TY - JOUR
T1 - The use of artificial intelligence to optimize medication alerts generated by clinical decision support systems
T2 - a scoping review
AU - Graafsma, Jetske
AU - Murphy, Rachel M.
AU - van de Garde, Ewoudt M. W.
AU - Karapinar-Carkit, Fatma
AU - Derijks, Hieronymus J.
AU - Hoge, Rien H. L.
AU - Klopotowska, Joanna E.
AU - van den Bemt, Patricia M. L. A.
PY - 2024/6/1
Y1 - 2024/6/1
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - clinical decision support systems
KW - medication safety
KW - medication alerts
KW - ADVERSE DRUG EVENTS
KW - HIGH-RISK
KW - SAFETY
KW - CARE
KW - MODEL
U2 - 10.1093/jamia/ocae076
DO - 10.1093/jamia/ocae076
M3 - (Systematic) Review article
SN - 1067-5027
VL - 31
SP - 1411
EP - 1422
JO - Journal of the American Medical Informatics Association
JF - Journal of the American Medical Informatics Association
IS - 6
ER -