Enriching Medicare Severity-Diagnosis Related Group (MS-DRG) Payments for better Service to inpatients using ANFIS

K. Chimwayi, Noorie Haris, Ronnie D. Caytiles, N. Iyengar
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Abstract

Variations in the cost for the same diagnosis among different hospital providers is a great concern to the public at large. With huge amounts of data being availed every second, utilising the data for the benefit of the society is commendable. In this research a neuro-fuzzy approach is proposed for Medicare payments data. Machine learning clustering algorithms on neuro-fuzzy results are compared to understand the variations in price for same treatment and diagnosis among different healthcare providers. Cluster analysis has been applied in various domains to help reveal hidden structures. Cluster analysis has not been well exploited in healthcare claims datasets, the reason being that healthcare expenditure data is highly skewed which make analysis complicated. The Inpatient charges is a large dataset that has 163065 and 12 attributes describing amounts paid by Centers for Medicare and Medicaid Services (CMS) to different healthcare providers using different Diagnostic Related Group (DRGs).
充实医疗保险严重诊断相关组(MS-DRG)支付,为使用ANFIS的住院患者提供更好的服务
不同医院提供相同诊断的费用差异是广大公众非常关注的问题。每秒钟都有大量的数据被使用,利用这些数据为社会造福是值得称赞的。在这项研究中,神经模糊方法提出了医疗保险支付数据。对神经模糊结果的机器学习聚类算法进行比较,以了解不同医疗保健提供者之间相同治疗和诊断的价格变化。聚类分析已应用于各个领域,以帮助揭示隐藏的结构。聚类分析还没有很好地利用医疗保健索赔数据集,原因是医疗保健支出数据高度倾斜,这使得分析复杂。住院病人收费是一个大型数据集,有163065个属性和12个属性,描述了医疗保险和医疗补助服务中心(CMS)使用不同的诊断相关组(drg)向不同的医疗保健提供者支付的金额。
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