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Mitigating Automation Bias in Physician-LLM Diagnostic Reasoning Using Behavioral Nudges
Sponsor: Lahore University of Management Sciences
Summary
The goal of this randomized controlled trial is to evaluate whether behavioral nudges can reduce automation bias, the uncritical acceptance of automated output, in physicians using large language models (LLM) like ChatGPT-5.1 for clinical decision-making. The main question it aims to answer is: Does a dual-mechanism behavioral nudge intervention (baseline accuracy anchoring plus case-specific color-coded confidence signals) reduce physicians' uncritical acceptance of incorrect LLM recommendations? Researchers will compare physicians who receive LLM recommendations along with a behavioral nudge to those who receive LLM recommendations without the nudge to assess if the nudge reduces automation bias. Participants will: * Evaluate six clinical vignettes accompanied by LLM-generated recommendations (half containing deliberate, clinically significant errors). * Control group: Be able to view LLM recommendations in standard format without the nudge. * Treatment group: Be able to view ChatGPT's diagnostic accuracy on standard medical datasets as an initial anchor, then receive color-coded confidence signals alongside each recommendation (e.g., red for low confidence). * Have their responses evaluated by blinded reviewers using an expert-developed assessment rubric to detect uncritical acceptance of erroneous information.
Key Details
Gender
All
Age Range
Any - Any
Study Type
INTERVENTIONAL
Enrollment
50
Start Date
2026-01-17
Completion Date
2026-08
Last Updated
2026-03-31
Healthy Volunteers
Yes
Conditions
Interventions
Behavioral Nudge Intervention
Participants in the treatment group will receive a behavioral nudge intervention embedded in the LLM recommendations interface that presents two synchronized cognitive cues when the LLM panel is expanded: (1) an anchoring cue displaying ChatGPT's baseline diagnostic accuracy on standard medical datasets at the top of the panel to set realistic expectations before viewing the specific recommendation, and (2) a selective attention cue located immediately below, which shows the LLM recommendation alongside a case-specific and color-coded confidence signal. This signal is categorized as red when the mean ensemble confidence falls below the established baseline accuracy, flagging high-uncertainty cases that demand critical evaluation; orange when confidence meets or exceeds the baseline but remains below 100%, intended to prevent complacency and maintain active clinical scrutiny; and green for a 100% ensemble consensus, though standard cautionary warnings still apply to guard against.
Locations (1)
Lahore University of Management Sciences
Lahore, Punjab Province, Pakistan