Scheduled Care Research Articles

An Adaptive Large Neighbourhood Search algorithm for a real-world Home Care Scheduling Problem with time windows and dynamic breaks

This paper presents a Home Care Scheduling Problem (from now on HCSP) based on a real case of a care company for elderly and dependent people located in the North of Spain. The problem incorporates many of the common features addressed in the HCSP literature, such as soft and hard time windows for the services, available working time of the caregivers or affinity levels between users and caregivers. However, it also includes other novel characteristics that increase the difficulty of the problem significantly, since the breaks between services will play a key role in the quality of the solutions. To evaluate the solutions, the users welfare will be prioritized over the cost associated with the schedule.The problem has been formulated as a Mixed Integer Linear Programming (MILP) one but, due to the complexity of the model, it is not possible to solve it for real size instances. Therefore, we have designed a method that combines the Adaptive Large Neighbourhood Search (ALNS) methodology with a heuristic approach necessary to evaluate the objective functions. To analyse the behaviour of the algorithm, a set of computational experiments are carried out under different configurations. First, the MILP formulation and the algorithm have been compared over some standard instances from the literature. Finally, the performance of the algorithm is evaluated over a real case study based on the timetables of the company during some weeks from 2016 to 2017.

Frequency of Prenatal Care Visits: Protocol to Develop a Core Outcome Set for Prenatal Care Schedules.

Prenatal care, one of the most common preventive care services in the United States, endeavors to improve pregnancy outcomes through evidence-based screenings and interventions. Despite the prevalence of prenatal care and its importance to maternal and infant health, there are several debates about the best methods of prenatal care delivery, including the most appropriate schedule frequency and content of prenatal visits. Current US national guidelines recommend that low-risk individuals receive a standard schedule of 12 to 14 in-office visits, a care delivery model that has remained unchanged for almost a century. In early 2020, to mitigate individuals' exposure to the SARS-CoV-2 virus, prenatal care providers implemented new paradigms that altered the schedule frequency, interval, and modality (eg, telemedicine) of how prenatal care services were offered. In this paper, we describe the development of a core outcome set (COS) that can be used to evaluate the effect of the frequency of prenatal care schedules on maternal and infant outcomes. We will systematically review the literature to identify previously reported outcomes important to individuals who receive prenatal care and the people who care for them. Stakeholders with expertise in prenatal care delivery (ie, patients or family members, health care providers, and public health professionals and policy makers) will rate the importance of identified outcomes in a web-based survey using a 3-round Delphi process. A digital consensus meeting will be held for a group of stakeholder representatives to discuss and vote on the outcomes to include in the final COS. The Delphi survey was initiated in July 2022 with invited 71 stakeholders. A digital consensus conference was conducted on October 11, 2022. Data are currently under analysis with plans to submit them in a subsequent manuscript. More research about the optimal schedule frequency and modality for prenatal care delivery is needed. Standardizing outcomes that are measured and reported in evaluations of the recommended prenatal care schedules will assist evidence synthesis and results reported in systematic reviews and meta-analyses. Overall, this COS will expand the consistency and patient-centeredness of reported outcomes for various prenatal care delivery schedules and modalities, hopefully improving the overall efficacy of recommended care delivery for pregnant people and their families. DERR1-10.2196/43962.

A knowledge-based multi-objective evolutionary algorithm for solving home health care routing and scheduling problems with multiple centers

Nowadays, population aging is becoming more and more serious. The elderly occupies a great many of public medical resources, which brings great pressure to the public and governments. Home health care (HHC) is treated as an alternative answer to hospitalization, and plays positive roles in alleviating the stress from the shortage of medical resources incurred by the population aging. Hence, how to effectively manage and organize the operations of HHCs becomes an all-important problem in reality. HHC routing and scheduling problems attract a great deal of interest from modeling and optimization areas. Nevertheless, most of the existing studies just focus on the HHC routing and scheduling problems with a single HHC center. This study focuses on a multi-objective HHC routing and scheduling problem with multiple centers for minimizing total service cost and total tardiness incurred by delay service while meeting caregivers’ workload and resource constraints. To cope with it, we establish a mixed integer programming model to formulate the concerned problem. Then, a knowledge-based multi-objective evolutionary algorithm (KMoEA) is specially designed by employing two local search operators with the properties of the problem. Via comparing KMoEA with five algorithms and a mathematical programming solver (CPLEX), we validate that KMoEA is an effective approach for solving the studied problem.

