Identifying Homogeneous Patient Clusters in Swiss University Hospital Through Latent Class Analysis.

Abstract

In hospitalized populations, there is significant heterogeneity in patients' characteristics, disease severity, and treatment responses, which translates into different related outcomes and costs. Identifying inpatient clusters with similar clinical profiles could lead to better quality and personalized care while improving clinical resources used. Super-utilizers (SUs) are one 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 Hospitals of Geneva per year in 2017 - 2019. Using Latent Class Analysis it identifies 8 subgroups with highly similar patients demographics, medical conditions, types of service and costs within groups and which are highly different between groups. As such 82% of all SU patients, 99% of all patients less than 20 years old and 78% of all orthopedics patients are clustered into only 3 separate groups while one group contain only adult women 90% of them 20 to 40 years of age.