Testing of the Content Validity of a Modified OPCq Instrument—A Pilot Study in Norwegian Home Health Care

Aim: To test the content validity of a modified Oulu Patient Classification instrument (OPCq), part of the RAFAELA Nursing Intensity and Staffing system in home health care (HHC) in Norway. Background: Due to the growing number of patients in HHC, a Patient Classification System (PCS) whereby the systematic registration of patients’ care needs, nursing intensity (NI) and the allocation of nursing staff can occur is needed. The validity and reliability of the OPCq instrument have been tested with good outcomes in hospital settings, but only once in an HHC setting. In this study, the OPCq is tested for the first time in HHC in Norway. Methods: A pilot study with a descriptive design. The data were collected through a questionnaire (n = 44). Both qualitative and quantitative analyses were used. Results: The OPCq fulfills the requirements for validity in HHC, but the manual may need some minor adjustments. Discussion: The OPCq seems to be useful for measuring nursing intensity in HHC. Staff training and guidance, high-quality technological solutions and that all technology works satisfactorily are important when implementing a new PCS. Further research is needed in regard to NI and the optimal allocation of nursing staff in an HHC setting.

A Multi-Objective Scheduling and Routing Problem for Home Health Care Services via Brain Storm Optimization

At present,home health care(HHC)has been accepted as an effective method for handling the healthcare problems of the elderly.The HHC scheduling and routing problem(HHCSRP)attracts wide concentration from academia and industrial communities.This work proposes an HHCSRP considering several care centers,where a group of customers(i.e.,patients and the elderly)require being assigned to care centers.Then,various kinds of services are provided by caregivers for customers in different regions.By considering the skill matching,customers’appointment time,and caregivers’workload balancing,this article formulates an optimization model with multiple objectives to achieve minimal service cost and minimal delay cost.To handle it,we then introduce a brain storm optimization method with particular multi-objective search mechanisms(MOBSO)via combining with the features of the investigated HHCSRP.Moreover,we perform experiments to test the effectiveness of the designed method.Via comparing the MOBSO with two excellent optimizers,the results confirm that the developed method has significant superiority in addressing the considered HHCSRP.

Stochastic Bi-level Programming Model for Home Healthcare Scheduling Problems Considering the Degree of Satisfaction with Visit Time

Home health care(HHC)includes a wide range of healthcare services that are performed in customers'homes to help them recover.With the constantly increasing demand for health care,HHC policymakers are eager to address routing and scheduling problems from the perspective of optimization.In this paper,a bi-level programming model for HHC routing and scheduling problems with stochastic travel times is proposed,in which the degree of satisfaction with the visit time is simultaneously considered.The upper-level model is formulated for customer assignment with the aim of minimizing the total operating cost,and the lower-level model is formulated as a routing problem to maximize the degree of satisfaction with the visit time.Consistent with Stackelberg game decision-making,the trade-off relationship between these two objectives can be achieved spontaneously so as to reach an equilibrium state.A three-stage hybrid algorithm combining an iterated local search framework,which uses a large neighborhood search procedure as a sub-heuristic,a set-partitioning model,and a post-optimization method is developed to solve the proposed model.Numerical experiments on a set of instances including 10 to 100 customers verify the effectiveness of the proposed model and algorithm.