926 research outputs found

    The UK landscape for robotics and autonomous systems

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    Robotics and Autonomous Systems Special Interest Group Report: Innovate UK - Technology Strategy Board This landscape collates the output from a series of workshops designed to explore the impact on the UK of advances in Robotics and Autonomous Systems (RAS). In overviewing the resulting landscape it is clear that the RAS opportunity, as perceived by the UK community, is extensive and rich and that the UK has the potential to create a strong RAS market. It is also clear that robotics and autonomous systems will impact on each UK market sector and that the total size of this impact will be significantly greater than the size of the RAS sector itself. Across these sectors strong cross cutting themes exist that can be used to drive synergies to build technical capability and market opportunity. Within those sectors that will benefit the most from robotics and autonomous systems technology the potential for disruptive innovation and the need to respond to change through the development of new business models is now obvious. Robotics and autonomous systems do not work in isolation. They will require testing, regulation, standards, innovation, investment and skills together with technical progress and strong collaborative partnerships in order to fully realise the opportunity. The resulting Landscape carries an essential message; that the UK has a unique opportunity to engage with robotics and autonomous systems, to exploit existing expertise within the UK and explore its potential, but that other nations are similarly engaged and the UK must now be bold and invest to win. 41 Individuals listed as contributor

    The strategic impacts of Intelligent Automation for knowledge and service work : An interdisciplinary review

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    We would like to thank Professor Jarvenpaa and the review team for all the constructive comments and suggestions that were most helpful in revising the paper and in offering a stronger contribution. We would also like to thank Professor Guy Fitzgerald for his constructive comments on earlier versions of the paper. This study was funded by the Chartered Institute of Professional Development (CIPD). The views expressed are those of the authors and not necessarily those of the CIPD.Peer reviewedPublisher PD

    Classifying Service Robots for Policy

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    Impact of a large-scale robotics adoption on the hospital pharmacy workforce

