Home Health Care Companies Research Articles

A mixed integer linear programming model for quarantine-based home healthcare scheduling under uncertainty

Home healthcare companies (HHC) have emerged as vital alternatives to traditional hospitals, particularly in meeting the healthcare needs of individuals within the comfort of their homes. The COVID-19 pandemic has amplified the significance of HHC services, offering a crucial alternative for patients and the elderly to follow quarantine protocols while receiving essential healthcare at home. Consequently, HHC companies must align their planning strategies with the World Health Organization (WHO) health guidelines. This research introduces a Mixed Integer Linear Programming (MILP) model tailored for home healthcare services during COVID-19, aiming to ensure strict adherence to quarantine protocols while enhancing service efficiency and quality. The proposed vehicle routing problem with pickup/delivery and time window formulation incorporates critical elements such as patient and caregiver classification, work and break regulations adherence, workload balancing, and multi-depot capabilities. The model addresses uncertain demand and service times through a stochastic programming approach to enhance practicality. K-means clustering is applied to streamline scenarios, with a sensitivity analysis determining the optimal number of clusters. Additionally, measures intrinsic to stochastic programming, such as the Expected Value of Perfect Information (EVPI) and Value of Stochastic Solution (VSS), are computed for comprehensive analysis.

A Bi-Objective Home Health Care Routing and Scheduling Problem under Uncertainty.

Home health care companies provide health care services to patients in their homes. Due to increasing demand, the provision of home health care services requires effective management of operational costs while satisfying both patients and caregivers. In practice, uncertain service times might lead to considerable delays that adversely affect service quality. To this end, this paper proposes a new bi-objective optimization problem to model the routing and scheduling problems under uncertainty in home health care, considering the qualification and workload of caregivers. A mixed-integer linear programming formulation is developed. Motivated by the challenge of computational time, we propose the Adaptive Large Neighborhood Search embedded in an Enhanced Multi-Directional Local Search framework (ALNS-EMDLS). A stochastic ALNS-EMDLS is introduced to handle uncertain service times for patients. Three kinds of metrics for evaluating the Pareto fronts highlight the efficiency of our proposed method. The sensitivity analysis validates the robustness of the proposed model and method. Finally, we apply the method to a real-life case and provide managerial recommendations.

An advanced hyperheuristic approach for the home health care scheduling problem with time window in deterministic and uncertain environments

In the last two decades, Home Health Care (HHC) services have garnered substantial attention from global researchers and practitioners. These services face significant challenges related to strategic, tactical, and operational decisions that must be solved for enhancing their efficiency and quality. Notably, researchers have focused significantly on the scheduling and routing aspects, recognizing their potential to enhance patient satisfaction, safety, and cost-effectiveness for both patients and HHC companies. Recent studies have highlighted the stochastic nature of these services, including random service and travel times, that may affect the scheduling and routing components. Nonetheless, a significant portion of recent research predominantly focuses on the deterministic version of the problem, disregarding its fundamental stochastic nature as emphasized by feedback from HHC companies. Within this context, we propose a two-stage mixed integer mathematical formulation to address the Home Health Care Scheduling and Routing Problem (HHCSRP), encompassing stochastic demand, service, and travel times. Given the inherent Nondeterministic Polynomial (NP)-hardness of these scheduling and routing problems, we introduce a hyperheuristic approach based on a TS metaheuristic and Variable Neighborhood Search (VNS) heuristic. Extensive experiments validate the effectiveness of our proposed modeling and solving approaches, outperforming similar methods across various metrics. Notably, our stochastic model exhibits superior performance in terms of parameters like the number of patients served within time windows and computational time required to achieve optimal solutions. This study thus contributes a valuable decision support system for Home Health Care companies, empowering them to make informed decisions and construct robust schedules for their caregivers.

A multi-objective home healthcare routing problem

ABSTRACT As demand for home health care (HHC) services surges due to an aging population and public health constraints, HHC companies need to optimize their activities. Increasing costs to deliver care and the need to optimize the capacity of acute care settings such as hospitals, make the planning of HHC services more challenging. To optimally deliver care, the caregivers’ activities must be planned considering conflicting objectives. The proposed multi-objective HHC problem considers three components in the planning process: patients, nurses, and providers. An exact method is presented to efficiently solve a multi-objective HHC routing problem balancing the schedules of the nurses while maximizing quality of service (time windows, continuity, and consistency of care) and provider’s profit. A mixed-integer linear programming (MILP) model is formulated using the ϵ-constraint method and small- and medium-size problems are solved. Computational results on modified benchmark instances from the literature are used to test the method’s efficiency. A Pareto Frontier Analysis is used to evaluate optimality conditions for the provider, and it found that failing to satisfy the preferences of nurses has a larger impact on the provider’s bottom line. Furthermore, it was found that adjusting the patients’ preferences is not as taxing as adjusting the nurses’ preferences.

