In an effort to better understand and manage this segment of the patient population, many hospitals are turning to MedeAnalytics’ Self-Pay Analytics solution. Based on IBM SPSS predictive analytics software, the solution is enabling hospitals with large self-pay populations to measure and predict patient payment behavior, reduce risk from bad debt, and boost collection rates.
A better way to tackle self-pay collections
Most hospitals operate according to a treat-first, seek-payment-later protocol — an approach that works well ethically but not necessarily financially. In fact, research has shown that only about 15 percent of self-pay patients end up paying for their services, with the balance being written off by healthcare organizations as bad debt. The collection process itself can be time-consuming and expensive, requiring hours of phone calling and letter writing. Often, hospitals turn to outside collection agencies to help with the process, but that is a costly alternative.
MedeAnalytics, however, provides a better way to tackle the self-pay problem. Among other services, the healthcare performance analytics company develops statistical models that help hospitals prioritize which self-patients are likely to pay, and focus collections efforts on this highyield segment of the population. “We want to maximize the productivity of collectors by giving them a list of patients who are more likely to pay the hospital back and put the people who are unlikely to pay down at the bottom of the list,” says David Mould, Ph.D., predictive analytics scientist for MedeAnalytics and the principal developer of self-pay models used by more than 100 hospitals throughout the U.S.