Team Size and Composition in Home Healthcare: Quantitative Insights and Six Model-Based Principles.

The aim of this constructive study was to develop model-based principles to provide guidance to managers and policy makers when making decisions about team size and composition in the context of home healthcare. Six model-based principles were developed based on extensive data analysis and in close interaction with practice. In particular, the principles involve insights in capacity planning, travel time, available effective capacity, contract types, and team manageability. The principles are formalized in terms of elementary mathematical models that capture the essence of decision-making. Numerical results based on real-life scenarios reveal that efficiency improves with team size, albeit more prominently for smaller teams due to diminishing returns. Moreover, it is demonstrated that the complexity of managing and coordinating a team becomes increasingly more difficult as team size grows. An estimate for travel time is provided given the size and territory of a team, as well as an upper bound for the fraction of full-time contracts, if split shifts are to be avoided. Overall, it can be concluded that an ideally sized team should serve (at least) around a few hundreds care hours per week.

A three-step framework for capacity planning in a nursing home context.

This paper presents a three-step conceptual framework that can be used to structure the care-related capacity planning process in a nursing home context. The proposed framework provides a sound practical vehicle to organise client-centred care without overstretching available capacity. Within this framework, an MILP for shift scheduling and a Genetic Algorithm (GA) for task-scheduling are proposed. To investigate the performance of the proposed framework, it is benchmarked against the current situation. The results show that considerable improvements can be achieved in terms of efficiency and waiting time. More specifically, it is shown that very modest waiting times can be achieved without exceeding available capacity, despite the fluctuations in care demand across the day.

The Inflow, Throughput and Outflow of COVID-19 Patients in Dutch Hospitals: Experiences from Experts and Middle Managers.

At the beginning of 2020, the large and unforeseen inflow of COVID-19 patients had a deep impact on the healthcare operations of Dutch hospitals. From a patient flow logistics perspective, each hospital handled the situation largely in its own particular and improvised way. Nevertheless, some hospitals appeared to be more effective in their dealing with this sudden demand for extra care than others. This prompted a study into the factors which hindered and facilitated effective operations during this period. We provide an overview of actions and measures for organizing and managing the inflow, throughput and outflow of COVID-19 patients within Dutch hospitals from various types of departments in a large number of hospitals in The Netherlands, based on interviews with nine experts and twelve hospital managers. Ten actions or measures have been identified, which have been divided into the following three dimensions: Streamlining of the underlying in- and external processes, reducing unnecessary or undesirable inflow of patients and increasing or making more adequate use of the available (human) capacity. The main lessons learned are the importance of integral tuning in the care process, giving up habits and self-interest, good information provision and the middle manager as a linking pin.

A Data-Driven Digital Application to Enhance the Capacity Planning of the COVID-19 Vaccination Process.

In this paper, a decision support system (DSS) is presented that focuses on the capacity planning of the COVID-19 vaccination process in the Netherlands. With the Dutch national vaccination priority list as the starting point, the DSS aims to minimize the per-class waiting-time with respect to (1) the locations of the medical hubs (i.e., the vaccination locations) and (2) the distribution of the available vaccines and healthcare professionals (over time). As the user is given the freedom to experiment with different starting positions and strategies, the DSS is ideally suited for providing support in the dynamic environment of the COVID-19 vaccination process. In addition to the DSS, a mathematical model to support the assignment of inhabitants to medical hubs is presented. This model has been satisfactorily implemented in practice in close collaboration with the Dutch Municipal and Regional Health Service (GGD GHOR Nederland).

Keeping pace with the ebbs and flows in daily nursing home operations.

Nursing homes are challenged to develop staffing strategies that enable them to efficiently meet the healthcare demand of their residents. In this study, we investigate how demand for care and support fluctuates over time and during the course of a day, using demand data from three independent nursing home departments of a single Dutch nursing home. This demand data is used as input for an optimization model that provides optimal staffing patterns across the day. For the optimization we use a Lindley-type equation and techniques from stochastic optimization to formulate a Mixed-Integer Linear Programming (MILP) model. The impact of both the current and proposed staffing patterns, in terms of waiting time and service level, are investigated. The results show substantial improvements for all three departments both in terms of average waiting time as well as in 15 minutes service level. Especially waiting during rush hours is significantly reduced, whereas there is only a slight increase in waiting time during non-rush hours.

Care on demand in nursing homes: a queueing theoretic approach.

Nursing homes face ever-tightening healthcare budgets and are searching for ways to increase the efficiency of their healthcare processes without losing sight of the needs of their residents. Optimizing the allocation of care workers plays a key role in this search as care workers are responsible for the daily care of the residents and account for a significant proportion of the total labor expenses. In practice, the lack of reliable data makes it difficult for nursing home managers to make informed staffing decisions. The focus of this study lies on the 'care on demand' process in a Belgian nursing home. Based on the analysis of real-life 'call button' data, a queueing model is presented which can be used by nursing home managers to determine the number of care workers required to meet a specific service level. Based on numerical experiments an 80/10 service level is proposed for this nursing home, meaning that at least 80 percent of the clients should receive care within 10 minutes after a call button request. To the best of our knowledge, this is the first attempt to develop a quantitative model for the 'care on demand' process in a nursing home.