Solving a multi-period home health care routing and scheduling problem using an efficient matheuristic

This paper focuses on optimizing a multi-period home health care routing and scheduling problem (HHCRSP). The problem consists of assigning suitable caregivers to serve patients at their homes and designing a set of optimized visit routes for caregivers. Various practical constraints such as synchronized visits, lunch breaks, continuity of care, and workload balancing are concerned. There are three objectives considered in this study: the minimization of relevant operational costs, the maximization of the satisfaction of patients and the maximization of the satisfaction of caregivers. First, the target problem is formulated as a mixed-integer programming model, which can be solved directly by a commercial linear programming solver. Considering the NP-hard nature of the target problem, a matheuristic integrating a heuristic (adaptive large neighborhood search, ALNS) and a commercial solver (Gurobi) is proposed to solve large-scale instances efficiently. Two versions of the matheuristic, matheuristic1 and matheuristic2, are developed by combining ALNS and Gurobi in different ways. The experimental results highlight the excellent performance of the proposed matheuristic and the results are quite encouraging compared with those of the pure ALNS and Gurobi solver. In addition, matheuristic2 outperforms matheuristic1 in the majority of the instances. Furthermore, the sensitivities of a set of key characteristics in the model, such as the weight distribution of the objective function, prioritizing continuity of care, and departure rules for caregivers, are analyzed. This study has broad application prospects in the HHC field, and it provides home health care companies (HHCCs) an efficient approach to solve the multi-period HHCRSP and offers the managers managerial insights to achieve the objective they care about the most.

The daily routing and scheduling problem of home health care: based on costs and participants’ preference satisfaction

AbstractImproving the level of service and reducing total costs are the most important objectives for the decision support system of home health care companies. But these objectives are often in conflict. In this study, we aim to minimize the total costs and maximize the participants’ preference satisfaction simultaneously. The problem is formulated as a bi‐objective mixed‐integer linear programming model that considers the skill requirements of patients, hard time windows for the service start time, and the working duration of a nurse. Patient preference satisfaction captures the nurse skill level and nurse–patient familiarity, and nurses’ preference satisfaction captures the overtime duration. To solve this problem, a novel hybrid elitist nondominated sorting genetic algorithm (hybrid NSGA‐II) is developed by embedding a local search algorithm into the basic NSGA‐II framework. Computation results on a set of benchmarking instances show that the developed hybrid NSGA‐II can obtain approximate Pareto‐optimal solutions within a shorter computation time when compared with the ‐constraint method for small instances; it can also perform better than the basic NSGA‐II and SPEA‐II with the size increasing for middle and large instances. Using a small instance, this study also analyzes the problem properties and the trade‐off between total costs and participants’ preference satisfaction.

Hybrid metaheuristics for solving a home health care routing and scheduling problem with time windows, synchronized visits and lunch breaks

The home health care routing and scheduling problem (HHCRSP), a challenging operational problem in the field of home health care (HHC), consists of assigning suitable caregivers to serve patients at their homes and optimizing a set of caregiver’s visits according to certain criteria. Besides the time windows and qualifications of caregivers that are generally considered in HHCRSP, this study further concerns three practical constraints, which are (1): some patients require the services that should be performed by at least two caregivers simultaneously (synchronized visits); (2) caregivers should take lunch breaks when they working during the lunch period (lunch breaks); (3) caregivers can depart from either their homes or the HHC company (flexible departure modes of caregivers). In this study, the concerned problem is firstly modeled as a mixed-integer programming model, and four hybrid metaheuristics are developed. Numerical results obtained with the instances, adapted from a set of benchmark instances, as well as the statistical information, computed by Friedman test present that the hybrid genetic general variable neighborhood search (HGGVNS) shows the best performance among four algorithms. Furthermore, sensitivity analyses are conducted to evaluate the impact of synchronization scales, time window widths, break regulations, and departure strategies on the final solutions. The results of this study can offer HHC management a valuable scheme to construct a high-quality planning of HHC visits by taking into account various important real-life constraints, some of which were not yet been tackled in the literature.

