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Healthcare Provider vs. AI Communication and Chronic Disease Behavior in Rural India

Last registered on January 09, 2026

Pre-Trial

Trial Information

General Information

Title
Healthcare Provider vs. AI Communication and Chronic Disease Behavior in Rural India
RCT ID
AEARCTR-0017602
Initial registration date
January 09, 2026

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
January 09, 2026, 9:30 AM EST

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

Last updated
January 09, 2026, 9:40 PM EST

Last updated is the most recent time when changes to the trial's registration were published.

Locations

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Primary Investigator

Affiliation
Harvard University

Other Primary Investigator(s)

PI Affiliation
Harvard University

Additional Trial Information

Status
In development
Start date
2026-01-10
End date
2026-02-28
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This project investigates whether and how the quality of healthcare interactions affects patient health behaviors and outcomes through a randomized controlled trial with healthcare providers, an AI chatbot, and patients in rural West Bengal, India. We will randomly assign providers to receive communication skills training and cross-randomize patient access to an AI chatbot for blood pressure management advice. This design allows us to examine: i) whether the nature of patient-provider interactions affects patient behavior, ii) how AI substitutes or complements provider communication; and iii) what specific elements of communication lead patients to update their beliefs. We study these questions in the context of hypertension management in India, where 28% of adults have hypertension, yet only 12% achieve controlled blood pressure levels, and there is evidence of substantial gaps in both patient understanding of hypertension and medication adherence behaviors and minimal provider communication efforts.
External Link(s)

Registration Citation

Citation
Jha, Madhavi and Lauren Rice. 2026. "Healthcare Provider vs. AI Communication and Chronic Disease Behavior in Rural India." AEA RCT Registry. January 09. https://doi.org/10.1257/rct.17602-1.1
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
- Soft skill/communication training for healthcare providers
- AI voice-to-voice chatbot for hypertension management advice
Intervention Start Date
2026-01-11
Intervention End Date
2026-02-28

Primary Outcomes

Primary Outcomes (end points)
- Healthcare provider behavior and soft skills
- Patient-provider and patient-chatbot interaction quality (analyzed through video recordings or transcripts of interactions)
- Provider and patient hypertension knowledge and beliefs
- Patient hypertension management behavior and status
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
- Markers of nature of communication (e.g. information disclosure, questions asked)
- Patient beliefs about providers and provider choice
- Provider beliefs about patients
- Patient and provider self-beliefs
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Healthcare providers from our partner's network will be randomized into control (hard skill hypertension training only) or treatment (hard skill + soft skill training). Providers will be randomized within strata by an index of hard and soft skills and geographic block. Providers will then host hypertension clinics, where high-BP patients will be randomized into three chatbot exposure arms: control (no chatbot), pre-provider interaction chatbot exposure, and post-interaction chatbot exposure.

We analyze heterogeneity by:
- Patient beliefs about provider and provider beliefs about patient
- Baseline provider medical and communication skills (by which we stratify)
- Patient characteristics: demographics, hypertension understanding and behavior
Experimental Design Details
Not available
Randomization Method
Provider randomization done by computer, and patient randomization done by SurveyCTO
Randomization Unit
Provider randomization is at the individual level, and patient treatment is at the individual level within provider group.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
200 providers
Sample size: planned number of observations
200 providers, 2400 patients
Sample size (or number of clusters) by treatment arms
100 providers in control (hard skill training only)
100 providers in soft skill/communication training
Within provider, patients randomized into chatbot arms:
800 in control (provider interaction only)
800 in pre-interaction chatbot exposure arm
800 in post-interaction chatbot exposure arm
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
Harvard University-Area Committee on the Use of Human Subjects
IRB Approval Date
2025-10-08
IRB Approval Number
IRB24-1578