Ensuring the Service Quality of Long-Term Care Provided through Competitive Markets: The Experience of Care Workers' Training in Japan

Ensuring the service quality of long-term care provided through competitive markets is a major concern among the governments of OECD members. The public officials in these nations recognise the importance of care workers' training to address this issue. However, most of them have hesitated to introduce comprehensive training due to financial constraints. Analysing the experience of Japan, this paper reveals that governments can ensure the financial sustainability of care workers' training by aiming at the best possible long-term care.

Computational neurorehabilitation: modeling plasticity and learning to predict recovery

Despite progress in using computational approaches to inform medicine and neuroscience in the last 30 years, there have been few attempts to model the mechanisms underlying sensorimotor rehabilitation. We argue that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing computational models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. Here, we therefore discuss Computational Neurorehabilitation, a newly emerging field aimed at modeling plasticity and motor learning to understand and improve movement recovery of individuals with neurologic impairment. We first explain how the emergence of robotics and wearable sensors for rehabilitation is providing data that make development and testing of such models increasingly feasible. We then review key aspects of plasticity and motor learning that such models will incorporate. We proceed by discussing how computational neurorehabilitation models relate to the current benchmark in rehabilitation modeling – regression-based, prognostic modeling. We then critically discuss the first computational neurorehabilitation models, which have primarily focused on modeling rehabilitation of the upper extremity after stroke, and show how even simple models have produced novel ideas for future investigation. Finally, we conclude with key directions for future research, anticipating that soon we will see the emergence of mechanistic models of motor recovery that are informed by clinical imaging results and driven by the actual movement content of rehabilitation therapy as well as wearable sensor-based records of daily activity

Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.Comment: Portions of this paper were published in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper consists of 50 pages with 11 figures and 4 table

Robotic devices and ICT in long-term care in Japan: Their potential and limitations from a workplace perspective

In light of its rapidly aging society, Japan is pressured to full-heartedly address the labor shortage in long-term care. Among the various policy options currently in discussion, government agencies and business sector representatives agree that robotic devices and information and communication technology (ICT) constitute a suitable countermeasure. However, during our research in Japan in 2019, we found that robotic devices and ICT are only reluctantly being introduced into long-term care facilities. Based on our field visits and interviews as well as supplementary document research, this paper discusses the potential that facility managers ascribe to robotic devices and ICT when it comes to alleviating the labor shortage in the long-term care institutions they run. Of particular interest is the question to what degree the usage of robotic devices and ICT could reduce the physical hardships and mental stress that staff in long-term caregiving experience. This paper will further our understanding of the labor situation in long-term care facilities and contribute to the research field of robotic devices and ICT in Japan’s labor market

Technology-assisted training of arm-hand skills in stroke: concepts on reacquisition of motor control and therapist guidelines for rehabilitation technology design

<p>Abstract</p> <p>Background</p> <p>It is the purpose of this article to identify and review criteria that rehabilitation technology should meet in order to offer arm-hand training to stroke patients, based on recent principles of motor learning.</p> <p>Methods</p> <p>A literature search was conducted in PubMed, MEDLINE, CINAHL, and EMBASE (1997–2007).</p> <p>Results</p> <p>One hundred and eighty seven scientific papers/book references were identified as being relevant. Rehabilitation approaches for upper limb training after stroke show to have shifted in the last decade from being analytical towards being focussed on environmentally contextual skill training (task-oriented training). Training programmes for enhancing motor skills use patient and goal-tailored exercise schedules and individual feedback on exercise performance. Therapist criteria for upper limb rehabilitation technology are suggested which are used to evaluate the strengths and weaknesses of a number of current technological systems.</p> <p>Conclusion</p> <p>This review shows that technology for supporting upper limb training after stroke needs to align with the evolution in rehabilitation training approaches of the last decade. A major challenge for related technological developments is to provide engaging patient-tailored task oriented arm-hand training in natural environments with patient-tailored feedback to support (re) learning of motor skills.</p

Art and Medicine: A Collaborative Project Between Virginia Commonwealth University in Qatar and Weill Cornell Medicine in Qatar

Four faculty researchers, two from Virginia Commonwealth University in Qatar, and two from Weill Cornell Medicine in Qatar developed a one semester workshop-based course in Qatar exploring the connections between art and medicine in a contemporary context. Students (6 art / 6 medicine) were enrolled in the course. The course included presentations by clinicians, medical engineers, artists, computing engineers, an art historian, a graphic designer, a painter, and other experts from the fields of art, design, and medicine. To measure the student experience of interdisciplinarity, the faculty researchers employed a mixed methods approach involving psychometric tests and observational ethnography. Data instruments included pre- and post-course semi-structured audio interviews, pre-test / post-test psychometric instruments (Budner Scale and Torrance Tests of Creativity), observational field notes, self-reflective blogging, and videography. This book describes the course and the experience of the students. It also contains images of the interdisciplinary work they created for a culminating class exhibition. Finally, the book provides insight on how different fields in a Middle Eastern context can share critical /analytical thinking tools to refine their own professional practices

A Nurse Practitioner's Experience Using Remote Presence Technology in an Isolated Community: An Autoethnography

There are many barriers to accessing healthcare services, particularly for people living in remote communities. Embracing innovation, such as remote presence technology (RPT), is one approach to reducing barriers. Innovations for healthcare service delivery can affect the care provided at the local level. Research Question and Methodology I examined the effect of RPT on healthcare service delivery in a remote community. Employing analytic autoethnography, I took a three-pronged approach, considering a systems level, patient care level, and nurse practitioner (NP) practice level. Outcomes Systems Level In Saskatchewan, rural and remote dwellers experience a disparity in access to primary and specialist care, allied health professionals, and diagnostic services. Contributing factors involve the centralization of services, distance decay, transportation, and unfunded costs. Social determinants of health and systemic oppression contribute to the disparity. This results in patients receiving altered standards of care and poorer health outcomes. Patient Care Level RPT resulted in superior care provided to patients, resulting in a decrease in morbidity and mortality. Most patients were able to remain in the community rather than being transferred out, and patients and families were actively engaged with developing the care plan. NP Practice Level Working directly with specialists through collaboration, mentorship, and coaching enhanced NP work confidence and competence. Professional isolation and decision fatigue decreased. Challenges included not having access to the necessary providers, provider attitudes, inflexible policies, and having to shoulder the burden of care. Conclusion The introduction of RPT can have a drastic effect on the healthcare system, which is felt most at the local level. Patients can receive appropriate and timely care in their home community. NPs have a unique role working in remote healthcare settings. This model of care could have a significant influence on NP practice. Initiatives should be community-driven and responsive to match needs with services