The barriers of accepting Virtual Reality in Healthcare by older generation

Acute pain causes great anxiety in patients and is a significant challenge for the NHS staff. Chronic pain is a burden to the UK economy and causes substantial disability. Pain management is predicted to increase within the aging population in the UK. At the moment Pharmaceutical pain management techniques are commonly used, which are not only costly, but also resulting in significant side effects. Virtual Reality is an acceptable complement with minimal side effect. However there have been resistance in using VR amongst elderly population. This study explores the existing attitude and challenges expressed by elderly population in using VR technology for pain management. The study followed an experimental design and the finding indicated that pre-existing knowledge and experience of pain and VR, alongside the perceived ease of use and perceived usefulness of the technology has an impact on elderly’s attitude and adoption of VR technology

Virtual Reality in Healthcare – what stops hospitals and patients o adopt the technology?

Virtual Reality (VR) has been used in healthcare for nearly a decade but on it’s infancy in the UK. Innovation adoption is still a struggle based on recent reports, specially adoption of high tech innovation. This study looks into the barriers of adoption of VR in pain management in National Health Services in Dorset. The study investigates this from patient and staff point of view

A framework for successful adoption of surgical innovation

Background Innovation Adoption Frameworks are applied in healthcare industry, but surgical innovation does not follow the same models as medical innovation and it is not always adopted fully by members of the team. Purpose The aim of this paper is to develop a framework for successful adoption of surgical innovation. Research design This paper is inspired by design thinking. Based on a pragmatic research philosophy, a mixed method approach was selected including semi-structured interview and focus groups, following a questionnaire. Study sample A sample of five specialists in the field (doctors and managers) were selected for interview. Six focus groups were conducted. On average, five people were involved in each focus groups, 30 participants in total, including consultants, senior and junior ward nurses, health care assistant (HCA), cancer nurse specialist, stoma nurses, theatre senior and junior staff. Data collection/analysis Qualitative data was collected and analyzed using Thematic Analysis. Results Following a design thinking approach; firstly, an initial Surgical Adoption Model was proposed, based on the existing literature. Then, the challenges, processes and teams involved in Robotic Surgery adoption, an existing surgical innovation in a local NHS hospital, were explored. Five main themes were extracted from interviews and focus groups data - ‘Innovation Perception’, ‘Guilty vs. Undervalued’, ‘Knowledge is Power’, ‘Ex-novation’ and ‘Facilitators and Super-users’. This resulted into the development of an adapted Surgical Innovation Framework. Conclusions The Surgical Innovation Framework incorporated the themes extracted from the data. The framework is unique within the field of surgical innovation and is designed with the aim of improving surgical innovation adoption success rate. Future research can trial the framework to evaluate its effectiveness

Innovation Adoption in Robotic Surgery - A Surgical Innovation Framework using Royal Bournemouth Hospital

Robotic Surgery is studied as an innovative surgical procedure and we examine the way Robotic Surgery is adopted by healthcare professionals in Royal Bournemouth Hospital. This research aims to develop a Surgical Innovation Framework (SIF) that incorporates the broader adoption of surgical innovation for the National Health Service (NHS) in the United Kingdom. The aim of the framework is to assist the NHS in its determination and adoption of surgical innovation. The framework is developed taking into consideration The recent reports by the Department of Health and Social Care, NHS, NICE and existing body of knowledge. The proposed model considers unique characteristics of Robotic Surgery, NHS as the host organization, healthcare professionals as adopters and other elements such as communication channels. An end-to-end communication pathway is developed for the robotic surgery team to identify stakeholders involved in this process

Troublesome trade-offs: balancing urban activities and values when securing a city-centre governmental quarter

Background Homeland security measures increasingly affect urban life and activities. Standoff distance, which prevents unscreened vehicles from approaching within a certain distance of a building, is a widely applied measure when protecting buildings against attacks with vehicle-borne improvised explosive devices. This measure both is rather inexpensive and has few negative externalities when implemented in rural areas. Unfortunately, sites with protection needs often are situated in city centres. Methods We apply the so-called Security Function Framework to illuminate the externalities or the ‘troublesome trade-offs’ between protecting a high-value site against vehicle-borne improvised explosive devices and protecting other urban values. Results This paper demonstrates that standoff creates challenges for other important values, such as functional office spaces for all employees, deliveries and emergency vehicle access. Simultaneously, standoff creates opportunities for reinforcing social-responsibility requirements, such as accessibility for pedestrians and environmental considerations. Conclusions Security measures can have both negative and positive externalities and planning might alleviate some of the negative ones

A novel underdetermined source recovery algorithm based on k-sparse component analysis

Sparse component analysis (SCA) is a popular method for addressing underdetermined blind source separation in array signal processing applications. We are motivated by problems that arise in the applications where the sources are densely sparse (i.e. the number of active sources is high and very close to the number of sensors). The separation performance of current underdetermined source recovery (USR) solutions, including the relaxation and greedy families, reduces with decreasing the mixing system dimension and increasing the sparsity level (k). In this paper, we present a k-SCA-based algorithm that is suitable for USR in low-dimensional mixing systems. Assuming the sources is at most (m−1) sparse where m is the number of mixtures; the proposed method is capable of recovering the sources from the mixtures given the mixing matrix using a subspace detection framework. Simulation results show that the proposed algorithm achieves better separation performance in k-SCA conditions compared to state-of-the-art USR algorithms such as basis pursuit, minimizing norm-L1, smoothed L0, focal underdetermined system solver and orthogonal matching pursuit