van den Bulck Anne O E, Metzelthin Silke F, Elissen Arianne M J, Stadlander Marianne C, Stam Jaap E, Wallinga Gia, Ruwaard Dirk
Faculty of Health, Medicine and Life Sciences, Care and Public Health Research Institute (CAPHRI), Department of Health Services Research, Maastricht University, Maastricht, The Netherlands.
Dutch Healthcare Authority (NZa), Utrecht, The Netherlands.
Health Soc Care Community. 2019 Jan;27(1):93-104. doi: 10.1111/hsc.12611. Epub 2018 Jul 19.
Fee-for-service, funding care on an hourly rate basis, creates an incentive for home-care providers to deliver high amounts of care. Under casemix funding, in contrast, clients are allocated-based on their characteristics-to homogenous, hierarchical groups, which are subsequently funded to promote more effective and efficient care. The first step in developing a casemix model is to understand which client characteristics are potential predictors of home-care needs. Nurses working in home care (i.e. home-care nurses) have a good insight into clients' home-care needs. This study was conducted in co-operation with the Dutch Nurses' Association and the Dutch Healthcare Authority. Based on international literature, 35 client characteristics were identified as potential predictors of home-care needs. In an online survey (May, 2017), Dutch home-care nurses were asked to score these characteristics on relevance, using a 9-point Likert scale. They were subsequently asked to identify the top five client characteristics. Data were analysed using descriptive statistics. The survey was completed by 1,007 home-care nurses. Consensus on relevance was achieved for 15 client characteristics, with "terminal phase" being scored most relevant, and "sex" being scored as the least relevant. Relevance of the remaining 20 characteristics was uncertain. Additionally, based on the ranking, "ADL functioning" was ranked as most relevant. According to home-care nurses, both biomedical and psychosocial client characteristics need to be taken into account when predicting home-care needs. Collaboration between clinical practice, policy development, and science is necessary to realise a funding model, to work towards the Triple Aim (improved health, better care experience, and lower costs).
按服务收费，即按小时费率为护理提供资金，这促使家庭护理提供者提供大量护理。相比之下，在病例组合资金模式下，客户根据其特征被分配到同质的、分层的组中，随后这些组获得资金以促进更有效和高效的护理。开发病例组合模型的第一步是了解哪些客户特征是家庭护理需求的潜在预测因素。从事家庭护理工作的护士（即家庭护理护士）对客户的家庭护理需求有很好的洞察力。本研究是与荷兰护士协会和荷兰医疗保健管理局合作进行的。基于国际文献，确定了35个客户特征作为家庭护理需求的潜在预测因素。在一项在线调查（2017年5月）中，荷兰家庭护理护士被要求使用9点李克特量表对这些特征的相关性进行评分。随后，他们被要求确定最重要的五个客户特征。使用描述性统计方法对数据进行分析。1007名家庭护理护士完成了调查。15个客户特征在相关性上达成了共识，其中“终末期”被评为最相关，“性别”被评为最不相关。其余20个特征的相关性尚不确定。此外，根据排名，“日常生活活动功能”被列为最相关。家庭护理护士认为，在预测家庭护理需求时，生物医学和心理社会客户特征都需要考虑。临床实践、政策制定和科学之间的合作对于实现一种资金模式、朝着“三重目标”（改善健康、提供更好的护理体验和降低成本）努力是必要的。
