Large language models and conditional rules in clinical decision support systems.

ABSTRACT:

Background

Clinical Decision Support Systems (CDSS) improve patient outcomes and support sustainable health services by enhancing medical decisions. Developing rules for a CDSS is expensive due to delays in capturing and defining the rules through multiple iterations between clinicians and developers as the role of a clinician is patient care.

Objective

We investigate the effectiveness of large language models (LLMs) and large reasoning models (LRMs) in generating a triaging rule set for a CDSS.

Methods

We prompt various LLMs (GPT-3.5, GPT-4, GPT-4o, Gemini, Claude 3.5 Sonnet) and various LRMs (GPT-o1-mini, Grok-4, Claude 4 Sonnet) using alternative prompting techniques. We compare the LLM generated rule sets against the clinical rule set from our Pandemic Interventio

SUBMITTER: Sivasothy S 

PROVIDER: S-EPMC12824032 | biostudies-literature | 2026 Dec

REPOSITORIES: biostudies-literature