Developing a mathematical model for staff routing and scheduling in home health care industries: Genetic Algorithm based solution scheme

Efficient management of providing home health care services requires many considerations. In this paper, a mathematical model for the daily staff routing and service scheduling is developed for home health care companies. In this model, both economic factors and qualitative service-oriented performance measures are simultaneously optimized. To make the model more realistic, many real situations such as considering different qualifications and diverse vehicles for staff members, different requirements and predetermined preferences for patients, possible temporal interdependencies between services, and continuity of care (CoC) are taken into account. We also added some important constraints related to blood sampling requirements, which make our proposed model more complex. The proposed model is a mixed integer linear programming model (MILP) that belongs to an NP-hard class of optimization problems. To solve such a complex mathematical model, a genetic algorithm (GA) is proposed to find near-optimal solutions. We use some randomly generated test instances with different sizes to evaluate the performance of the GA. Finally, it is demonstrated how the proposed solution scheme can end up with proper scheduling and routing policies compared to those obtained through exact methods.

The Large-Scale Periodic Home Health Care Server Assignment Problem: A Region-Partition-Based Algorithm

This article addresses the periodic home health care (HHC) server assignment problem in HHC companies. This problem is a variant of the periodic vehicle routing problem with specific constraints for customers and servers (e.g., customers’ requirements for multiple medical skills and continuous care offered by a given number of servers and servers’ requirements for workload balance). Solving large-scale problems from practical applications of HHC companies within an acceptable computation time is challenging. Consequently, we develop an efficient region-partition-based algorithm to solve these large-scale problems. First, the algorithms assign customers and servers to many independent regions. Second, four different tabu search (TS) algorithms are designed to solve the optimization problem of each region. Finally, the algorithms iteratively adjust the assignment of customers and servers to regions and solve the problems of each region. The performances of different TS algorithms are discussed. The effectiveness of the proposed region-partition-based algorithm for large-scale problems is validated. Note to Practitioners —This article is motivated by our collaboration with an HHC company in Shanghai, China. The company delivers medical, paramedical, and social services to customers in their homes to help them improve their clinical and psychological conditions without hospital stays. In practice, customers require long-term and periodic service (e.g., weekly), which may last many months or even years. HHC companies have to assign a combination of service days and at least one server to each customer. Due to specific constraints in practical operations and the large scale of the practical problems (e.g., 1000–2000 customers) of the HHC in this article, the server assignment problem is challenging to the HHC company. The algorithm presented in this article takes into account the special constraints of the HHC and is able to solve the large-scale problem in a reasonable time span. The algorithm can be used to help decision-makers assign combinations of service days and servers to customers with the objective of balancing the workloads among servers.

A Green Routing and Scheduling Problem in Home Health Care

The growing concern about the influences of anthropogenic pollutions has forced researchers and scholars to study the environmental concerns. This paper addresses a green routing and scheduling problem in home health care (HHC) with the constraints of synchronized visits and carbon emissions. In this work, the objective is to design a reasonable logistics route meanwhile reduce the effect on the environment for the HHC company. The formulated mixed-integer programming (MIP) model is solved for a set of small scale instances using Gurobi solver with a time limit of 1 hour. An efficient two-phase heuristic approach through decomposing the studied problem into a routing problem and a speed optimization problem is proposed. The heuristic approach is based on two exact methods using Gurobi solver and dynamic programming (DM) method. The proposed heuristic approach is examined by a total of 19 instances with different scales. The experimental results for the studied problem highlight the effectiveness and efciency of the proposed heuristic approach.

Patient Experiences With Outpatient Parenteral Antibiotic Therapy: Results of a Patient Survey Comparing Skilled Nursing Facilities and Home Infusion.

ObjectiveThis work compares overall patient satisfaction with outpatient parenteral antibiotic therarpy (OPAT) care across the skilled nursing facility (SNF) and home healthcare company (HHC) settings; identifies barriers to patient satisfaction in OPAT; and develops a model for OPAT patient satisfaction that can help programs improve the patient experience across both sites of care.MethodWe developed and administered a patient experience survey to 100 patients returning to a single clinic for follow up. The survey consisted of 15 items (Likert scale, multiple choice, and free text responses). Patient characteristics and responses to the survey for patients who received care at home and at SNFs were analyzed and compared.ResultsOf the 100 patients surveyed, 98 completed the survey. Overall, HHC patients were satisfied more with their care than patients in SNFs, with a greater proportion stating they would recommend the site to others (71.7% for HHC and 32.7% for SNFs, P < .01). Patients in SNFs had a larger number of complaints about lapses in medical care, infection prevention, and the physical environment than HHC patients.ConclusionsPatient satisfaction in OPAT is higher for home infusion than SNFs. In order to improve the patient experience, OPAT programs need to engage stakeholders in HHCs and SNFs to improve communication and care delivery.

