Procedure code overutilization detection from healthcare claims using unsupervised deep learning methods.

ABSTRACT:

Background

Fraud, Waste, and Abuse (FWA) in medical claims have a negative impact on the quality and cost of healthcare. A major component of FWA in claims is procedure code overutilization, where one or more prescribed procedures may not be relevant to a given diagnosis and patient profile, resulting in unnecessary and unwarranted treatments and medical payments. This study aims to identify such unwarranted procedures from millions of healthcare claims. In the absence of labeled examples of unwarranted procedures, the study focused on the application of unsupervised machine learning techniques.

Methods

Experiments were conducted with deep autoencoders to find claims containing anomalous procedure codes indicative of FWA, and were compared against a baseline density-based cl

SUBMITTER: Suesserman M 

PROVIDER: S-EPMC10536726 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature