Advancing Healthcare Automation: Multi-Agent System for Medical Necessity Justification

• URL: http://arxiv.org/abs/2404.17977v2
• Date: Sat, 6 Jul 2024 09:29:16 GMT
• Title: Advancing Healthcare Automation: Multi-Agent System for Medical Necessity Justification
• Authors: Himanshu Pandey, Akhil Amod, Shivang,
• Abstract summary: This paper explores the application of Multi-Agent System (MAS) that utilize specialized LLM agents to automate Prior Authorization task. We demonstrate that GPT-4 checklist achieves an accuracy of 86.2% in predicting item-level judgments with evidence, and 95.6% in determining overall checklist judgment.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Prior Authorization delivers safe, appropriate, and cost-effective care that is medically justified with evidence-based guidelines. However, the process often requires labor-intensive manual comparisons between patient medical records and clinical guidelines, that is both repetitive and time-consuming. Recent developments in Large Language Models (LLMs) have shown potential in addressing complex medical NLP tasks with minimal supervision. This paper explores the application of Multi-Agent System (MAS) that utilize specialized LLM agents to automate Prior Authorization task by breaking them down into simpler and manageable sub-tasks. Our study systematically investigates the effects of various prompting strategies on these agents and benchmarks the performance of different LLMs. We demonstrate that GPT-4 achieves an accuracy of 86.2% in predicting checklist item-level judgments with evidence, and 95.6% in determining overall checklist judgment. Additionally, we explore how these agents can contribute to explainability of steps taken in the process, thereby enhancing trust and transparency in the system.

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