Evaluating the Reasoning Capabilities of Large Language Models for Medical Coding and Hospital Readmission Risk Stratification: Zero-Shot Prompting Approach

Abstract

Current LLMs exhibit moderate success in zero-shot diagnosis and risk prediction but underperform in ICD-9 code generation, reinforcing findings from prior studies. Reasoning models offer marginally better performance and increased interpretability, with limited reliability. Overall, statistical analysis between the models revealed that OpenAI-O3 outperformed the other models. These results highlight the need for task-specific fine-tuning and need human-in-the-loop checking. Future work will explore fine-tuning, stability through repeated trials, and evaluation on a different subset of deidentified real-world data with a larger sample size.

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