Objective: The paper analyzed, summarized the characteristics of medical insurance data quality by taking the statistical process of “drug costs included in the payment scope” of negotiated drugs as an example, providing references for medical insurance data cleaning, application. Methods: Through the medical insurance settlement system of a city, this study collected medical insurance settlement records of national negotiated products within the agreement periodfrom January2018to September2024as the target dataset, supplemented with several auxiliary datasets by Internet search. A data quality assessment framework covering completeness, standardization, consistency was constructed. Results: In the completeness dimension, the main data problems were missing disease diagnosis codes, names, involving29.6%, 29.7% of record items. In the standardization dimension, 66.2% of record items had non-standard one-to-many correspondence between drug codes, names. There were problems of abnormal medical institution coding content, irregularities in the correspondence between medical institution codes, names.60.3% of record items had disease diagnosis codes that did not conform to standards. There were48non-basic medical insurance participants, involving405record items. In the consistency dimension, 4.9% of record items had payment prices that exceeded the payment standard by ±10%;1.4% of pharmacy purchase data items corresponded to retail pharmacies outside the “dual-channel” recognition scope;78record items did not qualify for medical insurance coverage of patient age or prescription hospital conditions. Conclusion: There are quality issues in medical insurance data, such as missing data, irregular application of medical security information coding system, confusion of patient identity, logic error due to suspected improper payment of medical insurance. It is suggested to accelerate the exploration of appropriate uniform, operational statistical, cleaning rules which are suitable for medical insurance, promote the efficacy of medical insurance data for decision-making
Key words
medical insurance data /
data quality /
medical insurance negotiation /
medical insurance payment scope /
real world data
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Funding
国家自然科学基金“医保战略性购买视角下慢性病多层次门诊用药保障的经济效应与健康效应研究”（72404098）; 中国博士后科学基金项目“价值导向下慢性病门诊用药保障与老年人健康产出：作用机制与政策优化”（2024M761028）; 湖北省博士后创新人才培养项目（2024HBBHCXB019）; 国家资助博士后研究人员计划（GZC20240534）
