ObjectiveTo construct a demand model for electronic medical record (EMR) data quality in regards to the lifecycle in machine learning (ML)-based disease risk prediction, to guide the implementation of EMR data quality assessment. MethodsReferring to the lifecycle in ML-based predictive model, we explored the demand for EMR data quality. First, we summarized the key data activities involved in each task on predicting disease risk with ML through a literature review. Second, we mapped the data activities in each task to the associated requirements. Finally, we clustered those requirements into four dimensions. ResultsWe constructed a three-layer structured ring to represent the demand model for EMR data quality in ML-based disease risk prediction research. The inner layer shows the seven main tasks in ML-based predictive models: data collection, data preprocessing, feature representation, feature selection and extraction, model training, model evaluation and optimization, and model deployment. The middle layer is the key data activities in each task; and the outer layer represents four dimensions of data quality requirements: operability, completeness, accuracy, and timeliness. ConclusionThe proposed model can guide real-world EMR data governance, improve its quality management, and promote the generation of real-world evidence.
The application of inpatient electronic medical records (EMRs) is a crucial component of modern healthcare informatization, and also a key factor in improving medical quality and safety. Establishing standardized EMRs for thoracic surgery helps to standardize treatment processes, improve medical efficiency, enhance quality of care, and better ensure patient safety. It also facilitates the collection and use of standardized and structured data, promoting clinical decision-making, the application of artificial intelligence, and the development of specialized clinical centers. Considering relevant national policies, information standards, clinical practice challenges and latest research findings in thoracic surgery EMRs, Chinese Association of Thoracic Surgeons, Cross-Strait Medicine Exchange Association’s Thoracic Surgery Professional Committee, WU Jieping Medical Foundation’s Lung Cancer Professional Committee, Zhejiang Provincial Thoracic Surgeons Associations and Fujian Provincial Thoracic Surgeons Associations have explored innovative paths for EMRs development. Through multiple rounds of professional discussions and research, the "Chinese expert consensus on quality control and management of electronic medical records for thoracic surgery (2024 version)" was formulated. It aims to provide a reference for the construction and application of inpatient EMRs for thoracic surgeons and information professionals across China, promoting continuous improvement in the informatization and medical standards of the thoracic surgery field, and contributing to the construction of "healthy China".