Evaluation of patient perception towards dynamic health data sharing using blockchain based digital consent with the Dovetail digital consent application : a cross sectional exploratory study

Background New patient-centric integrated care models are enabled by the capability to exchange the patient’s data amongst stakeholders, who each specialise in different aspects of the patient’s care. This requires a robust, trusted and flexible mechanism for patients to offer consent to share their data. Furthermore, new IT technologies make it easier to give patients more control over their data, including the right to revoke consent. These characteristics challenge the traditional paper-based, single-organisation-led consent process. The Dovetail digital consent application uses a mobile application and blockchain based infrastructure to offer this capability, as part of a pilot allowing patients to have their data shared amongst digital tools, empowering patients to manage their condition within an integrated care setting. Objective To evaluate patient perceptions towards existing consent processes, and the Dovetail blockchain based digital consent application as a means to manage data sharing in the context of diabetes care. Method Patients with diabetes at a General Practitioner practice were recruited. Data were collected using focus groups and questionnaires. Thematic analysis of the focus group transcripts and descriptive statistics of the questionnaires was performed. Results There was a lack of understanding of existing consent processes in place, and many patients did not have any recollection of having previously given consent. The digital consent application received favourable feedback, with patients recognising the value of the capability offered by the application. Patients overwhelmingly favoured the digital consent application over existing practice. Conclusions Digital consent was received favourably, with patients recognising that it addresses the main limitations of the current process. Feedback on potential improvements was received. Future work includes confirmation of results in a broader demographic sample and across multiple conditions

Unsupervised machine learning for developing personalised behaviour models using activity data

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. The goal of this study is to address two major issues that undermine the large scale deployment of smart home sensing solutions in people’s homes. These include the costs associated with having to install and maintain a large number of sensors, and the pragmatics of annotating numerous sensor data streams for activity classification. Our aim was therefore to propose a method to describe individual users’ behavioural patterns starting from unannotated data analysis of a minimal number of sensors and a ”blind” approach for activity recognition. The methodology included processing and analysing sensor data from 17 older adults living in community-based housing to extract activity information at different times of the day. The findings illustrate that 55 days of sensor data from a sensor configuration comprising three sensors, and extracting appropriate features including a “busyness” measure, are adequate to build robust models which can be used for clustering individuals based on their behaviour patterns with a high degree of accuracy (>85%). The obtained clusters can be used to describe individual behaviour over different times of the day. This approach suggests a scalable solution to support optimising the personalisation of care by utilising low-cost sensing and analysis. This approach could be used to track a person’s needs over time and fine-tune their care plan on an ongoing basis in a cost-effective manner

Rahapeliriippuvuus hallintaan -menetelmäkoulutus

This paper proposes an efficient algorithm to compress the cubes in the progress of the parallel data cube generation. This low overhead compression mechanism provides block-by-block and record-by-record compression by using tuple difference coding techniques, thereby maximizing the compression ratio and minimizing the decompression penalty at run-time. The experimental results demonstrate that the typical compression ratio is about 30:1 without sacrificing running time. This paper also demonstrates that the compression method is suitable for Hilbert Space Filling Curve, a mechanism widely used in multi-dimensional indexing

Reordering Columns for Smaller Indexes

Column-oriented indexes-such as projection or bitmap indexes-are compressed by run-length encoding to reduce storage and increase speed. Sorting the tables improves compression. On realistic data sets, permuting the columns in the right order before sorting can reduce the number of runs by a factor of two or more. Unfortunately, determining the best column order is NP-hard. For many cases, we prove that the number of runs in table columns is minimized if we sort columns by increasing cardinality. Experimentally, sorting based on Hilbert space-filling curves is poor at minimizing the number of runs.Comment: to appear in Information Science

Dethroning historical reputations: Universities, museums and the commemoration of benefactors

The campaigns in universities across the world to reject, rename and remove historic benefactions have brought the present into collision with the past. In Britain the attempt to remove a statue of one of Oxford’s most famous benefactors, the imperialist Cecil Rhodes, has spread to other universities and their benefactors, and now also affects civic monuments and statues in towns and cities across the country. In the United States, memorials to leaders of the Confederacy in the American Civil War and to other slaveholders have been the subject of intense dispute. Should we continue to honour benefactors and historic figures whose actions are now deemed ethically unacceptable? How can we reconcile the views held by our ancestors with those we now hold today? Should we even try, acknowledging, in the words of the novelist L. P. Hartley, that ‘the past is another country; they do things differently there’? The essays in this interdisciplinary collection are drawn from a conference at the Institute of Historical Research in the University of London. Historians, fundraisers, a sociologist and a museum director examine these current issues from different perspectives, with an introductory essay by Sir David Cannadine, president of the British Academy. Together they explore an emerging conflict between the past and present, history and ideology, and benefactors and their critics