Mitigating Revenue Leakage: An AI Framework for Connecticut Healthcare Organizations

This paper examined the impact of AI framework for mitigating revenue leakage in Connecticut Healthcare Organizations as a result of thin margins, strict state cost benchmarks, and complex regional payer dynamics that cause severe financial strain for the organizations. The Technology Acceptance Model (TAM) served as the theoretical roadmap for the review. Using a qualitative, non-empirical conceptual review framework and thematic synthesis, this review analyzed and synthesized healthcare literature from 2019 to 2026 across major databases. The methodology mapped data pipelines across the care continuum to isolate technical friction and deployment barriers. The review indicated that integrated AI frameworks deliver substantial financial protection across regional health networks. Specifically, clinical-grade NLP optimizes comorbidity capture from unstructured documentation, predictive analytics compute pre-bill risk scores to intercept billing errors, autonomous multi-agent systems execute closed loop denial remediation, and the Patient Prism framework activates front-end appointment recovery within a 60-second window. It was concluded that AI protects revenue, but only if it fits daily workflows and protects data security. It was recommended that to lower alert fatigue, executives should use AI systems tailored to local health insurance data alongside simple, user friendly dashboards. Also, hospitals should connect separate software systems using standard APIs. This removes manual workarounds, protects data, and keeps information flowing smoothly. Next-gen AI must use self-correcting networks that audit billing and automatically adapt to new regulations.