Introduction
In US healthcare, management of administrative workflows represents a pivotal yet formidable challenge. Physicians, nurses, and administrative personnel frequently allocate a substantial portion of their working hours to these procedural tasks, detracting from their primary focus on patient care. Establishing or verifying medical necessity for Prior Authorization is a process that forms a part of several administrative tasks of US Healthcare, necessitates considerable time and resource investment, impacting the overall efficiency of healthcare delivery.

Prior authorization (PA) is a healthcare management process used by insurance entities to determine whether a proposed treatment or service, including but not limited to medications, imaging, and procedures, is covered under a patient’s plan before it is approved to be carried out. Madhusoodanan et al. ( 2023 ) . Evaluating a PA application involves assessing medical necessity of patient-specific health records against prevailing coverage guidelines.
A major part of these coverage guidelines are clinical guidelines which are systematically developed statements designed to help practitioners make decisions about appropriate health care for specific clinical circumstances. Insurance companies review these clinical guidelines to to justify medical necessity of a procedure Chambers et al. ( 2016 ) . A majority of PAs filed today are created manually by healthcare professionals extensively collecting evidences that can justify these medical necessity guidelines. In this paper we explore the application of multi-agent systems in automating the process of determining medical necessity by comparing clinical guidelines with the Electronic Health Records (EHR) of a patient.

The introduction of Large Language Models (LLMs) has catalyzed a transformative shift in the capabilities of artificial intelligence, enabling the resolution of complex challenges previously inaccessible to conventional AI methods. LLMs excel in interpreting and synthesizing large volumes of unstructured data, enhancing tasks such as natural language understanding, sentiment analysis, and automated content creation. Building on this foundation, Swarm Intelligence architecture , which employs a collective of AI-powered agents, represents an even further advancement Li and Clerc ( 2019 ) . This approach decomposes a singular complex task into multiple, manageable sub-tasks and distributes them across multiple agents, each specialized through training for a sub-task. Following this methodology essentially infuses a microservice architecture into the traditional monolithic AI framework, enabling more modular, scalable, and robust AI systems. By integrating the depth and adaptability of LLMs with the collaborative and dynamic nature of Swarm Intelligence architecture, AI systems can achieve unprecedented levels of performance and versatility across various complex problems.

In this paper, we investigate the application of multi-agent systems for determining medical necessity. We break down the entire process into several sub-tasks, each handled by a different Large Language Model (LLM) agent. We explore a variety of prompting strategies for each agent and examine the effects of the specific LLM used on these agents, particularly focusing on the trade-off between accuracy and latency.