Regional health care planning: a methodology to cluster facilities using community utilization patterns
Regional health care planning: a methodology to cluster facilities using community utilization patterns
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DOI：
10.1186/1472-6963-13-333
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发表时间：
2013-08-22
影响因子：
2.8
通讯作者：
Messina, Joseph P.
中科院分区：
文献类型：
--
作者：
Delamater, Paul L.;Shortridge, Ashton M.;Messina, Joseph P.
背景资料：以社区为基础的卫生保健规划和管理需要将设施和区域单位分组到具有类似卫生保健用途的区域。有限的研究探讨了创建这些区域所使用的方法。我们提供了一种新的方法，根据患者利用模式和地理位置的相似性对设施进行聚类。我们的案例研究集中在密歇根州的医院集团，用于预测未来的住院病床需求在国家的床位需求方法的分配单位。在整个制定和发展的methods.Methods：聚类方法的科学，实用，和政治上的关注，采用了2步K-均值+病房的聚类算法组医院。聚类的最终数量是使用启发式方法选择的，该方法集成了基于聚类拟合的度量和由此产生的Hospital Groups.Results的特征：使用最近的医院利用率数据，聚类方法确定了密歇根州的33个医院组。尽管是在充满政治色彩的《需要证明书》监管环境中制定的，但我们提供了一个客观的、可复制的，和可持续发展的方法来创建医院集团。由于该方法是建立在理论上健全的聚类分析和卫生保健服务利用的原则，它是高度可转移的应用程序和适合分组设施或区域单位。
Background: Community-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.Methods: The 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.Results: Using recent hospital utilization data, the clustering methodology identified 33 Hospital Groups in Michigan.Conclusions: Despite 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.