BACKGROUND
Clinical decision support systems (CDSS) leveraging artificial intelligence (AI) are increasingly integrated into healthcare practices, including pharmacy medication verification. Communicating uncertainty in an AI prediction is viewed as an important mechanism for boosting human collaboration and trust. Yet, little is known about the effects on human cognition as a result of interacting with such types of AI advice.
OBJECTIVE
To evaluate the cognitive interaction patterns of pharmacists during medication product verification when using an AI prototype. Moreover, we examine the impact of AI’s assistance — both helpful and unhelpful — and the communication of uncertainty of AI-generated results on pharmacists’ cognitive interaction with the prototype and their performance.
METHODS
In a randomized controlled trial, 30 pharmacists from professional networks each performed 200 medication verification tasks while their eye movements were recorded using a virtual eye tracker. Participants completed 100 verifications without AI assistance and 100 with AI assistance (either with simple help without uncertainty information, or advanced help which displays AI uncertainty). Fixation patterns (first and last areas fixated, number of fixations, fixation duration, and dwell times) were analyzed in relation to AI help type and helpfulness.
RESULTS
Pharmacists shifted 19-26% of their total fixations to AI-generated regions when these were available, suggesting integration of AI advice in decision-making. AI assistance did not reduce the number of fixations on fill images, which remained the primary focus area. Unhelpful AI advice led to longer dwell times on reference and fill images, indicating increased cognitive processing. Displaying AI uncertainty led to longer cognitive processing times as measured by dwell times in original images.
CONCLUSIONS
Unhelpful AI increases cognitive processing time in the original images. Transparency in AI is needed in “black-box” systems, but showing more information can add cognitive burden. Therefore, the communication of uncertainty should be optimized and integrated into clinical workflows using user-centered design to avoid increasing cognitive load or impeding clinicians’ original workflow.