Joudaki Hossein, Rashidian Arash, Minaei-Bidgoli Behrouz, Mahmoodi Mahmood, Geraili Bijan, Nasiri Mahdi, Arab Mohammad
Health Economics Group, Social Security Organization, Tehran, Iran.
Department of Health Management and Economics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Int J Health Policy Manag. 2015 Nov 10;5(3):165-72. doi: 10.15171/ijhpm.2015.196.
We aimed to identify the indicators of healthcare fraud and abuse in general physicians' drug prescription claims, and to identify a subset of general physicians that were more likely to have committed fraud and abuse.
We applied data mining approach to a major health insurance organization dataset of private sector general physicians' prescription claims. It involved 5 steps: clarifying the nature of the problem and objectives, data preparation, indicator identification and selection, cluster analysis to identify suspect physicians, and discriminant analysis to assess the validity of the clustering approach.
Thirteen indicators were developed in total. Over half of the general physicians (54%) were 'suspects' of conducting abusive behavior. The results also identified 2% of physicians as suspects of fraud. Discriminant analysis suggested that the indicators demonstrated adequate performance in the detection of physicians who were suspect of perpetrating fraud (98%) and abuse (85%) in a new sample of data.
Our data mining approach will help health insurance organizations in low-and middle-income countries (LMICs) in streamlining auditing approaches towards the suspect groups rather than routine auditing of all physicians.
我们旨在确定全科医生药物处方索赔中的医疗欺诈和滥用指标，并找出更有可能实施欺诈和滥用行为的全科医生子集。
我们将数据挖掘方法应用于一个主要健康保险组织的私营部门全科医生处方索赔数据集。它包括5个步骤：明确问题的性质和目标、数据准备、指标识别与选择、聚类分析以识别可疑医生，以及判别分析以评估聚类方法的有效性。
总共制定了13个指标。超过一半的全科医生（54%）是实施滥用行为的“嫌疑人”。结果还确定2%的医生为欺诈嫌疑人。判别分析表明，这些指标在检测新数据样本中涉嫌欺诈（98%）和滥用（85%）的医生方面表现良好。
我们的数据挖掘方法将有助于低收入和中等收入国家（LMICs）的健康保险组织简化对可疑群体的审计方法，而不是对所有医生进行常规审计。
