Publications | Digital Health

Rockett RJ, Arnott A, Lam C, Sadsad R, Timms V, Gray K-A, Eden J-S, Chang S, Gall M, Draper J, Sim EM, Bachmann NL, Carter I, Basile K, Byun R, O’Sullivan MV, Chen SCA, Maddocks S, Sorrell TC, Dwyer DE, Holmes EC, Kok J, Prokopenko M, Sintchenko V. Revealing COVID-19 transmission in Australia by SARS-CoV-2 genome sequencing and agent-based modelling. Nature Medicine 2020; 26(9):1398-1404.

In January 2020, a novel betacoronavirus (family Coronaviridae), named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified as the etiological agent of a cluster of pneumonia cases occurring in Wuhan City, Hubei Province, China1,2. The disease arising from SARS-CoV-2 infection, coronavirus disease 2019 (COVID-19), subsequently spread rapidly causing a worldwide pandemic. Here we examine

Pederson M, Verspoor K, Jenkinson M, Law M, Abbott DF, Jackson GD (2020). Artificial Intelligence for clinical decision support in neurology. Brain Communications.

Artificial intelligence is one of the most exciting methodological shifts in our era. It holds the potential to transform healthcare as we know it, to a system where humans and machines work together to provide better treatment for our patients. It is now clear that cutting edge artificial intelligence models in conjunction with high-quality clinical

Liaw ST, Liyanage H, Kuziemsky C, Terry AL, Schreiber R, Jonnagaddala J, de Lusignan S. Ethical Use of Electronic Health Record Data and Artificial Intelligence: Recommendations of the Primary Care Informatics Working Group of the International Medical Informatics Association. Yearbook of Medical Informatics: Apr 2020

Objective: To create practical recommendations for the curation of routinely collected health data and artificial intelligence (AI) in primary care with a focus on ensuring their ethical use. Methods: We defined data curation as the process of management of data throughout its lifecycle to ensure it can be used into the future. We used a

Jonnagaddala J, Guo GN, Batongbacal S, Marcelo A, Liaw ST. Adoption of enterprise architecture for healthcare in AeHIN member countries. BMJ Health & Care Informatics 2020; 27: e100136. DOI: 10.1136/bmjhci-2020-100136

Background Healthcare organisations are undergoing a major transformational shift in the use of information and digital health technologies. Enterprise architecture (EA) has been incrementally adopted in many healthcare organisations globally to facilitate this change. EA can increase the effectiveness of an organisation’s digital health capabilities and resources. However, little is known about the status of

Godinho MA, Jonnagaddala J, Gudi N, Islam R, Narasimhan P, Liaw ST. mHealth for Integrated People-Centred Health Services in the Western Pacific: A Systematic Review. Int J Med Informatics 2020; 142: 104259. DOI: https://doi.org/10.1016/j.ijmedinf.2020.104259.

Objective This review aimed to examine how mobile health (mHealth) to support integrated people-centred health services has been implemented and evaluated in the World Health Organization (WHO) Western Pacific Region (WPR). Methods Eight scientific databases were searched. Two independent reviewers screened the literature in title and abstract stages, followed by full-text appraisal, data extraction, and

Godinho MA, Ashraf MM, Narasimhan P, Liaw ST. Community Health Alliances as social enterprises that digitally engage citizens and integrate services: a case study in Southwestern Sydney (Protocol). Digital Health 2020; 6: 1-8

South Western Sydney (SWS) is one of the fastest growing regions in the state of New South Wales (Australia). Much of the population live in local government areas (LGAs) with levels of disadvantage higher than the state average, with a predominance of non-communicable and chronic diseases that are typically associated with age-related and behavioural factors.

Degeling C, Carter SM, McAnulty J, Sintchenko V, Braunack-Mayer A, Yarwood T, Johnson J, Gilbert GL. Community perspectives on the benefits and risks of technologically enhanced communicable disease surveillance systems: A report on four community juries. BMC Medical Ethics 2020; 21(1):31.

Background: Outbreaks of infectious disease cause serious and costly health and social problems. Two new technologies – pathogen whole genome sequencing (WGS) and Big Data analytics – promise to improve our capacity to detect and control outbreaks earlier, saving lives and resources. However, routinely using these technologies to capture more detailed and specific personal information

Clark, J., Glasziou, P., Del Mar, C., Bannach-Brown, A., Stehlik, P. and Scott, A.M., 2020. A full systematic review was completed in 2 weeks using automation tools: a case study. Journal of Clinical Epidemiology, 121, pp.81-90.

