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-Çarkit, Fatma
AU - Derijks, Hieronymus J
AU - Hoge, Rien H L
AU - Klopotowska, Joanna E
AU - van den Bemt, Patricia M L A
N1 - Publisher Copyright:
© 2024 The Author(s). Published by Oxford University Press on behalf of the American Medical Informatics Association.
PY - 2024/5/20
Y1 - 2024/5/20
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 - Decision Support Systems, Clinical
KW - Humans
KW - Medical Order Entry Systems
KW - Medication Errors/prevention & control
U2 - 10.1093/jamia/ocae076
DO - 10.1093/jamia/ocae076
M3 - Article
C2 - 38641410
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 -