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    The National Health Service (NHS) regularly adopts new technologies which often result in the redesign of services, where large numbers of staff undergo organisational change. The NHS is made up of teams of people, all of whom continue to work interdependently providing safe and effective care throughout these times of change. Automation in pharmacy is becoming popular, with recent advancements involving the automation of the medicines supply chain. Previous ventures involving Automated Dispensing Systems (ADS) have been small-scale. Maximising efficiencies through automation relies on the effective introduction of technologies as well as the alignment of technical and social change, and there has been little exploration of how automation impacts on the staff experience and team effectiveness. In the literature there are numerous models available against which to compare and analyse the success of teams more generally. Underpinning many of these models is the Hackman model which proposes that team effectiveness is influenced by: the effort team members exhibit; the knowledge and skills team members possess; and the appropriateness of the performance strategies implemented. There is a gap in the literature on the impacts large-scale automation has on teams (and their success) in healthcare, specifically in pharmacy. Approved in August 2008, NHS Greater Glasgow & Clyde (GG&C) initiated a large-scale redesign (the PPSU Acute Pharmacy Redesign Programme). The Programme aimed to; provide a single procurement department for Glasgow pharmacy; have a centralised Pharmacy Distribution Centre (PDC); introduce ward-level ordering; and improve the current staff skill-mix while promoting the use of patients’ own medicines in hospital (Making the Most of Your Medicines or MMyM). Since opening in September 2010, the PDC (comprising 9 robots in total) is now the single facility responsible for the procurement and distribution of medicines to approximately 4000 destinations, and affected approximately 530 hospital pharmacy staff. This scale of pharmacy redesign has not been seen in any other automated schemes in the UK. The aim of the first study was to describe and evaluate NHS GG&C pharmacy staff experiences over the programme duration by different job roles/locations. Interviews were conducted with 36 pharmacy staff members from 4 hospital sites and the PDC, and 9 stakeholders, identified by members of the project Steering Group. Staff were interviewed about their experiences before, during and after the redesign. An inductive content analysis was performed, which produced two main themes: “The Work I Do” and “The Context of My Work”. The first theme allowed the exploration of the changes in staff job role, with a focus on tasks, work pace/control, morale, training/progression opportunities and voice/relationships. The second theme focused on social impacts of the redesign, including support, leadership, praise, reliability and trust of co-workers. Results showed that there was a lack of training available and morale was low in part due to this. There was no cohesive vision among participants as to why the redesign was happening. Hospital staff training was in theory available, yet completing training, and progressing into higher pay bands was not always feasible. Management were concerned with PDC technicians losing their clinical-skills as a result of a change in job location. PDC support workers experienced a gradual depletion of medicines knowledge due to this transition. The pharmacist role was seen as more social. Experiences between MMyM and non-MMyM staff were different in terms of how challenging, varied and social the work was. All roles within the PDC appeared to be less social compared with hospital roles. The aims of the second study were to apply Hackman’s model of team effectiveness in the context of the pharmacy team dynamics and performance and (based on this model) discuss the extent to which these teams were successful in the adoption of the automation. Hackman’s characteristics were applied to the pharmacy staff interviews (n=36). The results indicated that PDC and hospital teams exhibited 8 of the 23 characteristics: members have a variety of high-level skills; members contribute and are motivated equally; members are equally committed; members have personal and professional skills; relevant education and training is present; learning should be collective; members self-regulate; and there is clarity about task requirements, constraints, resources available and who the service user is. The “minimising of performance slippages” characteristic could be observed in one hospital team but not in the PDC. The teams did not exhibit 5 of the characteristics, indicating less success in these areas: autonomy is available; adequate feedback is available; excellent performance is rewarded; team size is appropriate; and relevant education and training is actually available. Nine of Hackman’s characteristics could not be commented on due to a lack of illustrative data. This thesis adds to the limited literature on the exploration of automation in healthcare, specifically pharmacy. Three main lessons can be concluded: staff consultation and engagement is critical to the successful redesign of services driven by technology; ensuring job role components are appropriate for job tasks is essential- technology adoption may require new skill sets and also cause other pre-existing skill sets to become lost; team effectiveness is an important focus within any organisational change programme, but less up-to-date models of team effectiveness may not be ideally applicable to teams utilising technology. These lessons align with current Scottish Government policy on pharmacy innovation and provide valuable key points for change implementers to support the continued adoption of automation locally, nationally and internationally.The National Health Service (NHS) regularly adopts new technologies which often result in the redesign of services, where large numbers of staff undergo organisational change. The NHS is made up of teams of people, all of whom continue to work interdependently providing safe and effective care throughout these times of change. Automation in pharmacy is becoming popular, with recent advancements involving the automation of the medicines supply chain. Previous ventures involving Automated Dispensing Systems (ADS) have been small-scale. Maximising efficiencies through automation relies on the effective introduction of technologies as well as the alignment of technical and social change, and there has been little exploration of how automation impacts on the staff experience and team effectiveness. In the literature there are numerous models available against which to compare and analyse the success of teams more generally. Underpinning many of these models is the Hackman model which proposes that team effectiveness is influenced by: the effort team members exhibit; the knowledge and skills team members possess; and the appropriateness of the performance strategies implemented. There is a gap in the literature on the impacts large-scale automation has on teams (and their success) in healthcare, specifically in pharmacy. Approved in August 2008, NHS Greater Glasgow & Clyde (GG&C) initiated a large-scale redesign (the PPSU Acute Pharmacy Redesign Programme). The Programme aimed to; provide a single procurement department for Glasgow pharmacy; have a centralised Pharmacy Distribution Centre (PDC); introduce ward-level ordering; and improve the current staff skill-mix while promoting the use of patients’ own medicines in hospital (Making the Most of Your Medicines or MMyM). Since opening in September 2010, the PDC (comprising 9 robots in total) is now the single facility responsible for the procurement and distribution of medicines to approximately 4000 destinations, and affected approximately 530 hospital pharmacy staff. This scale of pharmacy redesign has not been seen in any other automated schemes in the UK. The aim of the first study was to describe and evaluate NHS GG&C pharmacy staff experiences over the programme duration by different job roles/locations. Interviews were conducted with 36 pharmacy staff members from 4 hospital sites and the PDC, and 9 stakeholders, identified by members of the project Steering Group. Staff were interviewed about their experiences before, during and after the redesign. An inductive content analysis was performed, which produced two main themes: “The Work I Do” and “The Context of My Work”. The first theme allowed the exploration of the changes in staff job role, with a focus on tasks, work pace/control, morale, training/progression opportunities and voice/relationships. The second theme focused on social impacts of the redesign, including support, leadership, praise, reliability and trust of co-workers. Results showed that there was a lack of training available and morale was low in part due to this. There was no cohesive vision among participants as to why the redesign was happening. Hospital staff training was in theory available, yet completing training, and progressing into higher pay bands was not always feasible. Management were concerned with PDC technicians losing their clinical-skills as a result of a change in job location. PDC support workers experienced a gradual depletion of medicines knowledge due to this transition. The pharmacist role was seen as more social. Experiences between MMyM and non-MMyM staff were different in terms of how challenging, varied and social the work was. All roles within the PDC appeared to be less social compared with hospital roles. The aims of the second study were to apply Hackman’s model of team effectiveness in the context of the pharmacy team dynamics and performance and (based on this model) discuss the extent to which these teams were successful in the adoption of the automation. Hackman’s characteristics were applied to the pharmacy staff interviews (n=36). The results indicated that PDC and hospital teams exhibited 8 of the 23 characteristics: members have a variety of high-level skills; members contribute and are motivated equally; members are equally committed; members have personal and professional skills; relevant education and training is present; learning should be collective; members self-regulate; and there is clarity about task requirements, constraints, resources available and who the service user is. The “minimising of performance slippages” characteristic could be observed in one hospital team but not in the PDC. The teams did not exhibit 5 of the characteristics, indicating less success in these areas: autonomy is available; adequate feedback is available; excellent performance is rewarded; team size is appropriate; and relevant education and training is actually available. Nine of Hackman’s characteristics could not be commented on due to a lack of illustrative data. This thesis adds to the limited literature on the exploration of automation in healthcare, specifically pharmacy. Three main lessons can be concluded: staff consultation and engagement is critical to the successful redesign of services driven by technology; ensuring job role components are appropriate for job tasks is essential- technology adoption may require new skill sets and also cause other pre-existing skill sets to become lost; team effectiveness is an important focus within any organisational change programme, but less up-to-date models of team effectiveness may not be ideally applicable to teams utilising technology. These lessons align with current Scottish Government policy on pharmacy innovation and provide valuable key points for change implementers to support the continued adoption of automation locally, nationally and internationally

