Core Viewpoint - The recent cases of abnormal data in the healthcare system highlight the critical importance of data quality in ensuring the effective operation of medical insurance funds, which are essential for public health [1][5]. Group 1: Data Quality Issues - Numerous bizarre medical practices, such as a 73-year-old woman undergoing "painless egg retrieval" and male patients having hysteroscopy billing records, were identified as results of poor data quality management [1][5]. - The National Healthcare Security Administration (NHSA) has utilized a unified medical insurance information platform to identify and address these abnormal data cases, showcasing the empowering role of data in regulatory oversight [1][5]. Group 2: Implications of Data Errors - Errors in medical insurance data can trigger regulatory alarms and lead to on-site inspections, resulting in increased management costs for both regulatory bodies and medical institutions [5][7]. - The integrity of medical insurance funds, which are considered "lifesaving money" for the public, relies heavily on the quality of data management by designated medical institutions [5][7]. Group 3: Responsibilities and Recommendations - Designated medical institutions are urged to take primary responsibility for data quality, ensuring accurate reporting and ongoing verification to eliminate data risks at the source [7]. - Healthcare departments are encouraged to enhance their technical capabilities, implement intelligent monitoring and cross-verification mechanisms, and increase penalties for violations to ensure the proper functioning of medical insurance funds [7].

Core Insights - The article highlights the increasing role of data analytics in the supervision of medical insurance funds, emphasizing the ability to detect and rectify irregularities in healthcare data [1][11] Group 1: Data Monitoring and Irregularities - A specific case involved a doctor prescribing a substance at a rate over 200 times the national average, leading to an investigation that uncovered data quality issues due to improper data entry [2] - Instances of elderly patients undergoing inappropriate medical procedures were identified, prompting immediate verification by the insurance department [3][5] - A doctor was found to be issuing multiple prescriptions in a short time frame, raising suspicions of fraudulent practices, which were confirmed through further investigation [6] Group 2: Data Quality Management - The article stresses the importance of data quality management in healthcare institutions, noting that even minor discrepancies can trigger significant regulatory responses and resource wastage [11] - It calls for healthcare providers to enhance their data verification processes before submission to ensure accuracy and compliance with regulations [11] - Collaboration between healthcare institutions and insurance departments is essential to maintain the integrity of medical data and safeguard public funds [11]