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Automation and Mental Health: Algorithms for Patient-Therapist Matching

Last registered on April 12, 2022


Trial Information

General Information

Automation and Mental Health: Algorithms for Patient-Therapist Matching
Initial registration date
April 08, 2022

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 12, 2022, 8:16 AM EDT

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



Primary Investigator

Max Planck Institute for Innovation and Competition

Other Primary Investigator(s)

PI Affiliation
University of Sussex, IIMA, Stanford University
PI Affiliation
University of Massachusetts Amherst, Max Planck Institute for Innovation and Competition
PI Affiliation
Max Planck Institute for Innovation and Competition
PI Affiliation

Additional Trial Information

Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
In this project, we study the role of a human as opposed to an algorithmic mediator in compliance decisions of the users. We work in partnership with one of the largest mental health apps in India. The app enables matching between users in need of mental health support and therapists who are able to provide it. When users register in the app they are offered to undergo a psychological assessment that would improve the suggested therapist match. In this experiment, we vary if the prompt to undergo an assessment comes from a human mediator or from the system. A priori it is not clear if making the involvement of algorithm salient would increase or decrease the take up of the assessment in the context of mental health. Given the shortage of psychologists and high demand for mental health support, technologies such as mental health mobile apps are a powerful force in delivering mental health support to those in need. The results of our study are therefore of high applied importance and through a better match with a therapist can improve the welfare of many.
External Link(s)

Registration Citation

Ghosh, Mainak et al. 2022. "Automation and Mental Health: Algorithms for Patient-Therapist Matching." AEA RCT Registry. April 12.
Experimental Details



Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
user’s decision to undergo the assessment after the prompt
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
attrition rates;
time needed to complete the assessment;
user's decision to open the report after completing the assessment
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The study is conducted in partnership with a large mental health app in India. From the start of the trial all the new users are randomized either into a Human or in an Algorithm treatment arms. The treatments differ subtly in wording used to offer the prompt to undergo the assessment. The treatment either displays the involvement of a human or an algorithm in offering the prompt. As a main outcome variable we consider if the user took the assessment. Taking the assessment was a desirable action that would increase the quality of the match but it did not exclude users from communicating with a mediator and ultimately receiving the match with the therapist.
Experimental Design Details
The primary outcome of interest is (1) a user’s decision to undergo the assessment after the prompt. Additionally, we will consider (2) the attrition rates measured as a difference between the number of people who started the assessment and those who completed it, (3) time needed to complete the assessment, (4) if users open the report after completing the assessment.
Due to the field setting of the experiment, we do not have comprehensive data on each user’s demographic or other potentially relevant characteristics and have only limited ability to elaborate on mechanisms. The user data is anonymized.
As users were randomized at the registration, main outcomes will be analysed using t-tests. In addition, the compliance decision and attrition will be analysed using probit regressions controlling for a proxy of a geographical location as supplied by the data provider, age and gender as estimated from the chat data in the app. Additionally, we will use machine learning techniques to analyze the chat data (where available and feasible). In the human condition, we will control for the gender of a human mediator as revealed by the displayed name and estimated via gender.api.
Randomization Method
By a system upon registration of a user
Randomization Unit
Individual user
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
at least 600 users
Sample size: planned number of observations
According to our power calculations, the sample size of 600 people (300 per treatment arm) will allow us to detect an effect size of d=0.3 with alpha of 0 .05 and power of 0.95. The final number of observations depends on the industry partner.
Sample size (or number of clusters) by treatment arms
at least 300 per treatment arm
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
Indian Institute of Management Ahmedabad
IRB Approval Date
IRB Approval Number
IIMA IRB 2021-26
IRB Name
IRB of Max Planck Society
IRB Approval Date
IRB Approval Number


Post Trial Information

Study Withdrawal

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Is the intervention completed?
Data Collection Complete
Data Publication

Data Publication

Is public data available?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials

Article reporting the results of the RCT
Chatterjee C, Chugunova M, Ghosh M, Singhal A and Wang LX (2023) Human mediation leads to higher compliance in digital mental health: field evidence from India. Front. Behav. Econ. 2:1232462. doi: 10.3389/frbhe.2023.1232462