A Manifest-Based Framework for Organizing the Management of Personal Data at the Edge of the Network

Smart disclosure initiatives and new regulations such as GDPR allow individuals to get the control back on their data by gathering their entire digital life in a Personal Data Management Systems (PDMS). Multiple PDMS architectures exist, from centralized web hosting solutions to self-data hosting at home. These solutions strongly differ on their ability to preserve data privacy and to perform collective computations crossing data of multiple individuals (e.g., epidemiological or social studies) but none of them satisfy both objectives. The emergence of Trusted Execution Environments (TEE) changes the game. We propose a solution called Trusted PDMS, combining the TEE and PDMS properties to manage the data of each individual, and a Manifest-based framework to securely execute collective computation on top of them. We demonstrate the practicality of the solution through a real case-study being conducted over 10.000 patients in the healthcare field

A Hybrid Modelling Framework for Real-time Decision-support for Urgent and Emergency Healthcare

In healthcare, opportunities to use real-time data to support quick and effective decision-making are expanding rapidly, as data increases in volume, velocity and variety. In parallel, the need for short-term decision-support to improve system resilience is increasingly relevant, with the recent COVID-19 crisis underlining the pressure that our healthcare services are under to deliver safe, effective, quality care in the face of rapidly-shifting parameters. A real-time hybrid model (HM) which combines real-time data, predictions, and simulation, has the potential to support short-term decision-making in healthcare. Considering decision-making as a consequence of situation awareness focuses the HM on what information is needed where, when, how, and by whom with a view toward sustained implementation. However the articulation between real-time decision-support tools and a sociotechnical approach to their development and implementation is currently lacking in the literature. Having identified the need for a conceptual framework to support the development of real-time HMs for short-term decision-support, this research proposed and tested the Integrated Hybrid Analytics Framework (IHAF) through an examination of the stages of a Design Science methodology and insights from the literature examining decision-making in dynamic, sociotechnical systems, data analytics, and simulation. Informed by IHAF, a HM was developed using real-time Emergency Department data, time-series forecasting, and discrete-event simulation. The application started with patient questionnaires to support problem definition and to act as a formative evaluation, and was subsequently evaluated using staff interviews. Evaluation of the application found multiple examples where the objectives of people or sub-systems are not aligned, resulting in inefficiencies and other quality problems, which are characteristic of complex adaptive sociotechnical systems. Synthesis of the literature, the formative evaluation, and the final evaluation found significant themes which can act as antecedents or evaluation criteria for future real-time HM studies in sociotechnical systems, in particular in healthcare. The generic utility of IHAF is emphasised for supporting future applications in similar domains