Gilles Cohen, Pascal Briot and Pierre Chopard, Geneva University Hospitals Geneva, Switzerland

In hospitalized populations, there is significant heterogeneity in patient characteristics, disease severity, and treatment responses, which generally translates into very different related outcomes and costs. A better understanding of this heterogeneity can lead to better management, more effective and efficient treatments by personalizing care to better meet patients' profiles. Thus, identifying distinct clinical profiles among patients can lead to more homogenous subgroups of patients. Super-utilizers (SUs) are such a group, who contribute a substantial proportion of health care costs and utilize a disproportionately high share of health care resources. This study uses cost, utilization metrics and clinical information to segment the population of patients (N=32,759) admitted to the University Hospital of Geneva in 2019 and thus identifies the characteristics of its SUs group using Latent Class Analysis. This study shows how cluster analysis might be valuable to hospitals for identifying super-utilizers within their patient population and understanding their characteristics.

Latent Class Analysis, Clustering, Super-Utilizers, Inpatient Segmentation, Hospital Efficiency, Quality Improvement.