Development of needs-resources prediction model using integrated case management big data

Abstract

Participants H. Kim (Korea), T. Lee (Korea), E. Kim (Korea), and Y. Kim (Korea). ISSUE South Korea faces rapid population aging and an increased burden of healthcare costs and institutionalization. Policy initiatives that promote community-based integrated care have been launched since 2018, and various research and demonstration programs have been launched and executed. Technology is a known key to the success of such large system change, and it has been widely adopted in many demonstration programs and related research projects. Limited evidence and strategies have been reported, however, on how technology can contribute to reducing inequalities in health and care. CONTENT Our symposium is based on two government-funded projects aiming to advance healthand social-care delivery for low-income older populations. One is a machine learning (ML) prediction modeling project using nationwide integrated care management databases for social services, and the other is an ongoing health equity intervention project, a health and wellness program (HWePS) for community-dwelling seniors that has used a mobile app and other technologies since 2019 and continuing throughout the COVID-19 pandemic. We will highlight 1) the policy and social contexts of these technology-enhanced senior health and care projects in communities in Korea, 2) the development and validations of the technology (the mobile app and ML algorithms) in meeting specific project goals, and 3) the implementation process and (early) findings as well as policy and practical lessons from three case studies on the use of these innovative tools for design and delivery of integrated health and social care. STRUCTURE H. Kim first will present an overview of the two technology-enhanced, integrated healthand social-care management projects for older community-dwelling people including the goals, theoretical background, study design, settings, and data. Next, Y. Kim and colleagues will present a care-needs resource-use pattern analysis using machine learning methods to develop and analyze multi-year, nationwide, integrated casemanagement databases targeting the low-income older population receiving social welfare services. Lee and colleagues will then present the development and validation results of a self-checkup module in the HWePS project app, which is the foundation for making evidence-based, individualized (self)-care management plans for older people. E. Kim and colleagues will present the development and use of a care-management module in the HWePS app and the early health and service outcomes. CONCLUSION Through real-world case studies, this symposium will inform the audience of the large potential of ICT use in improving service quality and the health and wellbeing of socially disadvantaged older adults in resource-limited policy environments. It will also explore multi-dimensional challenges and also potential strategies to overcome these challenges and scale-up the projects in the next stages.