Addendum to Informatics for Health 2017: Advancing both science and practice

This article presents presentation and poster abstracts that were mistakenly omitted from the original publication

Minimizing hypoglycemia while maintaining glycemic control in diabetes

In the accompanying Perspective, Cryer identifies a number of different areas where therapeutic interventions have the potential to reduce hypoglycemia without compromising glycemic control. Some approaches provide well defined clinical benefits, a few offer dramatic reductions in hypoglycemia but remain out of reach for most people while others, although promising have yet to be properly evaluated. (Table 1) In this Perspective, I examine the evidence which underpins these interventions. It is beyond the scope of this article to review the data for each potential intervention in detail but the reader is directed to the appropriate source where appropriate. The Perspective focuses on treatment of Type 1 diabetes as most of the potential specific therapies have been evaluated in this group although I have commented in relation to recent trials of intensive therapy in Type 2 diabetes

Patient and public attitudes to and awareness of clinical practice guidelines : a systematic review with thematic and narrative syntheses

Article Accepted Date: 15 July 2014 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Acknowledgements The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement n° 258583 (DECIDE project). The Health Services Research Unit, Aberdeen University, is funded by the Chief Scientist Office of the Scottish Government Health Directorates. The authors accept full responsibility for this paper and the views expressed in it are those of the authors and do not necessarily reflect those of the Chief Scientist Office. NS receives funding through a Knowledge Translation Fellowship from the Canadian Institutes of Health Research. No funding bodies had a role in the manuscript. We would like to thank Healthcare Improvement Scotland and the University of Dundee for support, including access to literature. We would also like to thank Lorna Thompson (Healthcare Improvement Scotland), for her help with the protocol for this review.Peer reviewedPublisher PD

Biomedical Informatics Applications for Precision Management of Neurodegenerative Diseases

Modern medicine is in the midst of a revolution driven by “big data,” rapidly advancing computing power, and broader integration of technology into healthcare. Highly detailed and individualized profiles of both health and disease states are now possible, including biomarkers, genomic profiles, cognitive and behavioral phenotypes, high-frequency assessments, and medical imaging. Although these data are incredibly complex, they can potentially be used to understand multi-determinant causal relationships, elucidate modifiable factors, and ultimately customize treatments based on individual parameters. Especially for neurodegenerative diseases, where an effective therapeutic agent has yet to be discovered, there remains a critical need for an interdisciplinary perspective on data and information management due to the number of unanswered questions. Biomedical informatics is a multidisciplinary field that falls at the intersection of information technology, computer and data science, engineering, and healthcare that will be instrumental for uncovering novel insights into neurodegenerative disease research, including both causal relationships and therapeutic targets and maximizing the utility of both clinical and research data. The present study aims to provide a brief overview of biomedical informatics and how clinical data applications such as clinical decision support tools can be developed to derive new knowledge from the wealth of available data to advance clinical care and scientific research of neurodegenerative diseases in the era of precision medicine

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