GIBD receives $30,000 from the USC BDHSC Pilot Project Program to conduct a pilot study of developing a novel network-based big data approach to measure healthcare utilization disparity. The project team include Drs. Zhenlong Li, Shan Qiao, Bankole Olatosi, and Jiajia Zhang.
Project summary: Healthcare utilization is a critical factor that influences population health and wellbeing. To identify, explain, and address disparities and inequities in healthcare utilization, it is necessary to develop a valid measurement approach that can accurately capture the disparities and explore the factors that contribute to the disparities in a timely manner. Increasing attention is being paid to developing constructs or measurement approaches that can reflect complex interplays of factors at multiple socioecological levels. The availability of healthcare Big Data (e.g., large place visitation data sampled from mobile devices and electronic health records [EHR]) and advanced Big Data analytics makes it possible to use Big Data approaches to address existing knowledge gaps in measurement methodology including a lack of real-world evidence, limited availability of real-time and large-coverage datasets, and a dearth of studies applying multilevel perspectives. In this pilot project, we propose to develop a network-based big data approach to measure and visualize disparities in healthcare utilization in South Carolina (SC). Specifically, we will first develop a machine learning-based network prediction model to construct a statewide healthcare visitation network using cellphone-based place visitation data and ground-truth EHR data (for model training and refining); based on the validated statewide healthcare visitation network, we will detect actual catchment areas of healthcare facilities and develop healthcare utilization measures (indices) using geographically constrained network partition and aggregation. We will then test the performance and utility of the network-based big data approach in revealing healthcare utilization patterns using multivariate geo-visualization. Leveraging our fruitful collaboration with the state’s health department and health agencies and successful experiences with implementing NIH-funded Big Data studies since 2017, we will be able to develop this network-based big data approach for analyzing healthcare utilization disparity, which, with proven efficacy, will contribute to the paradigm shift from sampling-based study to population-based real-world study and the examination of interplays between factors at various socioecological levels.