The COVID-19 pandemic has severely affected healthcare delivery across the world. However, little is known about COVID-19’s impact on home healthcare (HHC) services. Our study aimed to: (1) describe the changes in volume and intensity of HHC services and the crisis management policies implemented; (2) understand the responses and the experiences of HHC staff and clients. We conducted an explanatory sequential mixed methods study. First, retrospective client data (N = 43,495) from four Dutch HHC organizations was analyzed. Second, four focus group interviews were conducted for the strategic, tactical, operational, and client levels of the four HHC organizations. Our results showed that both the supply of and demand for Dutch HHC decreased considerably, especially during the first wave (March–June 2020). This was due to factors such as fear of infection, anticipation of a high demand for COVID-19-related care from the hospital sector, and lack of personal protective equipment. The top-down management style initially applied made way for a more bottom-up approach in the second wave (July 2020–January 2021). Experiences vary between levels and waves. HHC organizations need more responsive protocols to prevent such radical scaling-back of HHC in future crises, and interventions to help HHC professionals cope with crisis situations.

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IntroductionCompared 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.Methods and analysisThe 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.Ethics and disseminationThe 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.