Premium
Identifying outlier patterns of inconsistent ambulance billing in Medicare
Author(s) -
Sanghavi Prachi,
Jena Anupam B.,
Newhouse Joseph P.,
Zaslavsky Alan M.
Publication year - 2021
Publication title -
health services research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.706
H-Index - 121
eISSN - 1475-6773
pISSN - 0017-9124
DOI - 10.1111/1475-6773.13622
Subject(s) - outlier , bayes' theorem , sample (material) , ambulance service , medical emergency , business , medicine , actuarial science , statistics , bayesian probability , mathematics , chemistry , chromatography
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.