An evaluation of a data linkage training workshop for research ethics committees

Health data linkage research in Australia remains challenging

Data linkage is a valuable technique for uniting information from multiple sources that relates to the same person, place, family or event. Despite its value, establishing such linkages in Australia remains challenging. Existing policies are a missed opportunity for research and innovation and engender a negative attitude among researchers when considering data linkage as a research means. Greater leadership from the Population Health Research Network and cooperation from data custodians and Human Research Ethics Committees are necessary to access optimally Australia's enormous data potential.

The Population Health Research Network - Population Data Centre Profile

The Population Health Research Network (PHRN) is an Australian data linkage infrastructure capable of securely and safely linking and integrating data collections from a wide range of sources. It is an example of a national data linkage infrastructure in a country with a federated system of government. This population data centre profile describes Australia's unique approach to enabling access to linked data from single jurisdictions and from multiple jurisdictions. It covers the background to the establishment of the PHRN as well as information about how it operates today including operating models, governance, data, data linkage and data access. Some of the challenges of data linkage across jurisdictions are also discussed.

Ethical Considerations in Using Social Media to Engage Research Participants: Perspectives of Australian Researchers and Ethics Committee Members

Researchers increasingly use social media (SM) to recruit, retain, and trace participants, yet empirical literature investigating the ethics of engaging participants via SM is lacking. We conducted a survey of 401 Australian researchers and human research ethics committee (HREC) members to examine their experience, attitudes, and ethical concerns toward engaging participants via SM. Data revealed that researchers and HREC members share similar concerns and attitudes about using SM in general and in research. We identified a strong demand for additional support, training, and guidance on SM research ethics. This need reflects researchers' and HREC members' limited confidence and knowledge of ethical issues in this context and a lack of awareness of available SM-specific ethical guidelines.

Clinical Research Informatics: Supporting the Research Study Lifecycle

Objectives: The primary goal of this review is to summarize significant developments in the field of Clinical Research Informatics (CRI) over the years 2015-2016. The secondary goal is to contribute to a deeper understanding of CRI as a field, through the development of a strategy for searching and classifying CRI publications. Methods: A search strategy was developed to query the PubMed database, using medical subject headings to both select and exclude articles, and filtering publications by date and other characteristics. A manual review classified publications using stages in the "research study lifecycle", with key stages that include study definition, participant enrollment, data management, data analysis, and results dissemination. Results: The search strategy generated 510 publications. The manual classification identified 125 publications as relevant to CRI, which were classified into seven different stages of the research lifecycle, and one additional class that pertained to multiple stages, referring to general infrastructure or standards. Important cross-cutting themes included new applications of electronic media (Internet, social media, mobile devices), standardization of data and procedures, and increased automation through the use of data mining and big data methods. Conclusions: The review revealed increased interest and support for CRI in large-scale projects across institutions, regionally, nationally, and internationally. A search strategy based on medical subject headings can find many relevant papers, but a large number of non-relevant papers need to be detected using text words which pertain to closely related fields such as computational statistics and clinical informatics. The research lifecycle was useful as a classification scheme by highlighting the relevance to the users of clinical research informatics solutions.