Abstract
INTRODUCTION: The population is heterogeneous with varying levels of healthcare needs. Clustering individuals into health segments with more homogeneous healthcare needs allows for better understanding and monitoring of health profiles in the population, which can support data-driven resource allocation. METHODS: Using the developed criteria, data from several of Singapore's national administrative datasets were used to classify individuals into the various health segments. Cross-sectional analysis of healthcare utilization charges was conducted. Validation was done for the framework's prognostic ability of clinically relevant outcomes measured in the following year. RESULTS: The framework is comprised of twelve segments classed within four broad groups. The segments comprising individuals with cancer, with transitional care needs, and in the last year of their lives had the highest mean per resident healthcare charges. The segments comprising adults and seniors with complex chronic conditions and with transitional care needs had the highest percentage of individuals historically diagnosed with obesity. The framework was able to distinguish varying tiers of healthcare utilization charges and relative risk of death in the following year. DISCUSSION: The framework was developed using a hybrid approach, with expert input and comprehensive national data that extended beyond the usual hospital patient population. The framework can be directly applied for use in program or policy design, evaluation, and cost-effectiveness analyses. CONCLUSION: The HealthSCOPES framework was developed to segment the entire population in Singapore with similar healthcare needs.