Prerequisites for International Exchanges of Health Information for Record Research: Comparison of Australian, Austrian, Finnish, Swiss, and US Policies.

The policies that address health information exchanges for research purposes in Australia, Austria, Finland, Switzerland, and the USA apply accountability and/or adequacy to protect privacy. Specific requirements complicate the exchanges: inform data subjects of data use purposes; assure that the subjects are no longer identifiable; destroy the data in the end; and not to use cloud computing without specific permission.

The Role of Digital Engagement in the Self-Management of Type 2 Diabetes.

Mobile tablet devices and applications have the potential to help type 2 diabetes patients in the self-management of their disease. However, users must be equipped with an appropriate level of digital literacy in order to use the tools and technologies effectively. This study reports from an exploratory mobile health (mHealth) pilot program that was conducted at a large, local clinic in Canberra, Australia.

Benchmarking clinical speech recognition and information extraction: new data, methods, and evaluations.

Background: Over a tenth of preventable adverse events in health care are caused by failures in information flow. These failures are tangible in clinical handover; regardless of good verbal handover, from two-thirds to all of this information is lost after 3-5 shifts if notes are taken by hand, or not at all. Speech recognition and information extraction provide a way to fill out a handover form for clinical proofing and sign-off.

Capturing patient information at nursing shift changes: methodological evaluation of speech recognition and information extraction.

Objective: We study the use of speech recognition and information extraction to generate drafts of Australian nursing-handover documents.

Methods: Speech recognition correctness and clinicians' preferences were evaluated using 15 recorder-microphone combinations, six documents, three speakers, Dragon Medical 11, and five survey/interview participants. Information extraction correctness evaluation used 260 documents, six-class classification for each word, two annotators, and the CRF++ conditional random field toolkit.

Machine intelligence for health information: capturing concepts and trends in social media via query expansion.

Introduction: We aim to improve retrieval of health information from Twitter.

Background: The popularity of social media and micro-blogs has emphasised their potential for knowledge discovery and trend building. However, capturing and relating concepts in these short-spoken and lexically extensive sources of information requires search engines with increasing intelligence.