Multivariate outlier detection in medicare claims payments applying probabilistic programming methods

AbstractThe rising elderly population continues to demand more cost-effective healthcare programs. In particular, Medicare is a vital program serving the needs of the elderly in the United States. The growing number of people enrolled in healthcare programs such as Medicare, along with the enormous volume of money in the healthcare industry, increases the appeal for, and risk of, fraudulent activities. Out of the many possible factors for the rising cost of healthcare, fraud is a major contributor, but its impacts can be lessened through the use of fraud detection methods. In this paper, we assess possible illegitimate activities by looking at the amounts paid to providers for services rendered to patients. We propose a novel method for fraud detection that focuses on discovering outliers in Medicare payment data using multiple predictors as model inputs. Our multivariate outlier detection approach is twofold: (1) create a Multivariate Adaptive Regression Splines model to produce studentized residuals and, (2) use the residuals as input into a general univariate outlier detection model, based on full Bayesian inference, using probabilistic programming. Using this approach, we are able to incorporate multiple variables to detect outliers with a model that provides probability distributions, with credible intervals, rather than just point values, as with most common outlier detection methods. Additionally, these credible intervals further enhance confidence that the detected ou...
Source: Health Services and Outcomes Research Methodology - Category: Statistics Source Type: research