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Background: Organizational readiness for change, defined as the collective preparedness of organization members to enact changes, remains understudied in implementing sepsis survivor transition-in-care protocols. Effective implementation relies on collaboration between hospital and post-acute care informants, including those who are leaders and staff. Therefore, our cross-sectional study compared organizational readiness for change among hospital and post-acute care informants. Methods: We invited informants from 16 hospitals and five affiliated HHC agencies involved in implementing a sepsis survivor transition-in-care protocol to complete a pre-implementation survey, where organizational readiness for change was measured via the Organizational Readiness to Implement Change (ORIC) scale (range 12-60). We also collected their demographic and job area information. Mann-Whitney U-tests and linear regressions, adjusting for leadership status, were used to compare organizational readiness of change between hospital and post-acute care informants. Results: Eighty-four informants, 51 from hospitals and 33 from post-acute care, completed the survey. Hospital and post-acute care informants had a median ORIC score of 52 and 57 respectively. Post-acute care informants had a mean 4.39-unit higher ORIC score compared to hospital informants (p = 0.03). Conclusions: Post-acute care informants had higher organizational readiness of change than hospital informants, potentially attributed to differences in health policies, expertise, organizational structure, and priorities. These findings and potential inferences may inform sepsis survivor transition-in-care protocol implementation. Future research should confirm, expand, and examine underlying factors related to these findings with a larger and more diverse sample. Additional studiesmay assess the predictive validity of ORIC towards implementation success. [ABSTRACT FROM AUTHOR]

BackgroundOrganizational readiness for change, defined as the collective preparedness of organization members to enact changes, remains understudied in implementing sepsis survivor transition-in-care protocols. Effective implementation relies on collaboration between hospital and post-acute care informants, including those who are leaders and staff. Therefore, our cross-sectional study compared organizational readiness for change among hospital and post-acute care informants.MethodsWe invited informants from 16 hospitals and five affiliated HHC agencies involved in implementing a sepsis survivor transition-in-care protocol to complete a pre-implementation survey, where organizational readiness for change was measured via the Organizational Readiness to Implement Change (ORIC) scale (range 12–60). We also collected their demographic and job area information. Mann-Whitney U-tests and linear regressions, adjusting for leadership status, were used to compare organizational readiness of change between hospital and post-acute care informants.ResultsEighty-four informants, 51 from hospitals and 33 from post-acute care, completed the survey. Hospital and post-acute care informants had a median ORIC score of 52 and 57 respectively. Post-acute care informants had a mean 4.39-unit higher ORIC score compared to hospital informants (p = 0.03).ConclusionsPost-acute care informants had higher organizational readiness of change than hospital informants, potentially attributed to differences in health policies, expertise, organizational structure, and priorities. These findings and potential inferences may inform sepsis survivor transition-in-care protocol implementation. Future research should confirm, expand, and examine underlying factors related to these findings with a larger and more diverse sample. Additional studies may assess the predictive validity of ORIC towards implementation success.

Providing appropriate home health care is one of the increasing concerns in the health care organizations. Home health care provides various services for disabled or elderly individuals at their homes. Also, dealing with the current critical situation of the coronavirus disease (COVID-19) due to the limited capacity of hospitals and the feeling of insecurity in crowded places, home health care is more recommended. This paper addresses a Home Health Care Routing and Scheduling Problem (HHCRSP) with two modes of transportations including public and private modes. Also, multi-depot version of the problem is studied to enhance the service delivery in scattered points. In this study, a mathematical model is presented based on a Mixed Integer Linear Programming (MILP) whose objective function is minimization of the sum of the travel distance and overtime costs. Furthermore, three meta-heuristic algorithms including Invasive Weed Optimization (IWO), Grasshopper Optimization Algorithm (GOA), and Simulated Annealing (SA) are presented for solving large-sized problems. Since the performance of meta-heuristic algorithms depends on setting the parameters, the Taguchi method is used to statistically set parameters of the developed algorithms. The computational results have shown that the proposed IWO has worked better than the other two proposed algorithms statistically. [ABSTRACT FROM AUTHOR]

Abstract Background The use of home health care (HHC) is increasing worldwide. This may have an impact not only on patients and their caregivers’ health but on care resource utilization and costs. We lack information on the impact of HHC on the broader dimensions of health status and care resource utilization. More understanding of the longitudinal HHC impact on HHC patients and caregivers is also needed. Moreover, we know little about the synergy between HHC and social care. Therefore, the present study aims to observe longitudinal changes in health, care resource utilization and costs and caregiving burden among HHC recipients and their caregivers in Taiwan. Methods A prospective cohort study “Home-based Longitudinal Investigation of the Multidisciplinary Team Integrated Care (HOLISTIC)” will be conducted and 600 eligible patient-caregiver dyads will be recruited and followed with comprehensive quantitative assessments during six home investigations over two years. The measurements include physical function, psychological health, cognitive function, wellbeing, shared decision making and advance care planning, palliative care and quality of dying, caregiving burden, continuity and coordination of care, care resource utilization, and costs. Discussion The HOLISTIC study offers the opportunity to comprehensively understand longitudinal changes in health conditions, care resource utilization and costs and caregiving burden among HHC patients and caregivers. It will provide new insights for clinical practitioners and policymakers. Trial registration ClinicalTrials.gov Identifier is NCT04250103 which has been registered on 31st January 2020.