Home health care routing and scheduling in densely populated communities considering complex human behaviours

This study focuses on the home health care routing problem (HHCRP) in the scenario of high population density areas where many elders live closely together. This study considers two main objectives. The first is to reduce travel and wait times for nurses or elders. The second concerns socially related objectives in scheduling problems, such as ‘quality of life’ and empowerment, by considering assumptions related to the acquaintanceship and mutual preferences of nurses and elders. This study models the effects of mutual preferences and acquaintanceship on service time in HHCRP. We use the Markov decision process and chance-constrained programming (CCP) to model the system to conserve the sequential service provision parameters and better represent the influence of stochastic service times. Because traditional deterministic algorithms cannot solve such a model, we apply a model-free reinforcement learning algorithm, Q-learning (QL), as well as the ant colony optimisation (ACO) algorithm. Thus, we tackle this problem by developing a model and algorithm to solve complex, large-scale systems. This study’s theoretical and practical contributions are verified by feedback from researchers and practitioners.

Does the Doctor-Patient Relationship Affect Enrollment in Clinical Research?

Recruiting patients for clinical research is challenging, especially for underrepresented populations, and may be influenced by patients' relationships with their physicians, care experiences, and engagement with care. This study sought to understand predictors of enrollment in a research study among socioeconomically diverse participants in studies of care models that promote continuity in the doctor-patient relationship. A study of the effects of vitamin D levels and supplementation on COVID-19 risk and outcomes was implemented from 2020 to 2022 within 2 studies of care models at the University of Chicago that promoted continuity of inpatient and outpatient care from the same physician. Hypothesized predictors of vitamin D study enrollment included patient-reported measures of the care experience (quality of relationship with the doctor and their staff, timely receipt of care), engagement in care (scheduling and completing outpatient visits), and engagement with these "parent" studies (follow-up survey completion). The authors used univariate tests and multivariable logistic regression to examine the association of these predictors with enrollment in the vitamin D study among participants in the parent study intervention arms. Among 773 eligible participants, 351/561 (63%) in the parent study intervention arms enrolled in the vitamin D study, versus 35/212 (17%) in the control arms. Among intervention arm participants, vitamin D study enrollment was not associated with reported quality of communication with or trust in the doctor, or helpful/respectful office staff, but was associated with report of receiving timely care, more completed clinic visits, and higher parent study follow-up survey completion. Study enrollment may be high in care models with high levels of continuity in the doctor-patient relationship. Rates of clinic involvement, parent study engagement, and experience of receiving timely access to care may better predict enrollment than quality of the doctor-patient relationship.

727 Development of an Acuity and Scheduling System for Wound Care

Abstract Introduction Acuity systems have been used in nursing to quantify the care needed for each patient based on the patient’s nursing needs. The benefits for using an acuity system include avoiding over or understaffing and improving patient outcomes. To date no formal acuity system has been used to quantify the work associated with burn wound care. The purpose of this quality assurance project was to develop an acuity and scheduling system for burn patients and provide more effective and equitable staffing for burn wound care. Methods A wound care subgroup was formed by Unit Based Council to develop a point-based acuity scale for wound care. Numerical points were assigned for each body area dressed and wound care procedures such as surgical dressing take downs, moderate sedation, staple removal, wound vac change, wraps or casting, initial debridement, and discharge teaching (Fig. 1). Points were totaled and associated with a range of time: 1-5 points equals 45 minutes; 6-10 points equals 60 minutes; 11-15 points equals 75 minutes, and 16-20 points equals 90 minutes (Fig. 2). The total time of all dressing changes for the day based on the acuity system determines the RN and Tech staffing for wound care. After considering provider and therapy priorities, a schedule is posted in Microsoft Teams allowing for interdisciplinary collaboration and visibility on wound care scheduling. Results To date the acuity-based schedule has been used to schedule 500 consecutive dressings. Acuity based scheduling has helped to quantify the number of staff needed for wound care related to the amount of time needed each day. Prior to the initiation of this practice, wound care was assigned based on the number of patients instead of the workload required per patient leading to over or understaffing. The use of the acuity system has allowed the unit to predict the number of staff needed for wound care each day and adherence to the schedule with a margin of 30 minutes plus/minus was achieved. Conclusions Use of an acuity and scheduling system for burn wound care can improve staffing and will be a tool to quantify productivity. Applicability of Research to Practice Acuity systems and wound care scheduling can improve staffing for burn centers.

