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Errors in medicine are a significant problem, highlighted as a global safety priority. General Practice is one clinical arena where error is more likely due to clinical decisions being made on a background of clinical complexity, undifferentiated symptoms and diseases, and multiple other factors as yet unquantified. Interventions designed to reduce error are either underutilised, untested, fail to produce lasting results, are designed on inadequate knowledge, or have failed to appreciate the interaction of multiple factors, both cognitive and systemic. We present a potential solution, in the form of GP Benchmark. GP Benchmark is an online simulation environment and tool designed to test clinical decision making in a group of practicing General Practitioners. Its aim is to address two pressing requirements: 1) the need to capture clinical decision making in real-time, in the context of personality, cognitive bias and environmental factors, and 2) the need to provide a validated platform that models the clinical environment so future intervention decisions may be tested without risking patient safety. We highlight the requirements satisfied for implementing GP Benchmark, the plans for validation, and discuss how GP Benchmark will be used to identify further requirements necessary to develop the environment into a tool for testing clinical decision support systems and error prevention strategies.

Digital health technology utilizing wearables, IoT and mobile devices has been successfully applied in the monitoring of numerous diseases and conditions. However, intervention, in response to monitored data, is yet to benefit from technological support and continues to follow a traditional point-of-care delivery model by providers and health professionals. Mental health is an example of a critical health area in dire need for technology solutions to enable timely, effective and scalable interventions. This is especially the case with an increasing prevalence of mental health conditions and a declining capacity of the healthcare professional workforce. Numerous studies reveal the potential for peer support groups as an effective, scalable, cost-effective, first-line of response in mental health interventions. Peer support helps participants, at low and moderate risk, better understand their diseases or conditions and empowers them to take control of their own health. Peer support interactions also seems to inform health professionals with insights and intricate knowledge, making it effectively a learning health system. This paper proposes a software architecture to better enable "peer-sourcing". We present related work and show how the proposed architecture might draw similarity to and differences from crowd-sourcing architectures. We also present a study in which we interacted with service users (mental health patients) and mental healthcare professionals to better understand and elicit the key requirements for the software architecture.

Digital health technology utilizing wearables, IoT and mobile devices has been successfully applied in the monitoring of numerous diseases and conditions. However, intervention, in response to monitored data, is yet to benefit from technological support and continues to follow a traditional point-of-care delivery model by providers and health professionals. Mental health is an example of a critical health area in dire need for technology solutions to enable timely, effective and scalable interventions. This is especially the case with an increasing prevalence of mental health conditions and a declining capacity of the healthcare professional workforce. Numerous studies reveal the potential for peer support groups as an effective, scalable, cost-effective, first-line of response in mental health interventions. Peer support helps participants, at low and moderate risk, better understand their diseases or conditions and empowers them to take control of their own health. Peer support interactions also seems to inform health professionals with insights and intricate knowledge, making it effectively a learning health system. This paper proposes a software architecture to better enable "peer-sourcing". We present related work and show how the proposed architecture might draw similarity to and differences from crowd-sourcing architectures. We also present a study in which we interacted with service users (mental health patients) and mental healthcare professionals to better understand and elicit the key requirements for the software architecture.

We present a desktop monitoring application that combines keyboard, mouse, desktop and application-level activities. It has been developed to discover differences in cognitive functioning amongst older computer users indicative of mild cognitive impairment (MCI). Following requirements capture from clinical domain experts, the tool collects all Microsoft Windows events deemed potentially useful for detecting early clinical indicators of dementia, with a view to further analysis to determine the most pertinent. Further requirements capture from potential end-users has resulted in a system that has little impact on users? daily activities and ensures data security from initial recording of events through to data analysis. We describe two experiments: firstly, volunteers were asked to perform a short set of known tasks; the second (ongoing) experiment is a longitudinal study, with the software currently successfully running on participants? computers.

We have been generally successful for transferring software engineering knowledge to industry through various forms of education. However, many challenges in software engineering training remain. A key amongst these is how best to energise software engineering education with real-world software engineering practices. This paper describes our experience of delivering a radically different approach based on the notion of a Software Design Studio. The Software Design Studio is both a lab for students engaged in conceiving, designing and developing software products as well as an approach for teaching software engineering in the lab which emphasizes practical hands-on work and experimentation. The feedback on the Software Design Studio – from both staff and students – has been outstanding. Although the programme is designed as a small, elite programme there is interest to see if the teaching methods can be transferred across to the much larger undergraduate programme in Computer Science. In this paper, we provide a detailed description of how our studio works in practice so that others, thinking of taking a studio or studio-inspired approach, can use in designing their own courses.

Dementia is a serious threat to social sustainability. As life expectancy increases, more people are developing dementia. At the same time, demographic change is reducing the economically active part of the population. Care of people with dementia imposes great emotional and financial strain on sufferers, their families and society at large. In response, significant research resources are being focused on dementia. One research thread is focused on using computer technology for monitoring people in at-risk groups to improve rates of early diagnosis. In this paper we provide an overview of dementia monitoring research and identify a set of scientific challenges for the engineering of dementia-monitoring software, with implications for other mental health self-management systems.

We have been generally successful for transferring software engineering knowledge to industry through various forms of education. However, many challenges in software engineering training remain. A key amongst these is how best to energise software engineering education with real-world software engineering practices. This paper describes our experience of delivering a radically different approach based on the notion of a Software Design Studio. The Software Design Studio is both a lab for students engaged in conceiving, designing and developing software products as well as an approach for teaching software engineering in the lab which emphasizes practical hands-on work and experimentation. The feedback on the Software Design Studio – from both staff and students – has been outstanding. Although the programme is designed as a small, elite programme there is interest to see if the teaching methods can be transferred across to the much larger undergraduate programme in Computer Science. In this paper, we provide a detailed description of how our studio works in practice so that others, thinking of taking a studio or studio-inspired approach, can use in designing their own courses.

Novel envisioned systems face the risk of rejection by their target user community and the requirements engineer must be sensitive to the factors that will determine acceptance or rejection. Conventionally, technology acceptance is determined by perceived usefulness and ease-of-use, but in some domains other factors play an important role. In healthcare systems, particularly, ethical and emotional factors can be crucial. In this paper we describe an approach to requirements discovery that we developed for such systems. We describe how we have applied our approach to a novel system to passively monitor users for signs of cognitive decline consistent with the onset of dementia. A key challenge was eliciting users’ reactions to emotionally charged events never before experienced by them at first hand. Our goal was to understand the range of users’ emotional responses and their values and motivations, and from these formulate requirements that would maximise the likelihood of acceptance of the system. The problem was heightened by the fact that the key stakeholders were elderly people who represent a poorly studied user constituency. We discuss the elicitation and analysis methodologies used, and our experience with tool support. We conclude by reflecting on the affect issues for RE and for technology acceptance.

Studio-based learning for software engineering is a well-received concept, despite its apparent lack of uptake across institutions worldwide. Studio education affords a variety of highly desirable benefits, and is also popular amongst its students. This paper presents Lancaster University’s software engineering studio, details of its implementation, observations made throughout its first year, evidence of its successes, and reflections against the recently defined studio framework . This paper aims to provide useful information for anyone that is considering utilizing a studio-based approach.

Studio-based learning for software engineering is a well-received concept, despite its apparent lack of uptake across institutions worldwide. Studio education affords a variety of highly desirable benefits, and is also popular amongst its students. This paper presents Lancaster University’s software engineering studio, details of its implementation, observations made throughout its first year, evidence of its successes, and reflections against the recently defined studio framework . This paper aims to provide useful information for anyone that is considering utilizing a studio-based approach.