Elissen Arianne Mathilda Josephus, Verhoeven Gertjan Sebastiaan, de Korte Maud Hortense, van den Bulck Anne Odilia Emile, Metzelthin Silke Friederike, van der Weij Lieuwe Christiaan, Stam Jaap, Ruwaard Dirk, Mikkers Misja Chiljon
Department of Health Services Research, CAPHRI Care and Public Health Research Institute, Maastricht University, Maastricht, The Netherlands
Dutch Healthcare Authority, Utrecht, The Netherlands.
BMJ Open. 2020 Feb 17;10(2):e035683. doi: 10.1136/bmjopen-2019-035683.
Compared with fee-for-service systems, prospective payment based on casemix classification is thought to promote more efficient, needs-based care provision. We aim to develop a casemix classification to predict the costs of home care in the Netherlands.
The research is designed as a multicentre, cross-sectional cohort study using quantitative methods to identify the relative cost predictors of home care and combine these into a casemix classification, based on individual episodes of care. The dependent variable in the analyses is the cost of home care utilisation, which is operationalised through various measures of formal and informal care, weighted by the relative wage rates of staff categories. As independent variables, we will use data from a recently developed Casemix Short-Form questionnaire, combined with client information from participating home care providers' (nursing) classification systems and data on demographics and care category (ie, a classification mandated by health insurers). Cost predictors are identified using random forest variable importance measures, and then used to build regression tree models. The casemix classification will consist of the leaves of the (pruned) regression tree. Internal validation is addressed by using cross-validation at various stages of the modelling pathways. The Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis statement was used to prepare this study protocol.
The study was classified by an accredited Medical Research Ethics Committee as not subject to the Dutch Medical Research Involving Human Subjects Act. Findings are expected in 2020 and will serve as input for the development of a new payment system for home care in the Netherlands, to be implemented at the discretion of the Dutch Ministry of Health, Welfare and Sports. The results will also be published in peer-reviewed publications and policy briefs, and presented at (inter)national conferences.
与按服务收费系统相比，基于病例组合分类的前瞻性支付被认为能促进更高效、基于需求的护理服务提供。我们旨在开发一种病例组合分类方法，以预测荷兰家庭护理的成本。
本研究设计为一项多中心横断面队列研究，采用定量方法确定家庭护理的相对成本预测因素，并将这些因素整合为基于个体护理事件的病例组合分类。分析中的因变量是家庭护理利用成本，通过正式和非正式护理的各种衡量指标进行操作化，并根据工作人员类别的相对工资率进行加权。作为自变量，我们将使用最近开发的病例组合简表问卷中的数据，结合参与家庭护理提供者（护理）分类系统的客户信息以及人口统计学和护理类别数据（即健康保险公司规定的分类）。使用随机森林变量重要性度量来识别成本预测因素，然后用于构建回归树模型。病例组合分类将由（修剪后的）回归树的叶节点组成。在建模路径的各个阶段使用交叉验证来进行内部验证。本研究方案采用了个体预后或诊断多变量预测模型的透明报告。
经认可的医学研究伦理委员会将该研究分类为不受荷兰涉及人类受试者的医学研究法案约束。预计2020年得出研究结果，并将作为荷兰家庭护理新支付系统开发的输入，由荷兰卫生、福利和体育部酌情实施。研究结果还将发表在同行评审的出版物和政策简报中，并在（国际）会议上展示。