Balancing the satisfaction of stakeholders in home health care coordination: a novel OptaPlanner CSP model

ABSTRACT Home Health Care Routing and Scheduling Problem (HHCRSP) has been widely investigated in operations research. In this paper, a model based on the Constraint Satisfaction Problem (CSP) is proposed, which is able to deal with daily HHCRSPs. Human factors are considered in our formulation of the problem and we seek a balance between the different stakeholders’ satisfaction criteria. The considered temporal constraints are soft and controlled by the stakeholders’ personalised tolerance and satisfaction rates. We will explain how this new Satisfaction-Oriented HHCRSP (SOH2CRSP) model is built and solved by using an open-source solver: the OptaPlanner. In order to examine the impact of human factors, a study will estimate the added value provided when satisfaction is considered in the problem formulation. The comparison is based on a use case derived from the dataset of an existing HHC organisation. The numerical results will show the benefits of our approach.

An exact algorithm for the home health care routing and scheduling with electric vehicles and synergistic-transport mode

An exact algorithm for the home health care routing and scheduling with electric vehicles and synergistic-transport mode

Research on Home Health Care Scheduling Considering Synchronous Access of Caregivers and Vehicles

With the arrival of an aging society, the high shortage of public medical resources leads to the increasingly serious pension problem, which has attracted extensive attention. This has led to a sharp increase in the demand for family doctors. In order to alleviate the shortage of caregivers, this paper considers the scheduling problem of home health care, which includes the synchronous access of caregivers and vehicles, and the flexible marchingmatching between caregivers and patients. First of all, we construct a Mixed Integer Linear Programming (MILP) to characterize the optimization problem. Secondly, an improved non-dominated sorting genetic algorithm with elite strategy (INSGA-II) is designed. The initial solution with high quality is obtained through the simultaneous evolution of two populations. The chromosomes are optimized by using improved crossover and mutation operators, and local search strategies are designed on this basis. Finally, several groups of test examples are used to verify the feasibility and effectiveness of the algorithm, and analyze the impact of different visit modes on objective function. The results show that the synchronous access can effectively reduce the costs, and can provide theoretical support for the medical center to make plans.

Optimal Hospital Care Scheduling During the SARS-CoV-2 Pandemic

The COVID-19 pandemic has seen dramatic demand surges for hospital care that have placed a severe strain on health systems worldwide. As a result, policy makers are faced with the challenge of managing scarce hospital capacity to reduce the backlog of non-COVID patients while maintaining the ability to respond to any potential future increases in demand for COVID care. In this paper, we propose a nationwide prioritization scheme that models each individual patient as a dynamic program whose states encode the patient’s health and treatment condition, whose actions describe the available treatment options, whose transition probabilities characterize the stochastic evolution of the patient’s health, and whose rewards encode the contribution to the overall objectives of the health system. The individual patients’ dynamic programs are coupled through constraints on the available resources, such as hospital beds, doctors, and nurses. We show that the overall problem can be modeled as a grouped weakly coupled dynamic program for which we determine near-optimal solutions through a fluid approximation. Our case study for the National Health Service in England shows how years of life can be gained by prioritizing specific disease types over COVID patients, such as injury and poisoning, diseases of the respiratory system, diseases of the circulatory system, diseases of the digestive system, and cancer. This paper was accepted by Chung-Piaw Teo, optimization. Funding: G. Forchini acknowledges funding from Jan Wallanders and Tom Hedelius Foundation and the Tore Browaldh Foundation, funding from MRC Centre for Global Infectious Disease Analysis [Reference MR/R015600/1], jointly funded by the UK Medical Research Council (MRC) and the UK Foreign, Commonwealth and Development Office (FCDO), under the MRC/FCDO Concordat agreement, part of the EDCTP2 program supported by the European Union; and acknowledges funding by Community Jameel. D. Rizmie acknowledges partial funding from the MRC Centre for Global Infectious Disease Analysis [Reference MR/R015600/1]. J. C. D’Aeth acknowledges funding from the Wellcome Trust [Reference 102169/Z/13/Z]. S. Moret acknowledges partial support from the Swiss National Science Foundation (SNSF) under [Grant P2ELP2_188028]. S. Ghosal was funded by the Imperial College President’s PhD Scholarship. F. Grimm was funded by the Health Foundation as part of core staff member activity. This research was funded in whole, or in part, by the Wellcome Trust [Grant 102169/Z/13/Z]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4679 .

