Characterizing High-Needs High-Cost Patients with Segmentation
Healthcare utilization is extremely unevenly distributed in the population. Earlier research suggests that up to 50% of resources of a given healthcare system are used by only 5% of patients. This group is often called high-needs high-cost patients. While the existence of this high-needs high-cost patient group is well established, the group has not been properly characterized. We know that these patients are likely to have multiple chronic health conditions and that they need a lot of care to manage their health, which cannot be properly modeled using industry standard methods of inspecting patient groups one diagnosis and one visit at a time. In this talk, Juulia presents work done at Västra Götalandsregionen to detect and characterize this patient group using unsupervised learning. She will share some early findings and lessons learned from introducing advanced analytics to an organization more familiar with reporting and classical statistics.
Dr. Juulia Suvilehto is a senior data scientist at Sahlgrenska University Hospital, with over a decade of experience in data analytics for life sciences. Her work spans biotech startups, academia and now healthcare. In her current role she’s bridging the gap between clinical and technical expertise to advance AI in clinical settings. She is the founder of a network for Swedish healthcare data scientists, facilitating knowledge exchange and collaboration. Dr. Suvilehto is committed to making AI accessible to underrepresented groups and demystifying technology for non-technical individuals. She holds a PhD in systems neuroscience from Aalto University.
