Construction and Effect Evaluation on Quality Control Indicators of AI-Based Electronic Medical Record System
This paper aims to explore the construction of quality control indicators for artificial intelligence-based electronic medical record(EMR)quality control systems,specifically focusing on indicator screening and natural language structuring.Building upon the initial construction of quality control indicators based on policy documents and standards issued by competent authorities at all levels,these rough and diverse quality control indicators are processed to form more objective quality control indicators,including quantitative and qualitative dimensions index.On this basis,key information in EMRs,such as diagnoses and prescriptions,undergoes data cleansing,quantification,and natural language structuring,enabling automatic analysis,error detection,and quality assessment of EMR quality control indicators.Additionally,a Chinese EMR word segmentation engine is developed to enhance the segmentation accuracy of medical records.An artificial intelligence electronic medical record quality control system built based on this strategy can more accurately and effectively identify potential quality problems in electronic medical records and can play a significant assisting role in medical record quality control.This research provides a reference for the design of electronic medical record quality control products based on artificial intelligence.
Electronic medical recordsArtificial intelligenceQuality control strategy
NETL
NSTL
万方数据
