MeDML: Med-Dynamic Meta Learning - A multi-layered representation to identify provider fraud in healthcare

Authors

  • Nitish Kumar Mastercard
  • Deepak Chaurasiya Mastercard
  • Alok Singh Mastercard
  • Siddhartha Asthana Mastercard
  • Kushagra Agarwal Mastercard
  • Ankur Arora Mastercard

DOI:

https://doi.org/10.32473/flairs.v34i1.128525

Keywords:

Representation learning, Fraud detection, Healthcare

Abstract

Every year, health insurance fraud costs taxpayers billions of dollars and puts patient’s health and welfare at risk. Existing solutions to detect fraudulent providers (hospitals, physicians, etc.) aim to find unusual pattern at claim level features but fail to harness provider-provider and provider-patient interaction information.
We propose a novel framework, Med-Dynamic meta learning (MeDML), that extends the capability of traditional fraud detection by learning patterns from 1) patient-provider interaction using temporal and geo-spatial characteristics 2) provider's treatment using encounter data (e.g. medical codes, mix of attended patients) and 3) referral using underlying provider-provider relationships based on common patient visits within 30 days. To the best of our knowledge, MeDML is first framework that can model fraud using multi-aspect representation of provider.
MeDML also encapsulates provider's phantom billing index, which identifies excessive and unnecessary services provided to patients, by segmenting frequently co-occurring diagnosis and procedures in non-fraudulent provider's claims. It uses a novel framework to aggregate the learned representations capturing their task-specific relative importance via attention mechanism. We test the dynamically generated meta embedding using various downstream models and show that it outperforms all baseline algorithms for provider fraud prediction task.

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Published

2021-04-18

How to Cite

Kumar, N., Chaurasiya, D., Singh, A., Asthana, S., Agarwal, K., & Arora, A. (2021). MeDML: Med-Dynamic Meta Learning - A multi-layered representation to identify provider fraud in healthcare. The International FLAIRS Conference Proceedings, 34. https://doi.org/10.32473/flairs.v34i1.128525

Issue

Section

Special Track: AI in Healthcare Informatics