CMQCIC-Bench: A Chinese Benchmark for Evaluating Large Language Models in Medical Quality Control Indicator Calculation

TL;DR

This paper introduces CMQCIC-Bench, a Chinese medical dataset for evaluating large language models in medical quality control, proposing new methods that outperform existing approaches in clinical indicator calculation tasks.

Contribution

It presents a new Chinese EMR-based dataset and a novel CF-IR method for medical indicator calculation, advancing LLM applications in healthcare quality assessment.

Findings

CF-IR outperforms Chain-of-Thought methods in MQCIC tasks

The dataset contains 785 instances and 76 indicators

Comprehensive experiments on 20 LLMs demonstrate effectiveness

Abstract

Medical quality control indicators are essential to assess the qualifications of healthcare institutions for medical services. With the impressive performance of large language models (LLMs) like GPT-4 in the medical field, leveraging these technologies for the Medical Quality Control Indicator Calculation (MQCIC) presents a promising approach. In this work, (1) we introduce a real-world task MQCIC and propose an open-source Chinese electronic medical records (EMRs)-based dataset (CMQCIC-Bench) comprising 785 instances and 76 indicators. (2) We propose a semi-automatic method to enhance the rule representation. Then we propose the Clinical Facts-based Inferential Rule (CF-IR) method that disentangles the clinical fact verification and inferential rule reasoning actions. (3) We conduct comprehensive experiments on 20 representative LLMs, covering general and medical models. Our findings reveal that CF-IR outperforms Chain-of-Thought methods in MQCIC tasks. (4) We conduct an error analysis and investigate the capabilities of clinical fact verification and inferential rule reasoning, providing insights to improve performance in the MQCIC further. The dataset and code is available in this repository https://github.com/YuY-2001/C-MQCIC.