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Planning of health care services by geospatial analysis to be more cost-efficient

Authors: Markku Tykkylainen*, University of Eastern Finland, Aapeli Leminen, University of Eastern Finland, Mikko Pyykönen, University of Eastern Finland, Teppo Repo, University of Eastern Finland, Maija Toivakka, Univeristy of Eastern Finland, Tiina Laatikainen, University of Eastern Finland
Topics: Spatial Analysis & Modeling, Geography and Urban Health, Planning Geography
Keywords: Geospatial Health
Session Type: Paper
Day: 4/9/2020
Start / End Time: 3:20 PM / 4:35 PM
Room: Virtual Track 1
Presentation Link: Open in New Window
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The aim of the paper is to exemplify how health care could be planned more efficiently using geocoded and small area data combined with electronic health registers (EHR). Research is based on data of EHR of a health care district in conjunction with socioeconomic and environmental statistical databases, Finnish national road and street database and geocoded information about service providers and patients and their travel modes. We analysed the quality and cost efficiency of care related to common diseases, such as type 2 diabetes, cardiovascular diseases and atrial fibrillation. The study area is the health care district of North Karelia in eastern Finland.

We have developed statistics and indicators, applied statistical and computational modeling of associations, ran predictions, outlined alternative treatments and medication, simulated self-monitoring and made scenarios, and made maps and visualizations. Results can be used in research and in disease management and monitoring in practice.

This presentation shows the results of small area analyses for prevalence and disease management of type 2 diabetes, prevalence and hot spots of cardiovascular events and care, geospatial optimization of the medication costs of atrial fibrillation by minimizing patient's costs of travel and time loss and determined the market areas of medication and care of atrial fibrillation. We demonstrate that the use of EHRs combined with geospatial individual and small-area statistical data is useful and it provides a productive research approach for disease management and monitoring to improve the quality of care and reduce costs.

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