由于认知差异与边界模糊等问题,“治疗三费”（康复、理疗、中医治疗产生的费用）一直是影响医保基金合理使用、困扰监管执法的痼疾。“治疗三费”问题监管模型是针对这一痼疾开发的监管工具,旨在基于无监督自我学习进行精准预测和未知发现。模型通过数据预处理、多维聚类、疑点医师簇锁定等方法,实现“治疗三费”问题预警周期化、监控实时化、处置常态化。该模型已在某市医保监管中应用,根据模型预警线索有效识别潜在疑点,并通过跨部门合作,采用对医师开展监督检查、行政约谈和“驾照式”记分等方式,有效引导医疗机构及医师规范诊疗行为。

Due to issues such as cognitive differences and blurred boundaries, "three treatment fees" (expenses incurred from rehabilitation, physiotherapy, and traditional Chinese medicine treatments) have been maladies affecting the rational use of medical insurance funds and regulatory enforcement. The "three treatment fees" supervision model is a regulatory tool developed to address this issue, based on unsupervised self-learning for precise prediction and discovery of the unknown. By employing methods such as data preprocessing, multidimensional clustering, and suspicious physician cluster locking, the model is constructed to achieve periodic warning, real-time monitoring, and normalized handling. This model has been applied in the medical insurance supervision of a city, which can effectively identify potential suspicious physicians based on model alerts. Through inter-departmental cooperation, the model can effectively guide medical institutions and physicians to standardize their diagnosis and treatment behaviors using methods such as supervision and inspection, administrative discussions, and "driver's license" style scoring.