Remote Care for Remote Areas: The Impact of Telehealth in Rural India

Last registered on January 23, 2023


Trial Information

General Information

Remote Care for Remote Areas: The Impact of Telehealth in Rural India
Initial registration date
January 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
January 23, 2023, 7:22 AM EST

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


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Primary Investigator

London School of Economics

Other Primary Investigator(s)

PI Affiliation
Northwestern University
PI Affiliation
University of Milan-Bicocca

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Remote areas in low-income countries have poor access to quality healthcare. One challenge in developing state capacity in remote areas is the difficulty in attracting skilled workers (doctors and nurses), to which a common solution is to engage less skilled workers (community health workers). A new solution is to bring higher-skilled professionals to rural areas through digital technology. Telehealth, which connects patients to qualified healthcare professionals via phone, provides a new opportunity for governments to reach remote areas with high-quality healthcare services at relatively low costs. Although the popularity of telehealth has dramatically increased since the onset of the COVID-19 pandemic, there is to date no causal evidence of its impacts in low-income countries.

This project aims to provide the first experimental evidence on the impact of telehealth on healthcare utilization and health outcomes in low-income countries. The impact is ex-ante ambiguous: telehealth may expand access to healthcare in areas previously underserved by the health system, but it might also crowd out in-person care and lead to an overall drop in healthcare utilization by those most in need, who might be unable or unwilling to connect remotely with a health professional. The project will take place in 400 rural Indian villages that will be randomized into receiving telehealth or not, with or without a local facilitator, who will assist patients in connecting to the call and follow up with them after the visit. We will learn whether and under which conditions telehealth improves access and health outcomes for rural populations, and how it affects the divide in access by gender, income, and age.
External Link(s)

Registration Citation

Dahlstrand-Rudin, Amanda, Erika Deserranno and Andrea Guariso. 2023. "Remote Care for Remote Areas: The Impact of Telehealth in Rural India." AEA RCT Registry. January 23.
Sponsors & Partners

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Experimental Details


Telehealth connects patients to qualified health care professionals via phone. Although its popularity dramatically increased since the onset of the COVID-19 pandemic, there is limited rigorous evidence of its impact. In particular, the impact of telehealth on health access and healthcare inequality in low-income countries is ex-ante ambiguous. On the one hand, telehealth could allow individuals currently out of reach of the official health system -- e.g., because of their remote location or because of prevailing norms -- to access quality healthcare providers, thus improving the quality of care and equalizing access. On the other hand, telehealth might crowd out in-person care, with potential negative consequences on health outcomes for those individuals who do not engage with technology, who are not e-literate, and who have little trust in modern medicine to start with (e.g., women, the elderly, the poor). This may result in telehealth excluding individuals who might need care most, exacerbating inequality in healthcare.
Given the potential barriers to the utilization of telehealth in low-income settings, the presence of local “facilitators” may be crucial to its success. First, local facilitators may attenuate the digital divide by allowing marginalized individuals, including women with low power in the household, to contact the doctor and nurse through a smartphone that has access to the internet. Second, local facilitators may allow for more “continuity of care”, an aspect which is often lacking in telehealth programs that prioritize the “speed of care” (i.e., patients meeting with the first available provider even if that provider is unknown to them) and which might matter especially in contexts with low trust in modern medicine. Finally, local facilitators may complement telehealth services by monitoring the evolution of health conditions of patients that require frequent follow-ups (e.g., noncommunicable diseases).
India provides an ideal study setting, both because of the vast potential user base in need of telehealth services and because recent years have seen a rapid diffusion of telehealth providers. The Indian government has pioneered this technological revolution, by launching and promoting its platform, called eSanjeevani, and has been rapidly followed by other telehealth providers, in an attempt to reach even the most remote areas of the country. However, while millions of people have already used these services, their reach seems so far to be below expectations.
The project will develop in two phases. In the first phase, we will study the current diffusion of telehealth services in rural Bihar, with a particular focus on the government program eSanjeevani. Through a rich data collection that will span 400 villages, our objectives are to study:
• How popular are telehealth services in rural Bihar? In particular, how familiar are people with the government eSanjeevani program? What are the main challenges to telehealth diffusion?
• Which types of patients are more likely to use telehealth? In particular, does telehealth reduce gender inequality in access to healthcare?

In the second phase, we will then study the impact of the arrival of a new telehealth provider in the study location. The new health provider (Healing Fields Foundation, HFF) will introduce two alternative versions of telehealth: with and without a facilitator, i.e. a community health worker that will help community members connect through telehealth. In this second phase we will aim to study:
• What is the causal impact of telehealth on access to health services and health outcomes among people in rural areas?
• Does the presence of a facilitator increase the spreading and utilization of telehealth?
• Does telehealth reduce gender inequality in access to healthcare typically observed in many low-income countries?
• Which types of patients are more likely to use telehealth with vs. without the facilitator? Does the impact differ across gender, income, and age groups?
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Health service utilization, measured through a household survey and administrative data. This will encompass both telehealth utilization and in-person healthcare utilization. We will look at overall health service utilization as well as gaps in utilization by wealth, gender, and age.
We will also study whether telehealth crowds in or out in-person health services, and other telehealth programs (e.g. eSanjeevani), by looking at households’ interactions with each one of these service providers, as recorded in the household survey.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Health outcomes (household survey). Trust and satisfaction with the official health system (household survey).
Changes in effort, motivation, earnings and activities of the health workers (health providers survey).
Changes in the types of patients different provides cater to and the health services provided across the different treatment arms (household and health provider survey). Effects on the government telehealth program from introducing a NGO program.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The study will take place in 400 villages in the state of Bihar, India. The first part of the study will be based on a rich set of surveys administered to a representative set of households and to the universe of health providers located in these 400 study villages, as well as detailed administrative data from the existing telehealth provider eSanjeevani.
The second part of the study will be based on a 2x2 field experiment, which will be implemented after the initial data collection in collaboration with Healing Fields Foundation (HFF), an NGO which recently launched a telehealth program that relies on community health workers (CHWs) as “facilitators”. We will take advantage of the planned expansion of HFF activities in the study region, to randomly select the villages that will be reached first by the program. Each study village will be randomly assigned to one of four groups of equal size:
• Control (C): status quo (no HFF telehealth and no HFF CHW);
• Telehealth + CHW (T1): HFF will provide telehealth and will recruit and train a CHW, who will be in charge of facilitating it;
• Telehealth only (T2): HFF will provide telehealth services that community members can directly utilize, but won’t recruit any CHW;
• CHW only (T3): HFF will recruit and train a CHW, but there will be no HFF telehealth services.
Experimental Design Details
Not available
Randomization Method
Randomization will be done in office by a computer.
Randomization Unit
Randomization will be done at the village level.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
400 villages.
Sample size: planned number of observations
18 households per village (~7,200 households in total) are the units of observation for the household outcomes, and 5 health providers per village (~2,000 in total) for the provider outcomes. These are spread across the clusters of 400 villages.
Sample size (or number of clusters) by treatment arms
100 villages in control group (~1800 households, ~500 health providers)
100 villages in T1 (~1800 households, ~500 health providers)
100 villages in T2 (~1800 households, ~500 health providers)
100 villages in T3 (~1800 households, ~500 health providers)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
The study is designed to detect at the 5% significance level with 80% power, an effect on health service utilization of each intervention equal to 0.135 standard deviations or larger.

Institutional Review Boards (IRBs)

IRB Name
IFMR Human Subjects Committee
IRB Approval Date
IRB Approval Number
IRB Name
London School of Economics Research Ethics
IRB Approval Date
IRB Approval Number