Foreword
Data mining algorithms are a crucial tool for payers in advancing payment integrity and reducing improper payments. These sophisticated algorithms analyze vast amounts of healthcare data to uncover patterns, anomalies, and potential instances of fraud, waste, and abuse that human reviewers might miss. In the complex landscape of healthcare payments, algorithms serve as powerful tools in both prepay and post pay workflows.
Historically, payment integrity efforts relied on simplified rule-based systems. However, as data volume grew exponentially, more advanced techniques became necessary. Early data mining efforts as well as many of the algorithms used today focus mostly on post pay analysis, identifying overpayments after they occur. While effective, the pay and chase model has its limitations.
Over time, payers have been shifting to adding a proactive approach, and implemented prepay data mining alongside post pay algorithms. This dual strategy has proven effective in reducing improper payments while enhancing provider relationships and eliminating some of the administrative burdens of post pay processes.
This article takes a deeper dive into how algorithms have advanced over the past few years and where algorithms are growing and improving over the next few years to strengthen the system and catch potential
errors as early as possible.