BAN-Based m-health Services: Experiences and Prospects

The University of Twente and partners are developing and prototyping Body Area networks (BANs) for healthcare. We define a BAN as a network of devices worn on or around the body which communicate amongst themselves and perform a set of services for the user. Our work began with the European MobiHealth project whose objective was to investigate the potential of 2.5 and 3G wireless communication technologies to support useful services and applications. In this article we discuss the main aims and results of the MobiHealth project. On the basis of these results we further discuss a particular methodology which we believe gives leverage on the problem of managing the complex of objectives and expectations of the different parties involved in the process of design, development and implementation of ICT systems for healthcare. This methodology aims at articulation and translation of the visions and expectations of both designers and prospective users in future development scenarios. These scenarios may be used to specify potential uses of health BAN technology in particular contexts, to anticipate and evaluate possible outcomes and effects, and to feed back insights obtained from this anticipatory technology assessment into the ongoing process of design, development and deployment

Health Technology Scenarios and Implications for Spectrum

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Defining the methodological challenges and opportunities for an effective science of sociotechnical systems and safety

An important part of the application of sociotechnical systems theory (STS) is the development of methods, tools and techniques to assess human factors and ergonomics workplace requirements. We focus in this paper on describing and evaluating current STS methods for workplace safety, as well as outlining a set of six case studies covering the application of these methods to a range of safety contexts. We also describe an evaluation of the methods in terms of ratings of their ability to address a set of theoretical and practical questions (e.g. the degree to which methods capture static/dynamic aspects of tasks and interactions between system levels). The outcomes from the evaluation highlight a set of gaps relating to the coverage and applicability of current methods for STS and safety (e.g. coverage of external influences on system functioning; method usability). The final sections of the paper describe a set of future challenges, as well as some practical suggestions for tackling these. Practitioner Summary: We provide an up-to-date review of STS methods, a set of case studies illustrating their use and an evaluation of their strengths and weaknesses. The paper concludes with a ‘roadmap’ for future work

Behaviour Profiling using Wearable Sensors for Pervasive Healthcare

In recent years, sensor technology has advanced in terms of hardware sophistication and miniaturisation. This has led to the incorporation of unobtrusive, low-power sensors into networks centred on human participants, called Body Sensor Networks. Amongst the most important applications of these networks is their use in healthcare and healthy living. The technology has the possibility of decreasing burden on the healthcare systems by providing care at home, enabling early detection of symptoms, monitoring recovery remotely, and avoiding serious chronic illnesses by promoting healthy living through objective feedback. In this thesis, machine learning and data mining techniques are developed to estimate medically relevant parameters from a participant‘s activity and behaviour parameters, derived from simple, body-worn sensors. The first abstraction from raw sensor data is the recognition and analysis of activity. Machine learning analysis is applied to a study of activity profiling to detect impaired limb and torso mobility. One of the advances in this thesis to activity recognition research is in the application of machine learning to the analysis of 'transitional activities': transient activity that occurs as people change their activity. A framework is proposed for the detection and analysis of transitional activities. To demonstrate the utility of transition analysis, we apply the algorithms to a study of participants undergoing and recovering from surgery. We demonstrate that it is possible to see meaningful changes in the transitional activity as the participants recover. Assuming long-term monitoring, we expect a large historical database of activity to quickly accumulate. We develop algorithms to mine temporal associations to activity patterns. This gives an outline of the user‘s routine. Methods for visual and quantitative analysis of routine using this summary data structure are proposed and validated. The activity and routine mining methodologies developed for specialised sensors are adapted to a smartphone application, enabling large-scale use. Validation of the algorithms is performed using datasets collected in laboratory settings, and free living scenarios. Finally, future research directions and potential improvements to the techniques developed in this thesis are outlined

