MLOps Best Practices for Automating Patient Pre-Authorization in Insurance Workflows

Abstract

Automating patient pre-authorization (PA) with MLOps is redefining healthcare insurance workflows due to it being more efficient and removing delays, the burden of admin, and compliance issues. Instead of doing manual reviews, faxes, and phone calls, PA had been the slowest part of the process, error-prone, and high-cost. MLOps moves in the whole, continuously maturing line that links multimodal data—like EHR notes, claims data, and imaging metadata—with the models trained to perform such tasks as eligibility assessment, prediction of medical necessity, evaluation of documentation completeness, and routing of next-best actions. The latter method replaces the fragile automation with scalable, intelligent systems regulated by good practices such as versioned data lineage, reproducible training, automated testing, containerized packaging, compliance-embedded CI/CD pipelines, and real-time monitoring for model drift and data quality. The infrastructure is at the center of the incorporation of regulatory mitigations—the infrastructure is HIPAA and GDPR, payer audit, and clinical safety compliant. Feedback loops from claim adjudications and human overrides power active learning and retraining to guarantee that the models become an accurate reflection of the changing payer policies and population dynamics. The outcome is shorter cycles (from days to minutes), fewer rejections, improved quality of documentation, and more transparent decision-making. Organizations enjoy governance artifacts such as audit logs, model cards, and access controls that support accreditation and contracting. The implementations that work well will be those that combine technical tools and human workflows: clinicians receive real-time checklists, payer medical directors deal with edge cases, and interoperability standards (FHIR prior auth, X12 278, and secure APIs) allow integration of payer systems to be smooth