Procedure code overutilization detection from healthcare claims using unsupervised deep learning methods.

Journal: BMC medical informatics and decision making
Published Date:

Abstract

BACKGROUND: Fraud, Waste, and Abuse (FWA) in medical claims have a negative impact on the quality and cost of healthcare. A major component of FWA in claims is procedure code overutilization, where one or more prescribed procedures may not be relevant to a given diagnosis and patient profile, resulting in unnecessary and unwarranted treatments and medical payments. This study aims to identify such unwarranted procedures from millions of healthcare claims. In the absence of labeled examples of unwarranted procedures, the study focused on the application of unsupervised machine learning techniques.

Authors

  • Michael Suesserman
    AI Center of Excellence, Deloitte & Touche LLP, New York, NY, USA.
  • Samantha Gorny
    Program Integrity, Deloitte & Touche LLP, New York, NY, USA.
  • Daniel Lasaga
    Program Integrity, Deloitte & Touche LLP, New York, NY, USA.
  • John Helms
    AI Center of Excellence, Deloitte & Touche LLP, New York, NY, USA.
  • Dan Olson
    Program Integrity, Deloitte & Touche LLP, New York, NY, USA.
  • Edward Bowen
    AI Center of Excellence, Deloitte & Touche LLP, New York, NY, USA.
  • Sanmitra Bhattacharya
    AI Center of Excellence, Deloitte & Touche LLP, New York, NY, USA. sanmbhattacharya@deloitte.com.