A GIS-Based Decision Support System for Locating Primary Care Facilities

Melanie Reuter-Oppermann; Daniel Rockemann; Jost Steinhäuser

doi:10.1007/978-3-319-56925-3_17

A GIS-Based Decision Support System for Locating Primary Care Facilities

Melanie Reuter-Oppermann^*, Daniel Rockemann, Jost Steinhäuser

^*Corresponding author for this work

Institute of Family Medicine

University of Kiel

Abstract

Keeping up a high level of primary care services for a whole country or even a federal state is a very challenging task due to the demographic change and many other reasons, also for Germany. In the future it is expected that even more general practitioners (GP) are necessary to cover close to come healthcare. Therefore, an efficient use of resources and an optimized planning is crucial. Mathematical models and approaches can help facing the challenge by determining optimal locations for practices, shift schedules or appointment strategies, for example. To use these in practice, decision support systems (DSS) are necessary that link the input data to the approaches and display the results. The outline of such a decision support system for optimally locating GP practices is presented in this paper.

Original language	English
Title of host publication	IESS 2017: Exploring Services Science
Number of pages	13
Publisher	Springer Verlag
Publication date	2017
Pages	210-222
ISBN (Print)	978-3-319-56924-6
ISBN (Electronic)	978-3-319-56925-3
DOIs	https://doi.org/10.1007/978-3-319-56925-3_17
Publication status	Published - 2017
Event	8th International Conference on Exploring Service Science - Rome, Italy Duration: 24.05.2017 → 26.05.2017 Conference number: 190679

Research Areas and Centers

Research Area: Center for Population Medicine and Public Health (ZBV)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/978-3-319-56925-3_17

Cite this

@inproceedings{e90f2142e7d24a4c95269312129e8a97,

title = "A GIS-Based Decision Support System for Locating Primary Care Facilities",

abstract = "Keeping up a high level of primary care services for a whole country or even a federal state is a very challenging task due to the demographic change and many other reasons, also for Germany. In the future it is expected that even more general practitioners (GP) are necessary to cover close to come healthcare. Therefore, an efficient use of resources and an optimized planning is crucial. Mathematical models and approaches can help facing the challenge by determining optimal locations for practices, shift schedules or appointment strategies, for example. To use these in practice, decision support systems (DSS) are necessary that link the input data to the approaches and display the results. The outline of such a decision support system for optimally locating GP practices is presented in this paper.",

author = "Melanie Reuter-Oppermann and Daniel Rockemann and Jost Steinh{\"a}user",

year = "2017",

doi = "10.1007/978-3-319-56925-3_17",

language = "English",

isbn = "978-3-319-56924-6",

series = "Lecture Notes in Business Information Processing",

publisher = "Springer Verlag",

pages = "210--222",

booktitle = "IESS 2017: Exploring Services Science",

note = "8th International Conference on Exploring Service Science, IESS 2017 ; Conference date: 24-05-2017 Through 26-05-2017",

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Reuter-Oppermann, M, Rockemann, D & Steinhäuser, J 2017, A GIS-Based Decision Support System for Locating Primary Care Facilities. in IESS 2017: Exploring Services Science . Lecture Notes in Business Information Processing, Springer Verlag, pp. 210-222, 8th International Conference on Exploring Service Science, Rome, Italy, 24.05.17. https://doi.org/10.1007/978-3-319-56925-3_17

A GIS-Based Decision Support System for Locating Primary Care Facilities. / Reuter-Oppermann, Melanie; Rockemann, Daniel; Steinhäuser, Jost.
IESS 2017: Exploring Services Science . Springer Verlag, 2017. p. 210-222 (Lecture Notes in Business Information Processing).

Research output: Chapters in Books/Reports/Conference Proceedings › Conference contribution › peer-review

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N2 - Keeping up a high level of primary care services for a whole country or even a federal state is a very challenging task due to the demographic change and many other reasons, also for Germany. In the future it is expected that even more general practitioners (GP) are necessary to cover close to come healthcare. Therefore, an efficient use of resources and an optimized planning is crucial. Mathematical models and approaches can help facing the challenge by determining optimal locations for practices, shift schedules or appointment strategies, for example. To use these in practice, decision support systems (DSS) are necessary that link the input data to the approaches and display the results. The outline of such a decision support system for optimally locating GP practices is presented in this paper.

AB - Keeping up a high level of primary care services for a whole country or even a federal state is a very challenging task due to the demographic change and many other reasons, also for Germany. In the future it is expected that even more general practitioners (GP) are necessary to cover close to come healthcare. Therefore, an efficient use of resources and an optimized planning is crucial. Mathematical models and approaches can help facing the challenge by determining optimal locations for practices, shift schedules or appointment strategies, for example. To use these in practice, decision support systems (DSS) are necessary that link the input data to the approaches and display the results. The outline of such a decision support system for optimally locating GP practices is presented in this paper.

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DO - 10.1007/978-3-319-56925-3_17

M3 - Conference contribution

SN - 978-3-319-56924-6

T3 - Lecture Notes in Business Information Processing

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BT - IESS 2017: Exploring Services Science

PB - Springer Verlag

T2 - 8th International Conference on Exploring Service Science

Y2 - 24 May 2017 through 26 May 2017

ER -

A GIS-Based Decision Support System for Locating Primary Care Facilities

Abstract

Research Areas and Centers

UN SDGs

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