Program Arrives Home Smoothly: Uncertainty-Based Routing Scheduling of Home-Based Elderly Care Programs

In China, the home-based elderly care program system plays an important role in meeting the needs of elderly individuals. The routing scheduling of home-based elderly care (RSHEC) programs is closely related to the quality of the home-based elderly care programs. The structural supply problem faced by home-based elderly care programs is a prominent problem, and the RSHEC programs are an important aspect that has rarely been studied. This paper explores RSHEC programs under uncertainty by comprehensively considering the costs of home-based elderly care, such as the fixed costs, time, and transportation costs. First, a deterministic mixed integer programming (MIP) model was constructed to solve the general routing scheduling problem. In addition, to effectively cope with the uncertainty and risk of the modern market, the robust optimization theory and algorithm model are introduced, namely, the mixed integer box set robust optimization (MIBRO) model and the mixed integer ellipsoid set robust optimization (MIERO) model. Finally, MATLAB and the Gurobi package are applied to obtain the solutions of the models. The case verification shows that the MIP model has the lowest total cost under deterministic conditions. However, the MIERO and MIBRO models can achieve more robust RSHEC programs under uncertain conditions. The results prove the effectiveness and feasibility of the optimization model and algorithm, which provides reference value for management decisions regarding home-based elderly care programs.

Brain tumours in the time of COVID-19: An online survey on patients' disease experience in one Italian region.

Since the outbreak, in 2019, of COVID-19, the world has experienced marked changes in daily habits, partly reflecting the exceptional social restrictions and health measures adopted to contain the disease. All these measures significantly affected not only peoples's daily lives and psychological well-being but also the possibility for the healthcare system to function properly. In this setting, brain tumour patients were at risk due to their higher physical and mental fragility and their need for regular care. The aim of the present study was to assess, using a self-reported online questionnaire, the patients's perceptions regarding their disease experience. We developed an online anonymous self-report survey to assess patients's disease experience during the pandemic. We investigated the impact of the COVID-19 pandemic on patients's cancer care schedules, their psychological distress and emotions felt during the pandemic, their levels of worry about COVID-19, and their oncological conditions. 107 patients answered our survey, most of them suffering from a glioma. Less than one-third of the sample had their appointments cancelled, delayed or converted into online visits due to the pandemic. Of the patients who answered the survey, 95% declared they were satisfied with their Institute's oncological management. The feelings reported most often were peacefulness or anxiety/worry; the majority of the sample reported high levels of loneliness, which tended to increase with age, whilst the psychological distress was correlated with age and with having a recurrence of the disease. Half of the sample declared severe worry about their oncological condition, in particular subjects with a recurrence or who were receiving adjuvant therapies. Patients with recurrence tended to worry more about the possibility of contracting COVID-19, and its effects. Our findings illustrate how fragile and in need of care patients with a brain tumour may be, especially those with more severe clinical conditions. These data may help boost healthcare professionals's knowledge about brain tumour patients's needs and fears, so as to be able to offer them a better hospital experience and improve their clinical management, while possibly also reducing the psychological burden on patients and their families.

A bi-objective home care routing and scheduling problem considering patient preference and soft temporal dependency constraints

A Home Health Care Routing and Scheduling Problem (HHCRSP) is one of the most practical branches in Home Health Care (HHC) optimization. The main focus of this study is to investigate an HHCRSP with both soft and Hard Time Windows (HTWs) associated with caregivers and patients, respectively. Furthermore, five different types of soft temporal dependency constraints are considered, which specify different relations amongst the starting time of dependent visits. Accordingly, a bi-objective Mixed-Integer Programming (MIP) model is devised to incorporate staff rostering, vehicle routing, and scheduling simultaneously. This model aims at minimizing the system’s total cost while maximizing the total satisfaction of patient preference. Since the problem is NP-hard, Iterated Local Search (ILS) is applied to solve large-sized problems in high frequencies and within reasonable computational time. Computational results on some real-world-inspired benchmark instances highlight the overall efficiency of the employed algorithm compared to the Non-dominated Sorting Genetic Algorithm (NSGA-II).

