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

James Kemp, Chris Barker, Norm Good, Michael Bain

2023

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.

Paper Citation

in Harvard Style

Kemp J., Barker C., Good N. and Bain M. (2023). Context Discovery and Cost Prediction for Detection of Anomalous Medical Claims, with Ontology Structure Providing Domain Knowledge. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 5: HEALTHINF; ISBN 978-989-758-631-6, SciTePress, pages 29-40. DOI: 10.5220/0011611000003414

in Bibtex Style

@conference{healthinf23,
author={James Kemp and Chris Barker and Norm Good and Michael Bain},
title={Context Discovery and Cost Prediction for Detection of Anomalous Medical Claims, with Ontology Structure Providing Domain Knowledge},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 5: HEALTHINF},
year={2023},
pages={29-40},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011611000003414},
isbn={978-989-758-631-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 5: HEALTHINF
TI - Context Discovery and Cost Prediction for Detection of Anomalous Medical Claims, with Ontology Structure Providing Domain Knowledge
SN - 978-989-758-631-6
AU - Kemp J.
AU - Barker C.
AU - Good N.
AU - Bain M.
PY - 2023
SP - 29
EP - 40
DO - 10.5220/0011611000003414
PB - SciTePress