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ASHABot: An LLM-Powered Chatbot to Support the Informational Needs of Community Health Workers

Pragnya Ramjee, Mehak Chhokar, Bhuvan Sachdeva, Mahendra Meena, Hamid Abdullah, Aditya Vashistha, Ruchit Nagar, Mohit Jain

TL;DR

ASHABot addresses information gaps for ASHAs and supervised by ANMs in India's frontline health system using a WhatsApp-based LLM-powered chatbot with expert-in-the-loop verification. The paper presents design, iterative pilot testing, and a field deployment in Rajasthan with 20 ASHAs and 15 ANMs, plus qualitative and log-based evaluation. Key findings show the bot offers private, trusted, and detailed information, acts as an authoritative resource, while supervisors experience workload concerns and accountability; ANMs contribute to knowledge base and there are concerns about scaling, governance, and value alignment. The work demonstrates the potential of LLM-enabled, complement-like tools in frontline health while highlighting the need for training, careful governance, and user-centered design to avoid overreliance or misalignment.

Abstract

Community health workers (CHWs) provide last-mile healthcare services but face challenges due to limited medical knowledge and training. This paper describes the design, deployment, and evaluation of ASHABot, an LLM-powered, experts-in-the-loop, WhatsApp-based chatbot to address the information needs of CHWs in India. Through interviews with CHWs and their supervisors and log analysis, we examine factors affecting their engagement with ASHABot, and ASHABot's role in addressing CHWs' informational needs. We found that ASHABot provided a private channel for CHWs to ask rudimentary and sensitive questions they hesitated to ask supervisors. CHWs trusted the information they received on ASHABot and treated it as an authoritative resource. CHWs' supervisors expanded their knowledge by contributing answers to questions ASHABot failed to answer, but were concerned about demands on their workload and increased accountability. We emphasize positioning LLMs as supplemental fallible resources within the community healthcare ecosystem, instead of as replacements for supervisor support.

ASHABot: An LLM-Powered Chatbot to Support the Informational Needs of Community Health Workers

TL;DR

ASHABot addresses information gaps for ASHAs and supervised by ANMs in India's frontline health system using a WhatsApp-based LLM-powered chatbot with expert-in-the-loop verification. The paper presents design, iterative pilot testing, and a field deployment in Rajasthan with 20 ASHAs and 15 ANMs, plus qualitative and log-based evaluation. Key findings show the bot offers private, trusted, and detailed information, acts as an authoritative resource, while supervisors experience workload concerns and accountability; ANMs contribute to knowledge base and there are concerns about scaling, governance, and value alignment. The work demonstrates the potential of LLM-enabled, complement-like tools in frontline health while highlighting the need for training, careful governance, and user-centered design to avoid overreliance or misalignment.

Abstract

Community health workers (CHWs) provide last-mile healthcare services but face challenges due to limited medical knowledge and training. This paper describes the design, deployment, and evaluation of ASHABot, an LLM-powered, experts-in-the-loop, WhatsApp-based chatbot to address the information needs of CHWs in India. Through interviews with CHWs and their supervisors and log analysis, we examine factors affecting their engagement with ASHABot, and ASHABot's role in addressing CHWs' informational needs. We found that ASHABot provided a private channel for CHWs to ask rudimentary and sensitive questions they hesitated to ask supervisors. CHWs trusted the information they received on ASHABot and treated it as an authoritative resource. CHWs' supervisors expanded their knowledge by contributing answers to questions ASHABot failed to answer, but were concerned about demands on their workload and increased accountability. We emphasize positioning LLMs as supplemental fallible resources within the community healthcare ecosystem, instead of as replacements for supervisor support.
Paper Structure (33 sections, 3 figures, 3 tables)

This paper contains 33 sections, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Technology to Support Community Health Workers
  4. AI and Community Health
  5. System Design
  6. Pilot Deployment and Response Evaluation
  7. Evaluation of ASHABot's Responses by Medical Professionals
  8. Field Deployment and Evaluation
  9. Field Deployment
  10. Semi-structured Interviews
  11. Data Analysis
  12. Ethics
  13. Positionality
  14. Findings
  15. Bot Usage by ASHAs and ANMs
  16. ...and 18 more sections

Figures (3)

  • Figure 1: Flow diagram of the ASHABot system.
  • Figure 2: When ASHABot cannot answer a question using its knowledge base, it sends that question to multiple ANMs. It identifies the relevant information from their responses and generates a consensus answer, which it sends back to the ASHA. While we present this example in English, we note that all ASHAs and ANMs interacted with ASHABot in Hindi.
  • Figure 3: ASHABot deployment statistics.