N.C. Medicaid Reform: A Bipartisan Path Forward

The North Carolina Medicaid program currently constitutes 32% of the state budget and provides insurance coverage to 18% of the state’s population. At the same time, 13% of North Carolinians remain uninsured, and even among the insured, significant health disparities persist across income, geography, education, and race. The Duke University Bass Connections Medicaid Reform project gathered to consider how North Carolina could use its limited Medicaid dollars more effectively to reduce the incidence of poor health, improve access to healthcare, and reduce budgetary pressures on the state’s taxpayers. This report is submitted to North Carolina’s policymakers and citizens. It assesses the current Medicaid landscape in North Carolina, and it offers recommendations to North Carolina policymakers concerning: (1) the construction of Medicaid Managed Care markets, (2) the potential and dangers of instituting consumer-driven financial incentives in Medicaid benefits, (3) special hotspotting strategies to address the needs and escalating costs of Medicaid\u27s high-utilizers and dual-eligibles, (4) the emerging benefits of pursuing telemedicine and associated reforms to reimbursement, regulation, and Graduate Medical Education programs that could fuel telemedicine solutions to improve access and delivery. The NC Medicaid Reform Advisory Team includes: Deanna Befus, Duke School of Nursing, PhD ‘17Madhulika Vulimiri, Duke Sanford School of Public Policy, MPP ‘18Patrick O’Shea, UNC School of Medicine/Fuqua School of Business, MD/MBA \u2717Shanna Rifkin, Duke Law School, JD ‘17Trey Sinyard, Duke School of Medicine/Fuqua School of Business, MD/MBA \u2717Brandon Yan, Duke Public Policy, BA \u2718Brooke Bekoff, UNC Political Science, BA \u2719Graeme Peterson, Duke Public Policy, BA ‘17Haley Hedrick, Duke Psychology, BS ‘19Jackie Lin, Duke Biology, BS \u2718Kushal Kadakia, Duke Biology and Public Policy, BS ‘19Leah Yao, Duke Psychology, BS ‘19Shivani Shah, Duke Biology and Public Policy, BS ‘18Sonia Hernandez, Duke Economics, BS \u2719Riley Herrmann, Duke Public Policy, BA \u271

Defining the methodological challenges and opportunities for an effective science of sociotechnical systems and safety

An important part of the application of sociotechnical systems theory (STS) is the development of methods, tools and techniques to assess human factors and ergonomics workplace requirements. We focus in this paper on describing and evaluating current STS methods for workplace safety, as well as outlining a set of six case studies covering the application of these methods to a range of safety contexts. We also describe an evaluation of the methods in terms of ratings of their ability to address a set of theoretical and practical questions (e.g. the degree to which methods capture static/dynamic aspects of tasks and interactions between system levels). The outcomes from the evaluation highlight a set of gaps relating to the coverage and applicability of current methods for STS and safety (e.g. coverage of external influences on system functioning; method usability). The final sections of the paper describe a set of future challenges, as well as some practical suggestions for tackling thes

IT-based Regulation of Personal Health: Nudging, Mobile Health Apps and Personal Health Data

journal articleMobile health applications and devices (“mobile health apps”) are increasingly embedded in organizational programs to regulate the personal health behaviors of individuals and populations. In this paper, we draw on de Vaujany et al.’s (2018) framework for IT-based regulation systems to consider how regulatory outcomes can develop in such settings, in which individual actors have strong agency and regulation is indirect and voluntary. Through an instrumental case of a continuous glucose monitoring system used for self-regulation of diabetes, we examine how IT artifacts become embedded in self- regulation practices, how data generated by these apps are implicated in regulatory feedback loops, and how networks of individual, organizational and technological actors are mobilized in regulatory regimes. We examine how data about bodily states and IT features such as displays and alarms ‘nudge’ individuals towards compliance with expert rules materialized in the IT artifact. We then identify regulatory affordances of mobile health apps for predicting and surveilling personal health. We also theorize how multilevel networks of trifecta of rules, IT artifacts, and practices develop through regulatory episodes as a regulatory lattice, and how social regulation is realized as a result. We conclude by considering the theoretical and practical implications of this analytical approach to investigate IT-based regulation in the open, distributed, and indirect regulatory contexts