The Un‘Healthy’ Gaps: Evidence on Gendered Faultlines in Digital Healthcare Services

Last registered on September 22, 2025
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Pre-Trial

Trial Information

General Information

Title
The Un‘Healthy’ Gaps: Evidence on Gendered Faultlines in Digital Healthcare Services
RCT ID
AEARCTR-0016830
Initial registration date
September 19, 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
September 22, 2025, 6:52 AM EDT

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

Locations

Country
India
Region

Primary Investigator

Name
Nikita Sangwan
Affiliation
Queens University Belfast
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Dweepobotee Brahma
PI Affiliation
Indian Institute of Technology Jodhpur
Contact Investigator

Additional Trial Information

Status
Completed
Start date
2024-11-08
End date
2024-11-13
Keywords
Gender, Health
Additional Keywords
gender bias, digital platforms, healthcare, high-skilled professionals
JEL code(s)
J16, J24, I11, O33, L86
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This paper examines gender disparities in engagement on a large-scale digital health platform in a developing country context.
External Link(s)

Registration Citation

Citation
Brahma, Dweepobotee and Nikita Sangwan. 2025. "The Un‘Healthy’ Gaps: Evidence on Gendered Faultlines in Digital Healthcare Services." AEA RCT Registry. September 22. https://doi.org/10.1257/rct.16830-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
This paper examines gender disparities in engagement on a large-scale digital health platform in a developing country context.
Intervention (Hidden)
To examine the role of taste-based bias in patient choices on digital health platforms, we conducted a discrete choice experiment (DCE) at the end of November 2024.
Intervention Start Date
2024-11-08
Intervention End Date
2024-11-13

Primary Outcomes

Primary Outcomes (end points)
We examine (1) physician participation on the platform at both the extensive margin (number of consultation slots offered per week) and the intensive margin (total duration in minutes offered per week); (2) consultation fees set by doctors, booking rates and the physician’s rank on the platform; (4) platform engagement and user feedback
through the indicator of the number of reviews left by patients and the recommendation
rate (i.e., share of patients who would recommend the physician). This final set of variables
captures patient satisfaction with the healthcare services delivered by doctors and the visibility
dynamics shaped by platform algorithms
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
This paper examines gender disparities in engagement on a large-scale digital health platform in a developing country context.
Experimental Design Details
To examine the role of taste-based bias in patient choices on digital health platforms, we conducted a discrete choice experiment (DCE) at the end of November 2024. The experiment was administered to approximately 270 undergraduate students at the Indian Institute of Technology (IIT) Jodhpur. Participants interacted with a custom web interface designed to mimic the user interface of a real digital health platform. Each respondent was shown a series of fictitious physician profiles, with attributes systematically varied across choice sets.
The attributes included physician gender (male or female), consultation fee (INR 600, 800, or 1000), years of experience (8, 15, 25, or 40 years), and patient rating (3, 4, or 5 stars). We created a full factorial design of all possible combinations and applied a D-efficient fractional
factorial design to generate 15 choice sets. In each choice task, participants were presented with six doctor cards with a pair of doctors - one male and one female, displayed at the same rank with similar attributes to isolate gender effects.
Randomization Method
Discrete Choice Experiment (DCE)
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
We don't cluster
Sample size: planned number of observations
270
Sample size (or number of clusters) by treatment arms
270 undergraduate students
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Indian Institute of Technology, Jodhpur
IRB Approval Date
2024-08-11
IRB Approval Number
IEC/IITJ/ 2024-25/10
Analysis Plan

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
Yes
Intervention Completion Date
November 13, 2024, 12:00 +00:00
Data Collection Complete
Yes
Data Collection Completion Date
November 13, 2024, 12:00 +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
270 individuals
Was attrition correlated with treatment status?
No
Final Sample Size: Total Number of Observations
270 individuals
Final Sample Size (or Number of Clusters) by Treatment Arms
270 individuals, all filled the survey
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
No
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials