibility, 10 studies were included. Three studies (30%) reported on both statistical performance and clinical outcomes. The most often reported performance measure was positive predictive value ranging from 9% to 100%. Regarding main outcome measures, alerts optimized using AI-based methods resulted in a decreased alert burden, increased identification of inappropriate or atypical prescriptions, and enabled prediction of user responses. In only 2 studies the AI-based alerts were implemented in hospital practice, and none of the studies conducted external validation. Discussion and Conclusion AI-based methods can be used to optimize medication alerts in a hospital setting. However, reporting on models’ development and validation should be improved, and external validation and implementation in hospital practice should be encouraged.

Funder

Dutch national Medicines Coordination Center

Landelijk Coördinatiecentrum Geneesmiddelen

Publisher

Oxford University Press (OUP)

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