Access to general practice during the COVID-19 pandemic - a cross-sectional view of the opinions of adults who use social media.

The COVID-19 pandemic has had a significant impact on the health and wellbeing of people worldwide. General practices were forced to adapt to constantly changing circumstances, leading to predominance of virtual consultations. The aim of this study was to examine the impact the pandemic had on the ability of patients to access general practice. Other focuses included determining the nature of changes to appointment cancellations or delays and the level of disruption to long-term medication regimes during this period. A 25-question online survey was administered using Qualtrics®. Adult patients of Irish general practices were recruited via social media between October 2020 and February 2021. The data were examined for associations between participant groupings and key findings using chi-squared tests. 670 persons participated. Half of all doctor-patient consultations during that time were completed virtually, predominantly via telephone. Overall, 497 (78%) participants accessed their healthcare teams as scheduled, and without disruption. 18% of participants (n=104) reported difficulty in accessing their long-term medications; those who were younger, and those who typically attend general practice on a quarterly or more basis, were associated most with this disruption (p<0.05; p<0.05). Despite the COVID-19 pandemic, Irish general practice has maintained its schedule for appointments in greater than three quarters of cases. There was a clear shift away from face-to-face consultations to telephone appointments. Maintaining the prescription of long-term medications for patients remains a challenge. Further work needs to be done to ensure the continuation of care and undisrupted medication schedules during any future pandemics.

Construindo pontes para o cuidado: a experiência da navegação de pares na habilitação do vínculo com a PrEP para homens adolescentes que fazem sexo com homens e mulheres transgêneros

Vulnerable populations are at increased risk for HIV/AIDS, especially adolescent men who have sex with men (AMSM) and adolescent travestis and transgender women (ATGW). Pre-exposure prophylaxis (PrEP) is one component of combination HIV prevention and is already available for these populations in Brazil. However, ensuring its uptake entails certain challenges since inequality and barriers have traditionally marked access and linkage to the related public health services. Peer navigation could be a way of mediating the linkage process because it involves peers keeping track of others' care schedules, dynamically fostering linkage to care according to the needs of users and the actors involved in their everyday care contexts. Therefore, this study proposes analyzing peer-navigator-mediated linkage to PrEP care for 15- to 19-year-old MSM and transgender women from the PrEP1519 project in Salvador, Bahia State, Brazil. In total, 15 field notebooks/diaries, written in April-July 2019, by four peer navigators were analyzed, as were the transcripts of one focal group and 20 semi-structured interviews with adolescents (17 MSM and three trans women) between June and December 2019. Linkage via peer navigator and participant is influenced by emotional dynamics and shared personal characteristics. It is fluid and unstable and calls for care practices to be shaped to meet each participant's needs. For peer navigation to be adopted as a care strategy for sexually transmitted infection prevention and treatment, it should envisage not only increased linkage to care but also sensitivity to service users' specific characteristics and lived experiences.

Artificial intelligence-enhanced care pathway planning and scheduling system: content validity assessment of required functionalities

BackgroundArtificial intelligence (AI) and machine learning are transforming the optimization of clinical and patient workflows in healthcare. There is a need for research to specify clinical requirements for AI-enhanced care pathway planning and scheduling systems to improve human–AI interaction in machine learning applications. The aim of this study was to assess content validity and prioritize the most relevant functionalities of an AI-enhanced care pathway planning and scheduling system.MethodsA prospective content validity assessment was conducted in five university hospitals in three different countries using an electronic survey. The content of the survey was formed from clinical requirements, which were formulated into generic statements of required AI functionalities. The relevancy of each statement was evaluated using a content validity index. In addition, weighted ranking points were calculated to prioritize the most relevant functionalities of an AI-enhanced care pathway planning and scheduling system.ResultsA total of 50 responses were received from clinical professionals from three European countries. An item-level content validity index ranged from 0.42 to 0.96. 45% of the generic statements were considered good. The highest ranked functionalities for an AI-enhanced care pathway planning and scheduling system were related to risk assessment, patient profiling, and resources. The highest ranked functionalities for the user interface were related to the explainability of machine learning models.ConclusionThis study provided a comprehensive list of functionalities that can be used to design future AI-enhanced solutions and evaluate the designed solutions against requirements. The relevance of statements concerning the AI functionalities were considered somewhat relevant, which might be due to the low level or organizational readiness for AI in healthcare.

