A Statistical Model to Detect DRG Upcoding

The Medicare program, private insurers, and managed care organizations reimburse hospitals for inpatient admissions using the Diagnosis Related Group (DRG). The DRG is determined from a complicated algorithm based on patient medical records. Previous studies generated concerns about ‘DRG upcoding’, in which incorrect DRG codes may be selected by the hospital to obtain higher reimbursement. Insurers rely on expensive manual audits of claims to verify the appropriateness of the DRG coding.A statistical system that can adaptively detect claims with incorrect DRG codes would provide a powerful improvement to current practice. This paper describes two aspects of the statistical system that provides proof that the concept is viable. The first aspect of the paper is the design of a hierarchical Bayesian model to be applied to claims data (without audit) to estimate the probability that a claim is coded incorrectly. The second aspect of the paper is the use of the Bayesian model to aid in the selection process of claims to audit by proposing that a claim should be investigated if the predicted recovery is more than the cost of auditing that claim. This approach improves upon that used currently by auditing 88% of the claims and recovering 98% of the overpayments. While these results improve upon the current approach for determining which claims to investigate, they are based on data that have been systematically selected for audit based on one insurer's past experience. Future work will create an adaptive system to determine the selection of claims to audit from the entire paid claims database, and that can be generalized for use by other insurers.