A robust optimization for a home health care routing and scheduling problem with consideration of uncertain travel and service times

In today’s competitive environment, one of the most critical objectives for Home Health Care (HHC) companies is to meet the demand of patients in a timely fashion. According to the feedback from HHC companies, caregivers have to deal with some uncertainties when carrying out a given schedule to visit their patients. However, a majority of the previous work only considers the deterministic models which ignore the uncertainties, and solutions obtained by these deterministic models are usually less robust in case of any possible changes in practical situations. Inspired by this point, in this work, we formulate a model for an HHC Routing and Scheduling Problem with taking into account uncertain travel and service times, from the perspective of Robust Optimization (RO). Specifically, the non-deterministic variables are defined based on the theory of budget uncertainty, and then the arrival time of each caregiver is rewritten as a complicated recursive function. After that, Gurobi Solver, Simulated Annealing, Tabu Search, and Variable Neighborhood Search are adapted to solve the model respectively. Finally, a series of experiments have been performed to validate the proposed models and algorithms. Experimental results from Monte Carlo simulation highlight the strength of considering uncertainties when modeling the problem. Additional, the influences of other characters in instances, like the width of time-window, distributed location have also been empirically analyzed. Finally, the comparison performed between the solutions obtained by the stochastic model and the RO model also demonstrates the advantage of the RO model. This work provides a valuable framework for HHC companies to make a robust schedule when arranging the caregivers.

Home Health Care Routing and Scheduling Problem Considering Temporal Dependencies and Perishability with Simultaneous Pickup and Delivery

Abstract Home health care (HHC) companies provide a wide range of medical and social services to patients in their own homes to help them rehabilitate after illness or injury. Providing high-quality services and reducing operating costs are accessible through proper planning of different sectors of HHC companies. This study presents a mathematical model integrated with real-life constraints to address a home health care routing and scheduling problem (HHCRSP). The planning takes into account temporal precedence and synchronization constraints and limited allowable times for transferring collected biological samples to the laboratory in the pickup and delivery environment. This problem is a new variant of the vehicle routing problem with time windows (VRPTW) with the aim of minimizing the cost related to the transportation and the idle time of caregivers by assigning proper caregivers with required qualifications to patients based on their disjunctive needs. Simulated annealing (SA) and tabu search (TS) are two meta-heuristics applied in two phases for each instance to schedule primary tasks in the first step, and then synchronized services in the next phase. The Solomon’s benchmark is used for generating comparative experiments.

Modeling and solving simultaneous delivery and pick-up problem with stochastic travel and service times in home health care

Home Health Care (HHC) companies are widespread in European countries and aim to serve patients at home to help them recover from illness and injury in a personal environment. Since transportation costs constitute one of the largest forms of expenditure in the HHC industry, it is of importance to research the optimization of the HHC logistics. This paper considers an HHC routing problem with stochastic travel and service times, which comes from the logistics practice of HHC companies. A Stochastic Programming model with Recourse (SPR model) is proposed based on the specific structure of the supply chain. Three series of experiments are carried out to evaluate the model. Firstly, the SPR model is reduced to the deterministic one, and five approaches (Gurobi Solver, Hybrid Genetic Algorithm, Simulated Annealing, Bat Algorithm and Firefly Algorithm) are respectively implemented to solve the deterministic model. The SPR model is finally solved by the proposed SA-based heuristic algorithm. Evaluation of the solutions achieved by the two models highlights the advantages of considering stochastic travel and service times. Finally, a case study is given to explain how the SPR model works for an HHC company. This research will help HHC companies to make appropriate decisions when planning their vehicle routes.

A Home Health Care Routing Problem with Stochastic Travel and Service Time

Home Health Care (HHC) companies are widespread in European countries, and aim to serve patients at home to help them recover from illness and injury in a personal environment. Since transportation costs constitute one of the largest forms of expenditure in the Home Health Care industry, it is of importance to research the optimization of the Home Health Care logistics. This paper considers a Home Health Care Routing Problem with stochastic travel and service time, which comes from the logistics practice of the home health care company. A stochastic programming model with recourse (SPR) is proposed, the Hybrid Genetic Algorithm (HGA) and stochastic simulation method are integrated to solve the proposed model. Three series of experiments are carried out to evaluate the model. Firstly, the SPR model is reduced to the deterministic one, experimental results for the related instances validate the effectiveness and efficiency of the proposed HGA. Then HGA and stochastic simulation are integrated to solve the stochastic model. Comparisons between the solutions obtained by stochastic model and deterministic one validate the reasonable and robustness of considering the stochastic travel and service time. This research will help HHC companies to make appropriate decisions when arranging their vehicle routes.

