Context Discovery and Cost Prediction for Detection of Anomalous Medical Claims, with Ontology Structure Providing Domain Knowledge

James Kemp, Christopher Barker, Norm Good, Michael Bain

Research output: Chapter in Book/Conference paperConference paperpeer-review

Abstract

Medical fraud and waste is a costly problem for health insurers. Growing volumes and complexity of data add challenges for detection, which data mining and machine learning may solve. We introduce a framework for incorporating domain knowledge (through the use of the claim ontology), learning claim contexts and provider roles (through topic modelling), and estimating repeated, costly behaviours (by comparison of provider costs to expected costs in each discovered context). When applied to orthopaedic surgery claims, our models highlighted both known and novel patterns of anomalous behaviour. Costly behaviours were ranked highly, which is useful for effective allocation of resources when recovering potentially fraudulent or wasteful claims. Further work on incorporating context discovery and domain knowledge into fraud detection algorithms on medical insurance claim data could improve results in this field.
Original languageEnglish
Title of host publicationProceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies
PublisherSciTe Press
Pages29-40
Number of pages12
DOIs
Publication statusPublished - 7 Mar 2023
Externally publishedYes
Event16th International Joint Conference on Biomedical Engineering Systems and Technologies - Lisbon, Portugal
Duration: 16 Feb 202318 Feb 2023
https://biosignals.scitevents.org/?y=2023

Conference

Conference16th International Joint Conference on Biomedical Engineering Systems and Technologies
Country/TerritoryPortugal
CityLisbon
Period16/02/2318/02/23
Internet address

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