Switzerland has some of the best health outcomes in the world and one of the most expensive systems to run. Making better use of health data is central to improving population health and keeping costs in check, and R plays a growing role in both.
This evening, we look at R and healthcare from three angles: how SWICA, a major health insurer, uses data to shift towards value-based care, how the Swiss Health Observatory uses R to plan and allocate resources across cantons, and how Swisstransplant uses statistical modelling to understand and predict organ transplants.
📢 Join us for an exciting evening on analytics in healthcare, and for the apéro!
Eva Blozik, Head of Healthcare Management at SWICA, will introduce how data analysis drives value-based care from an insurer’s perspective.
Every five years, the Swiss Health Observatory (Obsan) produces analyses based on the Swiss Health Survey (SHS), providing each canton with detailed insights into the population’s health, behaviours, and care utilisation.
In the past, these analyses were published in reports that were created by exporting SAS analyses into Excel and then manually inserting them into Word --- a tedious process that had to be replicated for every canton. In 2024, the team transitioned to a fully integrated and automated workflow using R and Excel. This shift enabled them to simultaneously generate both a web-based output and a pdf report.
This project examines regional disparities in access to primary care services in Switzerland using a Floating Catchment Area (FCA) approach developed by the Obsan. Healthcare provider capacity is estimated from health insurance claims data, while healthcare demand is modelled using population characteristics and commuter flows. Accessibility is assessed using travel times derived from road network data, with DuckDB used to process it efficiently.
Jonathan Zufferey is a demographer by training and holds a PhD from the University of Geneva. Reto Jörg is a political scientist with a postgraduate diploma in applied statistics from ETH Zürich. Both work as senior researchers at the Swiss Health Observatory (Obsan), and are co-responsible for the Swiss Health Care Atlas, a national platform tracking regional variations in healthcare use across more than 100 indicators.
In Switzerland, 292 deceased-donor kidney transplants were performed in 2025, while over 880 patients remained on the national waiting list at the end of the year. Although kidney transplantation has excellent outcomes, graft loss remains a major concern for patients and clinicians.
This talk presents the KIDMO project (Kidney Prediction Model), a statistical model to predict graft loss. Simon will explain the model development and validation pipeline, and demonstrate how these clinical prediction models can be translated into practical tools, including an R package and an online risk calculator built with Shiny, Quarto, and Posit Connect Cloud.
Simon Schwab is a statistician at Swisstransplant and a lecturer at the University of Zurich, where he teaches clinical biostatistics to medical students. He holds a PhD in Health Science and a postgraduate diploma in Statistical Data Science (both from the University of Bern).
Date: 16.09.2026
Place: Hotel Banana City, Winterthur
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