Associate Professor Farah Magrabi and Professor Wendy Rogers (both from Macquarie University) were invited to contribute to and review the Human Factors and Ergonomics in Healthcare AI White Paper produced by the Chartered Institute of Ergonomics and Human Factors (CIEHF). The aim of the paper is to promote systems thinking among those who develop, regulate,
Bringing AI into the real world of healthcare The National Health and Medical Research Council (NHMRC) recently awarded internationally recognised informatics expert Professor Enrico Coiera $1.5 million for the project: Bringing artificial intelligence into the real world of healthcare – the implementation challenge. The five-year research program will focus on three foundational challenges to bringing artificial
Challenges for Evaluating AI and Practical Implications Artificial intelligence (AI) promises to transform clinical decision-making processes as it has the potential to harness the vast amounts of genomic, biomarker, and phenotype data that is being generated across the health system including from health records and delivery systems, to improve the safety and quality of care
Genomic sequencing has proven vital in detecting the source and variants of COVID-19, improving contact tracing, reducing disease transmission and ensuring health security for Australia. But genomic sequencing isn’t new and researchers from the NHMRC Centre of Digital Health (CRE) have been investigating it’s use in cases from public surveillance of Salmonella Typhimurium, predicting antibiotic
Patients should be informed Media release from BMJ An in-depth analysis of more than 20,000 health related mobile applications (mHealth apps) published by The BMJ today (16 June 2021) finds “serious problems with privacy and inconsistent privacy practices.” The researchers say the collection of personal user information is “a pervasive practice” and that patients “should
Can co-designing artificial intelligence tools with general practitioners deliver better patient outcomes and what impact will it have on Doctors? And what about the healthcare system? We took it to the test in a study with general practitioners simulating an AI documentation assistant for use in patient consultations. While artificial intelligence is advancing rapidly across
Healthcare 4.0 (H4.0) adapts principles and applications from the Industry 4.0 movement to healthcare, enabling real-time customization of care to patients and professionals. As such, H4.0 can potentially support resilient performance in healthcare systems, which refers to their adaptive capacity to cope with complexity. This paper explores the impact of ten H4.0 digital technologies on
Objective: To examine how and to what extent medical devices using machine learning (ML) support clinician decision making. Methods: We searched for medical devices that were (1) approved by the US Food and Drug Administration (FDA) up till February 2020; (2) intended for use by clinicians; (3) in clinical tasks or decisions and (4) used
Background: As a major public health crisis, the novel coronavirus disease 2019 (COVID-19) pandemic demonstrates the urgent need for safe, effective, and evidence-based implementations of digital health. The urgency stems from the frequent tendency to focus attention on seemingly high promising digital health interventions despite being poorly validated in times of crisis. Aim: In this
Background: Overuse of proton pump inhibitors (PPIs) – frequently used for relieving symptoms of gastroesophageal reflux disease (GORD) – raises long-term safety concerns, warranting evidence-based non-drug interventions. We conducted a systematic review to evaluate the effect of head-of-bed elevation on relieving symptoms of GORD in adults. Methods: We included controlled trials comparing the effect of
Patients are increasingly encouraged to take an active role in managing their health and health care. New technologies, cultural shifts, trends in healthcare delivery, and policies have brought to the forefront the “work” patients, families, and other non-professionals perform in pursuit of health. Volume 1 provides theoretical and methodological foundation for the emerging discipline of
Domain pretraining followed by task fine-tuning has become the standard paradigm for NLP tasks, but requires in-domain labelled data for task fine-tuning. To overcome this, we propose to utilise domain unlabelled data by assigning pseudo labels from a general model. We evaluate the approach on two clinical STS datasets, and achieve r= 0.80 on N2C2-STS.
In this paper, we apply pre-trained language models to the Semantic Textual Similarity (STS) task, with a specific focus on the clinical domain. In low-resource setting of clinical STS, these large models tend to be impractical and prone to overfitting. Building on BERT, we study the impact of a number of model design choices, namely
Background: Approximately 15% of community-prescribed antibiotics are used in treating urinary tract infections (UTIs). Increase in antibiotic resistance necessitates considering alternatives. Aim: To assess the impact of increased fluid intake in individuals at risk for UTIs, for impact on UTI recurrence (primary outcome), antimicrobial use, and UTI symptoms (secondary outcomes). Design and setting: A systematic
Digital health applications (apps) have the potential to improve health behaviors and outcomes. We aimed to examine the effectiveness of a consumer web-based app linked to primary care electronic health records (EHRs). CONNECT was a multicenter randomized controlled trial involving patients with or at risk of cardiovascular disease (CVD) recruited from primary care (Clinical Trial
