Search

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. [ABSTRACT FROM AUTHOR]

Topic modeling has become an extraordinary tool for analyzing large, unclassified documents to detect patterns that use similar words. It also permits the discovery of hidden themes that pervade the collection, allowing annotation according to those themes, and then using these to summarize and search the text. This paper focuses on Latent Dirichlet Allocation, which is one of the most widely used methods for topic modeling. Trend detection and evolution of topics relating to research topics was attempted from the Journal of the Modern Language Association of America. The study also identified those documents that explained topics according to time series and citation. The experimental data consisted of 5,605 articles from the Journal between the years 1889-2007 held in the Journal Storage digital library. The results show that the Latent Dirichlet Allocation model can effectively detect distinct topics and those documents that explained these topics over time. [ABSTRACT FROM AUTHOR]