Collaborative artificial intelligence system for investigation of healthcare claims compliance

Author:

Sbodio Marco Luca,López Vanessa,Hoang Thanh Lam,Brisimi Theodora,Picco Gabriele,Vejsbjerg Inge,Rho Valentina,Mac Aonghusa Pol,Kristiansen Morten,Segrave-Daly John

Abstract

AbstractHealthcare fraud, waste and abuse are costly problems that have huge impact on society. Traditional approaches to identify non-compliant claims rely on auditing strategies requiring trained professionals, or on machine learning methods requiring labelled data and possibly lacking interpretability. We present Clais, a collaborative artificial intelligence system for claims analysis. Clais automatically extracts human-interpretable rules from healthcare policy documents (0.72 F1-score), and it enables professionals to edit and validate the extracted rules through an intuitive user interface. Clais executes the rules on claim records to identify non-compliance: on this task Clais significantly outperforms two baseline machine learning models, and its median F1-score is 1.0 (IQR = 0.83 to 1.0) when executing the extracted rules, and 1.0 (IQR = 1.0 to 1.0) when executing the same rules after human curation. Professionals confirm through a user study the usefulness of Clais in making their workflow simpler and more effective.

Publisher

Springer Science and Business Media LLC

Reference64 articles.

1. Healthcare Fraud, Waste and Abuse-Humana. https://www.humana.com/legal/fraud-waste-and-abuse. Accessed 1 Aug 2022.

2. Shrank, W. H., Rogstad, T. L. & Parekh, N. Waste in the US health care system: Estimated costs and potential for savings. JAMA 322, 1501–1509 (2019).

3. Office, U. S. G. A. Improper Payments. https://www.gao.gov/improper-payments. Accessed 1 Aug 2022.

4. Mohun, J. & Roberts, A. Cracking the Code. (2020).

5. Morris, J. Blawx: Rules as code demonstration. In MIT Computer Law Report (2020).

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