Home health care (HHC) logistics have become a hot research topic in recent years due to the importance of HHC services for the care of ageing population. The logistics of HHC services as a routing and scheduling problem can be defined as the HHC problem (HHCP) academically including a set of service centers and a large number of patients distributed in a specific geographic environment to provide various HHC services. The main challenge is to provide a valid plan for the caregivers, who include nurses, therapists, and doctors, with regard to different difficulties, such as the time windows of availability for patients, scheduling of the caregivers, working time balancing, the time and cost of the services, routing of the caregivers, and route balancing for their routes. This study establishes a biobjective optimization model that minimizes (i) the total service time and (ii) the total costs of HHC services to meet the aforementioned limitations for the first time. To the best of the authors' knowledge, this research is the first of its kind to optimize the time and cost of HHC services by considering the route balancing. Since the model of the developed HHCP is complex and classified as NP-hard, efficient metaheuristic algorithms are applied to solve the problem. Another innovation is the development of a new self-adaptive metaheuristic as an improvement to the social engineering optimizer (SEO), so-called ISEO. An extensive analysis is done to show the high performance of ISEO in comparison with itself and two well-known metaheuristics, i.e. FireFly algorithm and Artificial Bee Colony algorithm. Finally, the results confirm the applicability of new suppositions of the model and further development and investigation of the ISEO more broadly. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This integrative review analyzes research on the relationship of Home Health Care (HHC) to readmissions, specifically, identifying moderating and mediating factors and measurement constraints influencing effectiveness evaluations of HHC in reducing readmissions. HHC patients’ readmission rates are higher than patients not receiving home health services but measurement of effectiveness is confounded by both practice variation and comparisons using noncomparable control groups. Effectiveness evaluations of HHC in reducing readmission requires attention to sample comparability and control for mediating variables. Establishing evidence of effectiveness clarifies the utility of HHC as a strategy to reduce readmissions. [ABSTRACT FROM AUTHOR]

OBJECTIVES: To assess patient characteristics and risk factors associated with care transitions between skilled nursing facility (SNF) and home care for patients with multiple sclerosis (MS) in an effort to improve outcomes and optimize patient care pathways. BACKGROUND: MS is a chronic neurologic illness of younger adults that is associated with physical disability, cognitive impairment and a high need for supportive services. METHODS: The study was based on the 2005 Nursing Home Minimum Data Set and the Outcome and Assessment Information Set data (n = 10,064). We performed multivariate evaluation of patient risk factors for skilled nursing facility (SNF) admission and disposition while controlling for potential patient self-selection and other characteristics that affect care utilization. RESULTS: MS patients with recent history of home care use were less likely to be admitted to an acute care hospital and had higher physical disability at SNF admission than SNF patients who did not use home care. Insurance type (Medicaid) and availability of informal caregivers were associated with the use of homecare services after a SNF stay, while patient demographic and clinical characteristics did not explain SNF disposition. CONCLUSIONS: Future studies should explore the association between the local availability and affordability of home-based services and physician attitude about community-based care management of disabled patients. [ABSTRACT FROM AUTHOR]

Background: Electronic health information platforms have the potential to support standardized care delivery models, engage patients, and deliver patient self-assessment tools. Study Design: We tested whether an e-health platform could support the delivery and acquisition of patient-reported outcomes (PROs) during hospitalization after cardiac surgery. Secondarily, we tested if patient reported data were predictive of resource use (length of stay) or outcomes (discharge disposition). Subjects were 149 cardiac surgical patients, over age 50 years, undergoing routine surgery, with an expected length of stay of 5 to 7 days. While hospitalized, patients were provided with iPads (Apple), which delivered a personalized care plan. That plan included daily patient “To Do” lists with self-assessment modules that included recovery-related patient reported outcomes. Those included an early screen for discharge planning (ESDP) as well as daily pain and mobility self-assessments using the visual analog pain scale and the I-MOVE mobility scale. We measured completion rates for the self assessments, determined length of stay (short, intermediate, or long) and discharge disposition (home independently or other), and evaluated whether patient self-assessments were predictive of these outcomes. Results: Patients completed 98% of the 1,418 self-assessments that were delivered. The ESDP and mobility self-assessments were predictive of length of hospital stay (p = 0.004 and p = 0.012, respectively) and of discharge disposition (p < 0.0001 and p < 0.007, respectively). Lower ESDP scores and higher I-MOVE scores were predictive of shorter lengths of stay and a higher likelihood of discharge to home independently. Conclusions: Our trial demonstrated that an e-health platform, combined with mobile computing, can effectively deliver customized care plans with which patients can interact. Furthermore, patient self-reported data are predictive of length of stay and discharge disposition. [Copyright &y& Elsevier]