Quantitative analysis of medical quality intelligent management policies in China: a PMC index model approach.

Objective: This research aims to address the issues existing in the current assessment of national Medical Quality Intelligent Management policies (MQIMPs). By constructing a scientific, quantitative assessment system, it precisely identifies the strengths and weaknesses of existing policies across various aspects, providing clear direction for policy improvement, and promoting more efficient guidance of practice through intelligent management policies for medical quality. Methods: This study integrates text mining and content analysis techniques to examine the relationship between them. We construct a PMC index model. Then used the PMC index model to conduct a comprehensive assessment of the strengths and limitations of the current MQIMPs. Results: The evaluation indicates that China's current MQIMPs system is relatively well-established, with an overall excellent performance rating. However, notable deficiencies were identified across three key dimensions: Medical Quality Control, Data Support, and Policy Audience. The relatively low scores in these areas clearly demonstrate substantial room for improvement. Conclusion: Based on the comprehensive evaluation of MQIMPs, three key recommendations are proposed. First, from the Medical Quality Control Dimension, consider adding new policies and subdividing governance areas. Second, from the Data Support perspective, establish a data lifecycle governance framework to clarify the policy core content. Third, refine audience segmentation criteria from the Policy Audience dimension. These steps will effectively develop the MQIMPs, enhancing their ability to guide practice and drive national medical quality improvement.