Data mining to predict and prevent errors in health insurance claims processing

Abstract

Health insurance costs across the world have increased alarmingly in recent years. A major cause of this increase are payment errors made by the insurance companies while processing claims. These errors often result in extra administrative effort to re-process (or rework) the claim which accounts for up to 30% of the administrative staff in a typical health insurer. We describe a system that helps reduce these errors using machine learning techniques by predicting claims that will need to be reworked, generating explanations to help the auditors correct these claims, and experiment with feature selection, concept drift, and active learning to collect feedback from the auditors to improve over time. We describe our framework, problem formulation, evaluation metrics, and experimental results on claims data from a large US health insurer. We show that our system results in an order of magnitude better precision (hit rate) over existing approaches which is accurate enough to potentially result in over $15-25 million in savings for a typical insurer. We also describe interesting research problems in this domain as well as design choices made to make the system easily deployable across health insurance companies.

Graph based anomaly detection and description: a survey

“…Examples include detecting network intrusion or network failure , Idé and Kashima, 2004, credit card fraud [Bolton and Hand, 2001], calling card and telecommunications fraud [Cortes et al, 2002, Taniguchi et al, 1998, auto insurance fraud [Phua et al, 2004], health insurance claim errors [Kumar et al, 2010], accounting inefficiencies [McGlohon et al, 2009], email and Web spam [Castillo et al, 2007], opinion deception and reviews spam [Ott et al, 2012], auction fraud [Pandit et al, 2007], tax evasion [Abe et al, 2010, Wu et al, 2012, customer activity monitoring and user profiling Provost, 1996, 1999], click fraud [Jansen, 2008, Kshetri, 2010, securities fraud [Neville et al, 2005], malicious cargo shipments [Das andSchneider, 2007, Eberle andHolder, 2007] malware/spyware detection [Invernizzi and Comparetti, 2012, Ma et al, 2009, Provos et al, 2007, false advertising , data-center monitoring [Li et al, 2011b], insider threat [Eberle and Holder, 2009], image/video surveillance [Damnjanovic et al, 2008, Krausz andHerpers, 2010], and many others.…”

Rule-Based Prediction of Medical Claims' Payments: A Method and Initial Application to Medicaid Data

“…Few published papers focus on detection of detection of claims that most likely will be denied. The work most similar to one presented in this paper is by Kumar et al [8] who used support vector machines to identify such claims. The authors used a large dataset from an insurance company (3.5 million claims with significantly oversampled incorrect ones).…”

Statistical Medical Fraud Assessment: Exposition to an Emerging Field

“…He et al () use k‐nearest neighbour algorithm to classify practitioners' practice profiles. As an alternative, a support vector machine‐based approach is utilised by Kumar et al (). A number of the medical detection efforts include combining different supervised methods.…”