    Cognitive assisted living ambient system: a survey

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    The demographic change towards an aging population is creating a significant impact and introducing drastic challenges to our society. We therefore need to find ways to assist older people to stay independently and prevent social isolation of these population. Information and Communication Technologies (ICT) provide various solutions to help older adults to improve their quality of life, stay healthier, and live independently for a time. Ambient Assisted Living (AAL) is a field to investigate innovative technologies to provide assistance as well as healthcare and rehabilitation to impaired seniors. The paper provides a review of research background and technologies of AAL

    Machine Medical Ethics

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    In medical settings, machines are in close proximity with human beings: with patients who are in vulnerable states of health, who have disabilities of various kinds, with the very young or very old, and with medical professionals. Machines in these contexts are undertaking important medical tasks that require emotional sensitivity, knowledge of medical codes, human dignity, and privacy. As machine technology advances, ethical concerns become more urgent: should medical machines be programmed to follow a code of medical ethics? What theory or theories should constrain medical machine conduct? What design features are required? Should machines share responsibility with humans for the ethical consequences of medical actions? How ought clinical relationships involving machines to be modeled? Is a capacity for empathy and emotion detection necessary? What about consciousness? The essays in this collection by researchers from both humanities and science describe various theoretical and experimental approaches to adding medical ethics to a machine, what design features are necessary in order to achieve this, philosophical and practical questions concerning justice, rights, decision-making and responsibility, and accurately modeling essential physician-machine-patient relationships. This collection is the first book to address these 21st-century concerns
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