Identifying outlier patterns of inconsistent ambulance billing in Medicare

Health Serv Res. 2021 Apr;56(2):188-192. doi: 10.1111/1475-6773.13622. Epub 2021 Jan 25.


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.

Keywords: Medicare; abuse; ambulances; expectation-maximization; fraud.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Ambulances / statistics & numerical data*
  • Bayes Theorem
  • Fee-for-Service Plans / statistics & numerical data*
  • Fraud / statistics & numerical data*
  • Humans
  • Insurance Claim Review
  • Insurance, Health, Reimbursement / statistics & numerical data*
  • Medicare / statistics & numerical data*
  • United States