Effect of Uncertainty-Aware AI Models on Pharmacists’ Reaction Time and Decision-Making in a Web-Based Mock Medication Verification Task: Randomized Controlled Trial
Pharmacists' performance and reaction times varied by AI type and AI accuracy. Overall, uncertainty-aware AI resulted in faster decision-making and acted as a safeguard against bad AI advice to approve a misfilled medication. Conversely, black-box AI had the longest reaction times, and user performance degraded in the presence of bad AI advice. However, uncertainty-aware AI could result in unnecessary double-checks, but it is preferred over false negative advice, where patients receive the wrong medication. These results highlight the importance of well-designed AI that addresses users' needs, enhances performance, and avoids overreliance on AI.
Authors
- Corey A. Lester (ORCID: https://orcid.org/0000-0001-8774-793X)
- Qiyuan Chen (ORCID: https://orcid.org/0009-0006-4112-014X)
- Yifan Zheng (ORCID: https://orcid.org/0000-0002-6997-735X)
- Vincent D. Marshall (ORCID: https://orcid.org/0000-0002-2594-7407)
- Brigid Rowell (ORCID: https://orcid.org/0000-0002-8469-899X)
- X. Jessie Yang (ORCID: https://orcid.org/0000-0001-6071-0387)
- Raed Al Kontar (ORCID: https://orcid.org/0000-0002-4546-324X)
- Zoe Co (ORCID: https://orcid.org/0000-0003-3656-0459)
- Jin Yong Kim (ORCID: https://orcid.org/0009-0002-1626-7204)
Institutions
- University of Michigan (US)
- Michigan Medicine (US)
Publication Details
- Journal
- JMIR Medical Informatics
- Published
- 2025-03-08
- DOI
- https://doi.org/10.2196/64902
- Citations
- 3
- Primary Topic
- Patient Safety and Medication Errors
- Type
- article
- Field-Weighted Citation Impact
- 7.55