A hybrid genetic algorithm for a home health care routing problem with time window and fuzzy demand

Home Health Care (HHC) companies are widespread in European countries, and aim to serve patients at home to help them recover from illness and injury in a personal environment. Since transportation costs are among the biggest sources of expenditure in company activities, it is of great significance to optimize this in the Home Health Care industry. From the perspective of optimizing the cost of transportation, this paper studies the Vehicle Routing Scheduling problem as it applies to HHC companies. According to a survey of the HHC companies, during the process of delivering medication drugs, the quantity of drugs required for each patient is non-deterministic when the company makes planned routes. This paper considers uncertain demand as a fuzzy variable, which is closer to a potential real life scenario. A Home Health Care Scheduling Problem with fuzzy demand is considered and a fuzzy chance constraint model is designed. We propose a hybrid genetic algorithm integrated with stochastic simulation methods to solve the proposed model. Firstly, the problem is reduced to the classical vehicle routing problem within a time window. Experimental results for Solomon’s and Homberger’s benchmark instances show that the proposed algorithm performs efficiently. Then other experiments on the fuzzy version model are undertaken with the variable value of the Dispatcher Preference Index (DPI) parameter between [0, 1]. Finally, the influence of DPI on the final objective and the indicators of the problem are discussed using stochastic simulation, and the best value of DPI is obtained. This research will help HHC companies to make appropriate decisions when arranging their vehicle scheduling routes.

Similar and Different: Professional Identity and the Experience of Partial Role Transitions

In this study, we sought to understand how individuals with professional identities experience opportunities for role transitions. Drawing on 47 semi- structured interviews and direct observation conducted at a home healthcare company over a 7 month period, we employed grounded theory techniques to reveal the motivations underlying the decisions to undergo partial or complete transitions into administrative roles as well as the forms of identity work that facilitated the adjustment to new roles. Nurses who underwent complete transitions into administrative roles redefined their professional identity such that it became compatible with their new role. Those who made partial transitions into administrative roles satisfied different needs as they acted out their nursing and administrative roles, acting in accordance with their professional identity in their nursing position and fulfilling separate needs while conducting administrative work. The ability of these partially transitioned professionals to satisfy...

Hybridization of tabu search with feasible and infeasible local searches for periodic home health care logistics

This paper addresses a periodic vehicle routing problem encountered in home health care (HHC) logistics. It extends the classical Periodic Vehicle Routing Problem with Time Windows (PVRPTW) to three types of demands of patients at home. Demands include transportation of drugs/medical devices between the HHC depot and patients׳ homes, delivery of special drugs from the hospital to patients, and delivery of blood samples from patients to the lab. Each patient requires a certain number of visits within a planning horizon and has a set of possible combinations of visit days. Daily routing should meet time window constraints associated with patients, the hospital and the lab. The problem consists in determining the visit days of each patient and vehicle routes for each day in order to minimize the maximal routing costs among all routes over the horizon. We propose a Tabu Search method combined with different local search schemes including both feasible and infeasible local searches. The proposed approaches are tested on a range of instances derived from existing Vehicle Routing Problem with Time Window (VRPTW) benchmarks and benchmarks on special cases of our problem. Numerical results show that local search scheme starting with an infeasible local search with a small probability followed by a feasible local search with high probability is an interesting hybridization. Experiments with field data from a HHC company show that the proposed approach reduces the total cost and better balances the workloads of vehicles.

Heuristic approaches for a special simultaneous pickup and delivery problem with time windows in home health care industry

This paper addresses a problem encountered in home health care logistics in France. The home health care company must complete pickup and delivery tasks to satisfy the demands of clients (the patients receive home health care). The requirements can be partitioned into four types: delivering medicines from the company's depot or hospital to patients' home, and picking up materials from patients' home to the lab or return to the company's depot. Each patient may require various pickup services and various delivery services, simultaneously. Meanwhile, there are time window constraints for lab, hospital and each patient. The objective is to determine a set of vehicle routes with minimal costs, satisfying all the constraints. We build two mathematical formulations for this complex problem, and propose heuristic approaches to solve this problem. Computational experiments on some test instances are presented.

Employment Screening: Risk Management Tool and Essential Compliance Utility

Home health care companies are under pressure to quickly hire the best people to care for a growing and aging US population. However, the litigation and regulatory environments have made hiring the right employees a mission-critical function. Employment screening, including background checks, aids risk management with proactive efforts to hire only those with suitable backgrounds to work in patients' homes. Background checks also address federal compliance mandates by ensuring that employees are properly licensed, as well as not excluded or sanctioned by any agency.