Identifying outlier patterns of inconsistent ambulance billing in Medicare

Abstract

Objective To illustrate a method that accounts for sampling variation in identifying suppliers and counties with outlying rates of a particular pattern of inconsistent billing for ambulance services to Medicare Data Sources U.S. Medicare claims for a 20% simple random sample of 2010-2014 fee-for-service beneficiaries Study Design We identified instances in which ambulance suppliers billed Medicare for transporting a patient to a hospital, but no corresponding hospital visit appeared in billing claims. We estimated the distributions of outlier supplier and county rates of such ‘ghost rides’ by fitting a nonparametric Empirical Bayes model with flexible distributional assumptions to account for sampling variation. Data Collection We included Basic and Advanced Life Support ground emergency ambulance claims with a hospital destination. Principal Findings ‘Ghost ride’ rates varied considerably across both ambulance suppliers and counties. We estimated 6.1% of suppliers and 5.0% of counties had rates that exceeded 3.6%, which was twice the national average of ‘ghost rides’ (1.8% of all ambulance transports). Conclusions Health care fraud and abuse are frequently asserted but can be difficult to detect. Our data-driven approach may be a useful starting point for further investigation. Keywords Medicare, ambulances, fraud, abuse, expectation-maximization