Multicriteria decision frontiers for prescription anomaly detection over time. Issue 14 (26th October 2022)

• Record Type: Journal Article
• Authors: Zafari, Babak; Ekin, Tahir; Ruggeri, Fabrizio
• Is Part Of: Journal of applied statistics. Volume 49:Issue 14(2022)
• Page Start: 3638
• Page End: 3658
• Publication Date: 2022-10-26
• Subjects: Multivariate anomaly detection -- decision models -- Medicare Part D -- topic model -- prescription patterns -- health care fraud; Statistics -- Periodicals; 519.5
• DOI: 10.1080/02664763.2021.1959528
• Languages: English
• ISSNs: 0266-4763

Abstract

ABSTRACT: Health care prescription fraud and abuse result in major financial losses and adverse health effects. The growing budget deficits of health insurance programs and recent opioid drug abuse crisis in the United States have accelerated the use of analytical methods. Unsupervised methods such as clustering and anomaly detection could help the health care auditors to evaluate the billing patterns when embedded into rule-based frameworks. These decision models can aid policymakers in detecting potential suspicious activities. This manuscript proposes an unsupervised temporal learning-based decision frontier model using the real world Medicare Part D prescription data collected over 5 years. First, temporal probabilistic hidden groups of drugs are retrieved using a structural topic model with covariates. Next, we construct combined concentration curves and Gini measures considering the weighted impact of temporal observations for prescription patterns, in addition to the Gini values for the cost. The novel decision frontier utilizes this output and enables health care practitioners to assess the trade-offs among different criteria and to identify audit leads.