ASHABot: An LLM-Powered Chatbot to Support the Informational Needs of Community Health Workers
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Sep 2024.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2025: 2; 2026: 1.
3 papers.
- 2025Building Benchmarks from the Ground Up: Community-Centered Evaluation of LLMs in Healthcare Chatbot Settingscited by 5
- 2025Streaming, Fast and Slow: Cognitive Load-Aware Streaming for Efficient LLM Servingcited by 3
- 2026Enhancing Mental Health Counseling Support in Bangladesh using Culturally-Grounded Knowledgecited by 0
- 2026Enhancing Mental Health Counseling Support in Bangladesh using Culturally-Grounded Knowledge16 Apr 2026
- 2025Building Benchmarks from the Ground Up: Community-Centered Evaluation of LLMs in Healthcare Chatbot Settings29 Sep 2025
- 2025Streaming, Fast and Slow: Cognitive Load-Aware Streaming for Efficient LLM Serving25 Apr 2025