Background and Objectives: Systematic reviews (SRs) are time and resource intensive, requiring approximately 1 year from protocol registration to submission for publication. Our aim was to describe the process, facilitators, and barriers to completing the first 2-week full SR. Study Design and Setting: We systematically reviewed evidence of the impact of increased fluid intake, on

Clark J, Sanders S, Carter M, Honeyman D, Cleo G, Auld Y, et al. Improving the translation of search strategies using the polyglot search translator: A randomized controlled trial. J Med Libr Assoc. 2020;108(2):195-207.

Background: Searching for studies to include in a systematic review (SR) is a time- and labor-intensive process with searches of multiple databases recommended. To reduce the time spent translating search strings across databases, a tool called the Polyglot Search Translator (PST) was developed. The authors evaluated whether using the PST as a search translation aid

Chen J, Lyell D, Laranjo L, Magrabi F. Effect of Speech Recognition on Problem Solving and Recall in Consumer Digital Health Tasks: Controlled Laboratory Experiment. J Med Internet Res 2020;22(6):e14827 doi: 10.2196/14827[published Online First: Epub Date]

Background: Recent advances in natural language processing and artificial intelligence have led to widespread adoption of speech recognition technologies. In consumer health applications, speech recognition is usually applied to support interactions with conversational agents for data collection, decision support, and patient monitoring. However, little is known about the use of speech recognition in consumer health

Akbar S, Coiera, Enrico, Magrabi F. Safety concerns with consumer-facing mobile health applications and their consequences: a scoping review. Journal of the American Medical Informatics Association. 2019; 27(2):330-40.

Objective To summarize the research literature about safety concerns with consumer-facing health apps and their consequences. Materials and Methods We searched bibliographic databases including PubMed, Web of Science, Scopus, and Cochrane libraries from January 2013 to May 2019 for articles about health apps. Descriptive information about safety concerns and consequences were extracted and classified into

Yang Y, Walker TM, Walker AS, Wilson DJ, Peto TEA, Crook DW, Shamout F; CRyPTIC Consortium, Zhu T, Clifton DA. DeepAMR for predicting co-occurent resistance of Mycobacterium tuberculosis. Bioinformatics 2019;35(18):3240-3249.

Motivation: Resistance co-occurrence within first-line anti-tuberculosis (TB) drugs is a common phenomenon. Existing methods based on genetic data analysis of Mycobacterium tuberculosis (MTB) have been able to predict resistance of MTB to individual drugs, but have not considered the resistance co-occurrence and cannot capture latent structure of genomic data that corresponds to lineages. Results: We

Vo K, Jonnagaddala J and Liaw ST. Statistical supervised meta-ensemble algorithm for medical record linkage. J Biomed Inform. (online publication). 2019. https://doi.org/10.1016/j.jbi.2019.103220

Identifying unique patients across multiple care facilities or services is a major challenge in providing continuous care and undertaking health research. Identifying and linking patients without compromising privacy and security is an emerging issue in the big data era. The large quantity and complexity of the patient data emphasize the need for effective linkage methods

O’Connor AM, Tsafnat G, Gilbert SB, Thayer KA, Shemilt I, Thomas J, Glasziou P, Wolfe MS. Still moving toward automation of the systematic review process: a summary of discussions at the third meeting of the International Collaboration for Automation of Systematic Reviews (ICASR). Systematic Reviews. 2019; 8(1):57.

The third meeting of the International Collaboration for Automation of Systematic Reviews (ICASR) was held 17–18 October 2017 in London, England. ICASR is an interdisciplinary group whose goal is to maximize the use of technology for conducting rapid, accurate, and efficient systematic reviews of scientific evidence. The group seeks to facilitate the development and widespread

Liyanage H, Liaw ST, Jonnagaddala J, et al. Artificial Intelligence in Primary Health Care: Perceptions, Issues, and Challenges. Yearbook of Medical Informatics 2019. http://dx.doi.org/10.1055/s-0039-1677901

Background: Artificial intelligence (AI) is heralded as an approach that might augment or substitute for the limited processing power of the human brain of primary health care (PHC) professionals. However, there are concerns that AI-mediated decisions may be hard to validate and challenge, or may result in rogue decisions. Objective: To form consensus about perceptions,