Physicians’ Responses to Incentives and Noise resulting from Artificial Intelligence: A medically framed real effort experiment

Last registered on December 01, 2023
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Pre-Trial

Trial Information

General Information

Title
Physicians’ Responses to Incentives and Noise resulting from Artificial Intelligence: A medically framed real effort experiment
RCT ID
AEARCTR-0012542
Initial registration date
November 20, 2023

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
December 01, 2023, 4:57 AM EST

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

Locations

Country
China
Region
Shandong

Primary Investigator

Name
Yue Zhong
Affiliation
Wuhan University
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Other Primary Investigator(s)

PI Name
JIAN WANG
PI Affiliation
Wuhan university
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PI Name
Joan Costa-Font
PI Affiliation
LSE
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PI Name
Jialong Tan
PI Affiliation
Wuhan University
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Additional Trial Information

Status
In development
Start date
2023-12-01
End date
2024-06-30
Keywords
Behavior, Welfare
Additional Keywords
financial incentives, non-financial incentives, noise of AI, physicians' behavior, altruism
JEL code(s)
D90 Micro-Based Behavioral Economics: General
Secondary IDs
Taikang Yicai Public Health and Epidemic Control Fund
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Background: Physicians’ behavior can be influenced a number of potential incentives as well as noise. The purpose of the research is to investigate the effects of both financial and non-financial incentives on physicians’ prosocial behavior with the advice of AI in health care settings.
Methods: This study protocol draws on a natural setting to carry out a real effort experiment design of data filling, simulating the process of physicians’ diagnosis and treatment with AI under different incentives. The main task of the subjects is to fill data of abnormal results from paper test report into an input system developed by the software O-Tree in different settings of incentives. We plan to test whether physicians respond differently to financial incentives of different payment systems compared to, or along with the non-financial incentives of quality ranking. Furthermore, our AI-based payment scheme design explores the effects of noise, namely an imperfect AI advise on physician decision making. The main outcome will be the performance of the subjects, including the quantity and quality of data filling, and the subject’s decision making will reflect their tradeoff between the extrinsic and intrinsic motivations.
External Link(s)

Registration Citation

Citation
Costa-Font, Joan et al. 2023. "Physicians’ Responses to Incentives and Noise resulting from Artificial Intelligence: A medically framed real effort experiment." AEA RCT Registry. December 01. https://doi.org/10.1257/rct.12542-1.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
financial incentives
non-financial incentives
noise of AI
Intervention Start Date
2023-12-15
Intervention End Date
2024-03-31

Primary Outcomes

Primary Outcomes (end points)
the quality and quantity of subjects' data filling
Primary Outcomes (explanation)
the performance of physicians' behavior

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We will use the experiment to investigate the physicians’ responses to different incentives and diagnostic AI advice. The main task of the subjects is to fill data of abnormal results from paper test reports into an input system developed by the software O-Tree in different settings, simulating the process of diagnosis and treatment in primary medical care. Subjects’ performance was measured by the quantity and quality of data inputs, representing the diagnostic behavior of primary physicians.
Experimental Design Details
In this experiment, the patients are assumed, and the paper report will mirror the health state of patients, in which the abnormal results represent the diseases. We planned for this task to parallel medical decision-making by primary care physicians, with the data filling representing the process of diagnosis and treatment with the patient’s case during which the physician performs a number of tasks in medical care. In addition, subjects’ performance in the task will reflect physicians’ behavior under different payment systems. Physicians will gain benefits under different payment systems, either by quantity or by quality. If the abnormal results in the paper report are accurately filled in the input system, that is, the quality of the data filling represents the benefit of the patients. If the abnormal data were missed in subjects’ data filling, this would represent under-treatment in primary medical care. If normal results were filled repeatedly by subjects, this indicates that over-treatment maybe happen for patients.
Randomization Method
by computer
Randomization Unit
clustered by the level of health centers the subjects work in
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
about 30 health centers
Sample size: planned number of observations
about 500 physicians
Sample size (or number of clusters) by treatment arms
4 treatments and 16 groups
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
This calculation takes into account an effect size (f) of 0.25, aiming for 80% statistical power, and a significance level of 0.05.
Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name
The Ethics Committee of the Humanities and Social Sciences of Wuhan University
IRB Approval Date
2023-08-30
IRB Approval Number
WHU-HSS-IRB2023015
Analysis Plan

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials