Designing Intelligent Healthcare Ecosystems through Adaptive Data Integration and Autonomous Learning Systems

Abstract

An intelligent healthcare ecosystem comprises an interconnected and mutually supportive network of healthcare stakeholders and functions aimed at improving healthcare quality and accessibility while reducing costs. Intelligent healthcare ecosystems provide continuous learning by integrating data from heterogeneous sources, such as electronic health records, wearables, and patient-reported outcomes, and feeding the data into automated decision-support systems that learn from real-world evidence. Intelligent healthcare ecosystems can be assembled in an incremental manner, with targeted investments in the development of trustworthy adaptive data integration utilities and autonomous learning systems that can deliver value independently while scaling up and maturing.

Research on the development of intelligent healthcare ecosystems is particularly timely as cross-industry stakeholders prepare for the deployment of 5G networks and the Internet of Things. The adaptability and performance of intelligent healthcare ecosystems can be enhanced by focussing on clinical decision-support and clinical risk stratification capabilities that change the conduct and treatment of care, thereby producing clinical outcomes that enhance the day-to-day activities of healthcare caregivers. Enabling a shift in the conduct of care reduces the pressure on healthcare systems and the cost of care delivery. Adaptive data integration utilities and autonomous learning systems designed specifically for healthcare permit a more flexible approach to practical realisation of intelligent healthcare ecosystems, supporting incremental integration effort and enabling an agile response to changing business needs.