A medical treatment based scoring model to detect abusive institutions.,Journal of Biomedical informatics

当前位置： X-MOL 学术 › J. Biomed. Inform. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

A medical treatment based scoring model to detect abusive institutions.
Journal of Biomedical informatics ( IF 4.0 ) Pub Date : 2020-05-04 , DOI: 10.1016/j.jbi.2020.103423
Jehyuk Lee ₁ , Hunsik Shin ₁ , Sungzoon Cho ₁

Affiliation

Medical abuse refers to a type of abnormal medical practice which is not in compliance with qualitative or ethical standards, such as excessive prescription or overbilling of medical services. Detection of such medical abuses is crucial, especially for the patients and insurance providers, because they become subject to the extra payments incurred. As a result, insurance providers hire medical experts in order to review claims manually, yet through examination is almost impossible due to the volume of the claims filed. A typical approach is to select institutions on suspicion of abusive practices and to manually review all claims involving suspect institutions. In this light, several studies have developed models designed to extract institution-level variables. However, since these variables are at an institution-level, the model cannot account for different types of abuse practiced by individual institutions, hence degrading the accuracy of the prediction model. At the same time, these variables contain information too simple to construct an effective scoring model. In this study, we propose a model that scores the degree of abuse practiced by institutions at the treatment-level, which is the lowest level of data that can be obtained from a medical claim. Our model is the first to use such fine-grained information to construct a model for scoring the abuse by medical institutions. The proposed model consists of two stages: Training a deep neural network with embedding layers for categorical variables, and scoring the abuse degree for each treatment with the model. Then, we aggregate the resulting abuse score of each treatment and the claim amount associated with each treatment by an institution which we define as the abuse score of the institution. We test our model using real-world claim data submitted to the Health Insurance Review and Assessment (HIRA) in 2016. We also compare the performance of the proposed model against the scoring model HIRA has been using, which computes the abuse score of an institution by using institution-level variables as proposed in past literature. Experiment results show that the proposed model represents the degree of medical abuse better. In addition, the results suggest that the reviewers may examine through the claims by at most 6.1 times more efficiently than when using the scoring model with institution-level variables.

中文翻译：

基于医疗的评分模型来检测滥用机构。

医疗滥用是指不符合定性或道德标准的异常医疗行为，例如处方过多或医疗服务收费过高。尤其是对于患者和保险提供者而言，发现此类医疗虐待至关重要，因为它们要承担额外的费用。结果，保险提供者聘请了医学专家来手动审查索赔，但是由于提出的索赔量很大，通过检查几乎是不可能的。一种典型的方法是选择涉嫌滥用行为的机构，并手动审查涉及可疑机构的所有索赔。有鉴于此，一些研究开发了旨在提取机构水平变量的模型。但是，由于这些变量是在机构级别上，该模型无法解释各个机构实施的不同类型的滥用行为，因此会降低预测模型的准确性。同时，这些变量包含的信息过于简单，无法构建有效的评分模型。在这项研究中，我们提出了一个模型，用于对机构在治疗级别上的滥用程度进行评分，这是可以从医疗索赔中获得的最低数据级别。我们的模型是第一个使用此类细粒度信息构建用于对医疗机构滥用情况进行评分的模型。所提出的模型包括两个阶段：训练具有用于分类变量的嵌入层的深度神经网络，以及使用模型对每种处理的滥用程度进行评分。然后，我们将每个机构的滥用结果得分以及与该机构相关的每种疗法的索赔金额进行汇总，我们将其定义为机构的滥用得分。我们使用2016年提交给健康保险审查与评估（HIRA）的真实索赔数据对模型进行测试。我们还将提议的模型的绩效与HIRA一直在使用的评分模型进行比较，该评分模型用于计算机构的滥用评分通过使用过去文献中提出的机构级变量。实验结果表明，该模型较好地代表了医疗滥用程度。此外，结果表明，与使用带有机构级别变量的评分模型相比，审阅者对权利要求的检查效率最高可达6.1倍。

更新日期：2020-05-04

点击分享查看原文

点击收藏

阅读更多本刊最新论文本刊介绍/投稿指南11