In France and elsewhere, decision-makers, healthcare professionals and health planners need to better understand and specify the provision of medical care. To this end, a hospital-based research project on a gravitational health planning modelling process was initiated in 2002. Since then, geomatics has emerged as a major scientific field for facing new challenges in medical informatics and health planning, thanks to the use of attractive interfaces, new methods and user-friendly IT technologies. Our initial 2002 model has recently been enhanced, optimized and automated as part of a spatial decision support system (PoleSat_2018). These decisive improvements and optimizations were mainly based on Delaunay triangulation, the replacement of human expertise with a heuristic dominance rule that provides a complete automated algorithm, and an online graphical user interface. Rapid, easy planning scenarios (by grouping and/or closing hospitals) give a quasi-instantaneous, strategic visualization of hospital catchment areas for decision-makers who are not experts in geomatics via ready-to-use maps and spreadsheets. This new implementation achieves our main objective, since the proposed deterministic method provides a completely automated, stable algorithm. A custom version of this tool is now being used by the French Ministry of Health for real planning issues. Consequently, PoleSat could be easily generalized as a prospective, strategic decision support tool for various health planning issues.

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Faced with the imminent opening of the new competing “Lens/Bois-Bernard” care unit in the Nord - Pas-de-Calais region (4 million inhabitants), a prospective modeling study was carried out in 1999 in response to a request from the Lille University Hospital (LUH), which sought to estimate the positive or negative impact on its recruitment catchment areas and consequently on its 1996 cardiac surgery activity (comprising 2 care units, i.e., 84 beds). In 1996, only 3 care units with the “Clinique du Bois” in Lille showed our regional undersupply of cardiac surgery compared to the national average of 5.96 [1,2].