A management tool for controlling the rate of non-acceptable inpatient hospital claims.
This paper demonstrates a tool for substantially improved monitoring of the validity of health insurance claims. Using a Bayesian regression model, we predict the probability of a non-acceptable claim (NAC) for each claim record and the expected number of NACs for any set of claims. When applied to a large set of hospital discharge claims, the tool shows a substantial improvement in the ability to estimate the actual number of NACs in the set. The tool permits ongoing monitoring of claims, more precise control, and a substantial reduction in audit cost in claims administration. It is conceptually applicable to other ongoing quality control systems, where inexpensively obtained information can be used to predict events that are costly to measure directly.
Rosenberg
M A
MA
Risk Management and Insurance Department, School of Business, University of Wisconsin-Madison 53706, USA.
Griffith
J R
JR
D001499:Bayes Theorem; D054598:Blue Cross Blue Shield Insurance Plans / Q000706:statistics & numerical data*; D003953:Diagnosis-Related Groups / Q000145:classification; D017598:Efficiency, Organizational; D006300:Health Services Misuse / Q000706:statistics & numerical data*; D006302:Health Services Research; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D007345:Insurance Claim Review / Q000458:organization & administration*; D016015:Logistic Models; D008330:Management Audit; D008824:Michigan; D015233:Models, Statistical; D015999:Multivariate Analysis; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D012189:Retrospective Studies; D014600:Utilization Review / Q000458:organization & administration*