Collaborative artificial intelligence system for investigation of healthcare claims compliance

• Published in: Scientific reports Vol. 14; no. 1; pp. 11884 - 17
• Main Authors: Sbodio, Marco Luca, López, Vanessa, Hoang, Thanh Lam, Brisimi, Theodora, Picco, Gabriele, Vejsbjerg, Inge, Rho, Valentina, Mac Aonghusa, Pol, Kristiansen, Morten, Segrave-Daly, John
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 24.05.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 692/700/1538; 692/700/3934; Algorithms; Artificial Intelligence; Automation; Collaboration; Compliance; Delivery of Health Care; Fraud; Health care; Health care policy; Health policy; Humanities and Social Sciences; Humans; Insurance Claim Review; Investigations; Knowledge representation; Learning algorithms; Machine Learning; Medical wastes; Medicare; multidisciplinary; Natural language; Ontology; Performance evaluation; Professionals; Science; Science (multidisciplinary); Subject specialists; User interface
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-62665-0

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.