Learnings from a Large-Scale Deployment of an LLM-Powered Expert-in-the-Loop Healthcare Chatbot

• Main Authors: Sachdeva, Bhuvan, Ramjee, Pragnya, Fulari, Geeta, Murali, Kaushik, Jain, Mohit
• Format: Journal Article
• Language: English
• Published: 16-09-2024
• Subjects: Computer Science - Human-Computer Interaction
• DOI: 10.48550/arxiv.2409.10354

Large Language Models (LLMs) are widely used in healthcare, but limitations
like hallucinations, incomplete information, and bias hinder their reliability.
To address these, researchers released the Build Your Own expert Bot (BYOeB)
platform, enabling developers to create LLM-powered chatbots with integrated
expert verification. CataractBot, its first implementation, provides
expert-verified responses to cataract surgery questions. A pilot evaluation
showed its potential; however the study had a small sample size and was
primarily qualitative. In this work, we conducted a large-scale 24-week
deployment of CataractBot involving 318 patients and attendants who sent 1,992
messages, with 91.71% of responses verified by seven experts. Analysis of
interaction logs revealed that medical questions significantly outnumbered
logistical ones, hallucinations were negligible, and experts rated 84.52% of
medical answers as accurate. As the knowledge base expanded with expert
corrections, system performance improved by 19.02%, reducing expert workload.
These insights guide the design of future LLM-powered chatbots.