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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 US 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.


Sanghavi, Prachi, Anupam B. Jena, Joseph P. Newhouse, and Alan M. Zaslavsky. "Identifying outlier patterns of inconsistent ambulance billing in Medicare." Health Services Research 56.2 (April 2021): 188-192.