Geospatial health as interdisciplinary research for health care reform and planning

Authors: Markku Tykkylainen*, University of Eastern Finland, Department of Geographical and Historical Studies, Mikko Pyykönen, University of Eastern Finland, Department of Geographical and Historical Studies, Sami Sieranoja, University of Eastern Finland, School of Computing, Pasi Fränti, University of Eastern Finland, School of Computing, Tiina Laatikainen, National Institute for Health and Welfare
Topics: Geographic Information Science and Systems, Geography and Urban Health, Spatial Analysis & Modeling
Keywords: geospatial health, spatial analysis,spatial economics, operations research, GPS, GISicience, geoinformatics, spatial statistics, computational sciences, health sciences
Session Type: Paper
Day: 4/5/2019
Start / End Time: 3:05 PM / 4:45 PM
Room: Marshall South, Marriott, Mezzanine Level
Presentation File: No File Uploaded

Electronic patient registers and the availability of various geospatial data services with datasets in various resolutions are momentous for health research, planning and policy-making enabling spatially more cost-efficient service provision and geospatially tailored cost-efficient treatment. The paper recognizes the contributions of spatial analysis, spatial economics and operations research to have been the theoretically fruitful premise of health research and planning in the cost-efficient allocation of scarce healthcare resources over geographical space. Now 311 Finnish municipalities are independently responsible for organizing health and social services. This responsibility has planned to be transferred to 18 new regions in connection with the health and social services reform on 1st January 2021 on. The paper shows two empirical cases about the modeling to produce more tailored and cost-efficient solutions for public healthcare in the region. The first case shows the optimal solution to the derivation of the market areas of two alternative drugs for anticoagulation therapies. The solution of market areas minimizes the out-of-pocket costs of patients by modeling the health data of patients, geocoded home location, Digiroad data and socioeconomic zip code data. The second case study based on regional patient data lays foundations how the sets of related diseases could together be more cost effectively treated and medicated. These studies come from an on-going research project for developing improved knowledge base and service optimization to support health and social services reform in Finland (STN/IMPRO).

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