1. Collaborative artificial intelligence system for investigation of healthcare claims compliance.
- Author
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Sbodio ML, López V, Hoang TL, Brisimi T, Picco G, Vejsbjerg I, Rho V, Mac Aonghusa P, Kristiansen M, and Segrave-Daly J
- Subjects
- Humans, Fraud, Machine Learning, Delivery of Health Care, Insurance Claim Review, Artificial Intelligence
- Abstract
Healthcare 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., (© 2024. The Author(s).)
- Published
- 2024
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