Bridging The Gap In Value-Based Care: Overcoming Technical Barriers Through Intelligent Health IT Systems

Abstract

The transition from fee-for-service models to value-based care (VBC) aims to enhance healthcare quality while reducing costs. However, widespread adoption of VBC has been hindered by fragmented data systems, lack of interoperability, and inadequate real-time analytics capabilities. This paper explores how intelligent health IT systems—leveraging artificial intelligence (AI), natural language processing (NLP), and smart interoperability frameworks—can overcome these barriers and enable scalable, patient-centred VBC delivery. We examine system architectures that integrate diverse clinical, administrative, and behavioural datasets using standards such as HL7 FHIR and explore how machine learning can optimize care pathways, identify at-risk populations, and support outcome-based reimbursement models. Through case studies of successful implementations in accountable care organizations (ACOs) and health systems, we illustrate how intelligent IT infrastructure enhances decision-making, improves patient engagement, and streamlines reporting. Finally, we propose a blueprint for deploying adaptive, privacy-compliant IT ecosystems that align with evolving regulatory and clinical requirements. This paper provides actionable insights for policymakers, clinicians, and IT leaders seeking to transform the delivery of value-based healthcare.