Robust nurse-to-patient assignment in home care services to minimize overtimes under continuity of care

Abstract

Home Care (HC) providers are complex organizations that manage a large number of patients, different categories of operators, support staff and material resources in a context affected by high variability. Hence, robust resource planning is crucial for operations in HC organizations, in order to avoid process inefficiencies, treatment delays, and low quality of service. Under continuity of care, one of the main issues in HC planning is the assignment of a reference nurse to each assisted patient, because this decision has an impact on the workload assigned to the nurse for the entire patient's length of stay. In this paper, we derive an analytical structural policy for solving the nurse-to-patient assignment problem in the HC context under continuity of care. This policy accounts for the randomness that is related to the demands from patients already assigned to nurses and to the demands from new patients who need assignments. The policy is compared to other previously developed approaches, and applied to a relevant real case.

Planning the Delivery of Home-Based Long-Term Care: A Mathematical Programming-Based Tool to Support Routes’ Planning

“…Still, most literature in the area is based on deterministic models that ignore the impact of uncertainty on planning decisions (Fikar and Hirsch (2017)); Lanzarone and Matta (2014) notice that the variability in patients' demand is neglected in the literature and propose an exact model that deals with uncertainty in demands and service times. In their solution, the authors consider continuity of care while minimizing overtimes incurred by caregivers, and present an alternative policy for patient assignment that has been shown to reduce overtime and increase workload balance.…”

Location Planning Problem of Service Centers for Sustainable Home Healthcare: Evidence from the Empirical Analysis of Shanghai

“…Many scholars have conducted research on the application of home healthcare [13][14][15]. In addition, in recent years, the sustainability of healthcare has also attracted increasing attention from researchers and policy makers [16][17][18].…”

A Bayesian framework for describing and predicting the stochastic demand of home care patients

“…This provider operates in the north of Italy, covering a region of about 800 km 2 , with about 1000 patients assisted at the same time [2,3]. Moreover, the human resource organization and the patient classification adopted by this provider can be considered general and common to several other HC providers as underlined by Matta et al [1], so that it is representative of a general class of providers.…”

“…Appropriate resource planning is thus fundamental for avoiding process inefficiencies, delays, and overloaded operators. In addition, many random events may affect the service delivery and mine the feasibility of plans; see [1,2,3,4]. As mentioned above, the most relevant randomness sources are patients' health conditions, which may determine a different demand for visits than the planned one, as reported in [5].…”