Prachi Sanghavi PhD, Anupam B. Jena M.D., Ph.D., Joseph P. Newhouse Ph.D., Alan M. Zaslavsky Ph.D.
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.
US Medicare claims for a 20% simple random sample of 2010‐2014 fee‐for‐service beneficiaries.
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.
We included Basic and advanced life support ground emergency ambulance claims with a hospital destination.
“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).
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.