Objective To observe the effect of quality control circle (QCC) management tools to improve the electronic medical record timely writing rate. Methods Between June 2014 and January 2015, we used QCC to manage electronic medical record timely writing rate. By determining the subjects, investigation of the status quo, factor analysis, and and formulation and implementation of strategies, we tried to improve the electronic medical record timely writing rate. Results After QCC implementation, electronic medical records untimely rate dropped from 39.6% to 13.8%, with surgical departments dropping from 45.6% to 15.2% and non-surgical departments from 33.6% to 12.4%. Target compliance rate reached 124.04%, of which the untimely rate of nursing records and the overtime rate of rescue records were both reduced to 0. Quality management methods, team cohesion, confidence, personal comprehensive ability and problem-solving ability all improved significantly. Conclusions The timeliness of electronic medical records management has its importance and urgency. We should make good use QCC management to ensure timely electronic medical records writing.
Data integrity, accuracy, and traceability are key elements of high-quality clinical research, as well as weak links in the promotion of clinical research transparency. How to promote data quality has become a major concern to all clinical research stakeholders. In this article, we dissected and analyzed data generation and capturing process in clinical research, and identified a key aspect in improving data quality: to promote electronic source data, especially to break the barrier between electronic health records and clinical research systems. Additionally, we summarized the experiences regarding this issue in China and overseas to propose a solution suitable for China to improve data quality in clinical research: to strengthen clinical research source data management by building clinical research source data platform and adopt common source data management process in hospitals.
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".