AI-Powered Real-Time Claims & Provider Intelligence
Keywords:
Claim processing, health insurance, fraud detection, data integrity, entitlement verification, data intelligence, data governance, risk management, data privacy.0Abstract
Achieving such an outcome requires investing in a robust data infrastructure that sources, integrates, cleans, and curates multiple external data feeds, deploying a set of data-science-based machine-learning components for real-time claims processing, architecting a suitable system landscape, and establishing a governing framework that addresses privacy and compliance obligations as well as potential risks associated with fraud and data exposure. Proposed design considerations and patterns serve to guide the final solution and also provide a foundation for extension into the broader area of automated claims management.
Claims processing within the payer domain remains an unsatisfactorily solved problem. Parties on both sides of health system transactions perceive inefficiencies—providers as delay, payers as exposure to fraud and waste—and there are even news reports of claims processing fraud perpetrated against payers. Payers are also subject to the Inflation Reduction Act’s requirement for the Secretary of Health and Human Services to negotiate maximum prices for select drugs beginning in 2026—a mandate that presents a different set of pressures, including the need to ensure that negotiated prices reflect the benefits conferred by enrolled patients’ use of those drugs.
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