END-TO-END PATIENT-TO-PAYMENT ANALYTICS: LEVERAGING AI/ML TO ACCELERATE REIMBURSEMENT CYCLES IN HEALTHCARE
Keywords:
AI, ML, Healthcare, Patient, Analytics, Reimbursement, PaymentAbstract
This work introduces a comprehensive analytics system using AI, ML and LLMs to streamline the reimbursement process of healthcare. Using data from different areas, the framework spots likely claim denials, automatically handles and processes exceptions and expresses payer rules as actions. According to the research, healthcare providers can use real-time risk scores, reduce denials, and gain better insight into their finances. By stressing orchestration over solo improvements, the study reveals that intelligent analytics boost both speed to payments and the integrity of company revenues. The discoveries suggest key actions for RCM leaders who want to modernize workflows in all types of payment systems.
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Copyright (c) 2025 Sandeep Aluvaka (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
