Overcoming Medical Overuse with AI Assistance: An Experimental Investigation

Last registered on April 25, 2024
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Pre-Trial

Trial Information

General Information

Title
Overcoming Medical Overuse with AI Assistance: An Experimental Investigation
RCT ID
AEARCTR-0013376
Initial registration date
April 17, 2024

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
April 25, 2024, 11:45 AM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

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Primary Investigator

Name
Ziyi Wang
Affiliation
Wuhan University
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Other Primary Investigator(s)

PI Name
Lian Xue
PI Affiliation
Wuhan University
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PI Name
Lijia Wei
PI Affiliation
Wuhan University
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Additional Trial Information

Status
On going
Start date
2024-04-16
End date
2024-07-01
Keywords
Behavior, Health, Welfare
Additional Keywords
medical overuse; overtreatment; artificial intelligence (AI); lab-in-the-field experiment
JEL code(s)
Secondary IDs
Prior work
This trial is based on or builds upon one or more prior RCTs.
Abstract
This study explores the potential of Artificial Intelligence (AI) to mitigate medical overuse by examining the decision-making processes of physicians in a controlled experimental setting. Medical overuse, characterized by unnecessary interventions that may harm patients and inflate healthcare costs, is a significant issue in both developed and developing healthcare systems. By integrating AI tools that offer evidence-based recommendations, this research aims to understand the extent to which AI can influence physician behavior and reduce overtreatment.

Our experimental design involves a lab-in-the-field setting within a medical school, where we manipulate both monetary incentives and the availability of AI assistance across different groups of medical students. We employ a two-by-two factorial design to assess how these variables impact the rates of overtreatment and the accuracy of clinical decisions. The study’s primary outcomes include the frequency of overtreatment, diagnosis accuracy rates, and the adoption rate of AI advice.

The findings are expected to contribute to the broader discourse on how AI can be lever-aged to enhance clinical decision-making and reduce inefficiencies in medical practices. By providing empirical evidence on the effects of AI interventions in reducing overtreatment, this study could inform policymakers and healthcare administrators about the merits of incorporating AI technologies in healthcare settings, potentially leading to improved healthcare efficiency and patient safety.
External Link(s)

Registration Citation

Citation
Wang, Ziyi, Lijia Wei and Lian Xue. 2024. "Overcoming Medical Overuse with AI Assistance: An Experimental Investigation." AEA RCT Registry. April 25. https://doi.org/10.1257/rct.13376-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2024-04-17
Intervention End Date
2024-06-30

Primary Outcomes

Primary Outcomes (end points)
The key outcome variables in this experiment are the degree of physician overtreatment, and AI on accuracy rate.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We conducted a lab-in-the-field experiment at a medical school affiliated with Central South Hospital, utilizing a two-by-three factorial design. The participants, referred to as prospective physicians, were recruited from this institution, where they were trained to become medical doctors.

Incentive Schemes (Between-Subject): The experiment varied the incentive structures for participants when making choices in a multiple-choice medicine prescription task. Three types of incentives were tested: flat, progressive, and regressive, affecting how participants were compensated for their choices.

AI Assistance (Within-Subject): We also varied the decision-making process by introducing an AI assistance feature. Participants made initial choices independently and could then opt to view AI-generated recommendations before finalizing their decisions.

Control Variables: To account for individual fixed effects and other potential confounders affecting the propensity for overtreatment, we implemented pre- and post-experiment assessments. These assessments collected data on doctors’ professional abilities, cognitive reflection (CRT), IQ, algorithm literacy, trust in algorithms, awareness of algorithms, and perceptions of algorithm fairness.

Experimental Design Details
Not available
Randomization Method
randomization in recruitment
Randomization Unit
Individual.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
We aim to recruit between 120 and 160 participants.
Sample size: planned number of observations
We aim to recruit between 120 and 160 participants.
Sample size (or number of clusters) by treatment arms
40-60 individuals in the Group 1，Group 2 and Group 3, respectively.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Economics and Management School of Wuhan University
IRB Approval Date
2024-04-10
IRB Approval Number
EM240014
Analysis Plan