Faculty of Health, Medicine and Life Sciences, Department of Health Services Research, Maastricht University, Care and Public Health Research Institute (CAPHRI), P.O. Box 616, 6200 MD, Maastricht, The Netherlands.
Department of Economics, Tilburg University, P.O. Box 90153, 5037 AB, Tilburg, The Netherlands.
BMC Health Serv Res. 2022 Mar 25;22(1):394. doi: 10.1186/s12913-022-07733-9.
Case-mix based prospective payment of homecare is being implemented in several countries to work towards more efficient and client-centred homecare. However, existing models can only explain a limited part of variance in homecare use, due to their reliance on health- and function-related client data. It is unclear which predictors could improve predictive power of existing case-mix models. The aim of this study was therefore to identify relevant predictors of homecare use by utilizing the expertise of district nurses and health insurers.
We conducted a two-round Delphi-study according to the RAND/UCLA Appropriateness Method. In the first round, participants assessed the relevance of eleven client characteristics that are commonly included in existing case-mix models for predicting homecare use, using a 9-Point Likert scale. Furthermore, participants were also allowed to suggest missing characteristics that they considered relevant. These items were grouped and a selection of the most relevant items was made. In the second round, after an expert panel meeting, participants re-assessed relevance of pre-existing characteristics that were assessed uncertain and of eleven suggested client characteristics. In both rounds, median and inter-quartile ranges were calculated to determine relevance.
Twenty-two participants (16 district nurses and 6 insurers) suggested 53 unique client characteristics (grouped from 142 characteristics initially). In the second round, relevance of the client characteristics was assessed by 12 nurses and 5 health insurers. Of a total of 22 characteristics, 10 client characteristics were assessed as being relevant and 12 as uncertain. None was found irrelevant for predicting homecare use. Most of the client characteristics from the category 'Daily functioning' were assessed as uncertain. Client characteristics in other categories - i.e. 'Physical health status', 'Mental health status and behaviour', 'Health literacy', 'Social environment and network', and 'Other' - were more frequently considered relevant.
According to district nurses and health insurers, homecare use could be predicted better by including other more holistic predictors in case-mix classification, such as on mental functioning and social network. The challenge remains, however, to operationalize the new characteristics and keep stakeholders on board when developing and implementing case-mix classification for homecare prospective payment.
基于病例组合的家庭护理预付款制度正在几个国家实施，以实现更高效和以客户为中心的家庭护理。然而，由于依赖于健康和功能相关的客户数据，现有的模式只能解释家庭护理使用的有限部分差异。目前尚不清楚哪些预测因子可以提高现有病例组合模型的预测能力。因此，本研究的目的是利用地区护士和健康保险公司的专业知识，确定家庭护理使用的相关预测因子。
我们按照 RAND/UCLA 适宜性方法进行了两轮德尔菲研究。在第一轮中，参与者使用 9 分李克特量表评估了十一项常见于现有病例组合模型以预测家庭护理使用的客户特征的相关性。此外，参与者还被允许提出他们认为相关的缺失特征。这些项目被分组，选择了最相关的项目。在第二轮中，在专家小组会议之后，参与者重新评估了评估不确定的预先存在的特征和十一项建议的客户特征的相关性。在两轮中，都计算了中位数和四分位距来确定相关性。
22 名参与者（16 名地区护士和 6 名保险公司）提出了 53 项独特的客户特征（最初从 142 项特征中分组）。在第二轮中，12 名护士和 5 名健康保险公司评估了客户特征的相关性。在总共 22 项特征中，有 10 项客户特征被评估为相关，12 项特征被评估为不确定。没有一项被认为对预测家庭护理使用无关紧要。“日常功能”类别的大多数客户特征被评估为不确定。其他类别-即“身体健康状况”、“心理健康状况和行为”、“健康素养”、“社会环境和网络”以及“其他”的客户特征更频繁地被认为是相关的。
根据地区护士和健康保险公司的说法，通过在病例组合分类中纳入其他更全面的预测因子，如心理功能和社会网络，可以更好地预测家庭护理的使用。然而，在为家庭护理预付款制定和实施病例组合分类时，仍然存在将新特征付诸实践并让利益相关者参与的挑战。