A model-based evolutionary algorithm for home health care scheduling

In this paper, for the first time a model-based evolutionary algorithm is presented for a real-life Home Health Care Routing and Scheduling Problem (HHCRSP). The algorithm generates routes consisting of care activities jointly with the underlying shift schedule, while taking into account the qualification levels. The performance is optimized in terms of travel time, time window waiting time and shift overtime. The algorithm is a novel extension of the permutation Gene-Pool Optimal Mixing Evolutionary Algorithm. Numerical experiments, using real-life data, show that the algorithm performs close to optimal for small instances, and outperforms schedules from a case study, leading to efficiency gains of 41%. Furthermore, it is shown that the model-based evolutionary algorithm performs better than a more traditional evolutionary algorithm, which demonstrates the importance of learning and exploiting a model to guide the optimization in HHCRSP.

A biased random‐key genetic algorithm for the home health care problem

AbstractHome health care problems consist in scheduling visits to home patients by health professionals while following a series of requirements. This paper studies the Home Health Care Routing and Scheduling Problem, which comprises a multi‐attribute vehicle routing problem with soft time windows. Additional route inter‐dependency constraints apply for patients requesting multiple visits, either by simultaneous visits or visits with precedence. We apply a mathematical programming solver to obtain lower bounds for the problem. We also propose a biased random‐key genetic algorithm, and we study the effects of additional state‐of‐the‐art components recently proposed in the literature for this genetic algorithm. We perform computational experiment using a publicly available benchmark dataset. Regarding the previous local search‐based methods, we find results up to 26.1% better than those of the literature. We find improvements from around 0.4% to 6.36% compared to previous results from a similar genetic algorithm.

A Generic Algorithm for Traversing a Directed Acyclic Graph: An Application to the Home Health Care Scheduling Problem

Recently, Hierarchical Optimization Models (HOM) have been widely used to model Combinatorial Optimization Problems (COP) in numerous fields including transport, supply chain economics, healthcare, etc. Their efficiency as a modeling approach is justified by their ability to ease the difficulty of the studied COP. The main problem will be decomposed hierarchically into a set of interconnected subproblems using multiple decomposition strategies. However, solving the hierarchical optimization model presents a great challenge and difficult task to get an optimal solution for the main problem. In this context, we propose a generic algorithm to efficiently solve the hierarchical optimization model. Moreover, the developed algorithm will be able to solve all kinds of hierarchical optimization models using any decomposition strategy. To validate the proposed modeling and solving approach, we solved the Home Health Care Scheduling Problem (HCSP) by conducting some experiments on a set of real-world data instances.

Long-Term Care Sustainable Networks in ADRION Region

The Long-Term Care (LTC) industry mainly comprises networks managed by providers of services other than informal caregivers and government agencies. Among the providers are the local providers of community-based services. The segment still consists of mostly small businesses. As such, it needs many improvements in logistics, information and communication technology (ICT) support, and educational programs, specifically in the ADRION region, where the rural areas require a high percentage of travel time in a working day for service providers. The demand for LTC services must be known early enough for providers to adapt to the growth of these demands, and they also need methods to support decisions on how to optimize the number of care workers to be able to plan the necessary human resources in the long term. The results are based on the authors’ previous studies of sustainable hierarchical spatial systems. The paper presents the achievements of these research activities and policies, governance and financing in the hierarchically organized services and networks of educational programs for human resources and ICT innovations in LTC, which are currently in short supply. Projections of capacities from facilities are necessary. Logistic networks to human resources are based on geo-gerontological projections, such as the multistate transition model, which is a new achievement in this area, and the adequate norms and standards of these services. The optimal number of human resources is based on the combination of the Patterson-Albracht algorithm and Multiple Travelling Salesman Problem (mTSP), as a new Home Health Care Routing and Scheduling Problem (HHCRSP), which helps in ensuring the inclusion of travel time in the concept of norms and standards, to achieve a work balance and care schedule according to the wishes of clients. The proposed approach might help professionals adapt in advance to the coming changes caused by the growing number of seniors and rapid changes in technology, and might also help in considerations as to whether the priorities of clients should be included in the basic national insurance programs or additionally charged as a higher standard of home care services. The aim is to make care and supply networks as sustainable as possible.