ArticleBMC health services research2013
Regional health care planning: a methodology to cluster facilities using community utilization patterns.
Article in BMC health services research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
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- Mental health service areas in Switzerland.International journal of methods in psychiatric research · 2023Article
- Algorithmic hospital catchment area estimation using label propagation.BMC health services research · 2022Article
- The Association with COPD Readmission Rate and Access to Medical Institutions in Elderly Patients.International journal of chronic obstructive pulmonary disease · 2021Article
- Automated delineation of hospital service areas as a new tool for health care planning.Health services research · 2020Article
- Quality Through Coopetition: An Empiric Approach to Measure Population Outcomes for Emergency Care-Sensitive Conditions.Annals of emergency medicine · 2018Article
- Using Weighted Hospital Service Area Networks to Explore Variation in Preventable Hospitalization.Health services research · 2018Article
- Examining the spatiotemporal evolution of vaccine refusal: nonmedical exemptions from vaccination in California, 2000-2013.BMC public health · 2018Article
- Imbalance in Cardiovascular Surgery Medical Service Use Between Regions.The Korean journal of thoracic and cardiovascular surgery · 2016Article
- Do more hospital beds lead to higher hospitalization rates? a spatial examination of Roemer's Law.PloS one · 2013Article
- Optimizing Hospital Bed Capacity and Resource Allocation Using Inflow and Outflow Indices for Effective Healthcare Management.Inquiry : a journal of medical care organization, provision and financingArticle
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Authors and funding
3 authors.
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Abstract
backgroundCommunity-based health care planning and regulation necessitates grouping facilities and areal units into regions of similar health care use. Limited research has explored the methodologies used in creating these regions. We offer a new methodology that clusters facilities based on similarities in patient utilization patterns and geographic location. Our case study focused on Hospital Groups in Michigan, the allocation units used for predicting future inpatient hospital bed demand in the state's Bed Need Methodology. The scientific, practical, and political concerns that were considered throughout the formulation and development of the methodology are detailed.
methodsThe clustering methodology employs a 2-step K-means + Ward's clustering algorithm to group hospitals. The final number of clusters is selected using a heuristic that integrates both a statistical-based measure of cluster fit and characteristics of the resulting Hospital Groups.
resultsUsing recent hospital utilization data, the clustering methodology identified 33 Hospital Groups in Michigan.
conclusionsDespite being developed within the politically charged climate of Certificate of Need regulation, we have provided an objective, replicable, and sustainable methodology to create Hospital Groups. Because the methodology is built upon theoretically sound principles of clustering analysis and health care service utilization, it is highly transferable across applications and suitable for grouping facilities or areal units.
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