Generative AI in Outpatient Healthcare

Last registered on May 21, 2025
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Pre-Trial

Trial Information

General Information

Title
Generative AI in Outpatient Healthcare
RCT ID
AEARCTR-0015851
Initial registration date
May 20, 2025

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
May 21, 2025, 4:06 PM 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
Xinyao Qiu
Affiliation
Stanford University
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Other Primary Investigator(s)

PI Name
Yuyu Chen
PI Affiliation
Peking University
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PI Name
Hongbin Li
PI Affiliation
Stanford University
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PI Name
Lingsheng Meng
PI Affiliation
Stanford University
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PI Name
Qingxu Yang
PI Affiliation
University of Hong Kong
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Additional Trial Information

Status
In development
Start date
2025-05-12
End date
2026-05-12
Keywords
Health
Additional Keywords
Artificial Intelligence
JEL code(s)
I10, O33, D83
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This study examines the impact of large language models (LLMs) on the quality of medical services through randomized controlled trials conducted in a hospital setting. We assess whether LLM assistance can enhance service quality and identify potential mechanisms underlying these improvements.
External Link(s)

Registration Citation

Citation
Chen, Yuyu et al. 2025. "Generative AI in Outpatient Healthcare." AEA RCT Registry. May 21. https://doi.org/10.1257/rct.15851-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2025-05-26
Intervention End Date
2025-06-08

Primary Outcomes

Primary Outcomes (end points)
Short-term subjective and objective measures of medical services.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Long-term measures of medical services
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Physicians and patients are randomly assigned to either the LLM-exposed or unexposed group, respectively.
Experimental Design Details
Not available
Randomization Method
randomization done in office by a computer
Randomization Unit
Physicians and patients are randomized at the individual level.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
210 physicians
Sample size: planned number of observations
A total of 210 physicians and an average of 1,200 patients per day
Sample size (or number of clusters) by treatment arms
Physicians: 70 physicians unexposed, 140 physicians exposed.
Patients (per day): 800 control group, 400 treatment group
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
PKU GSM-IRB
IRB Approval Date
2025-05-07
IRB Approval Number
PKU GSM-IRB #2025-14
Analysis Plan