Webinar: Harnessing Digital Technologies to Enhance Patient Safety
The second webinar in the PCHSS 2020-2021 Webinar Series was on Harnessing Digital Technologies to Enhance Patient Safety.
Event summary
When we strive for patient safety, we aim for the absence of preventable harm to a patient in the provision of care and a reduction in the risk of unnecessary harm associated with healthcare. From better training to improvements in workplace culture, there are countless ways to curb harm and the risk of adverse care events, but undoubtedly one of the most promising is the increasing use of digital technologies.
This webinar featured presentations from experts in the fields of patient safety and artificial intelligence (AI). To begin, Professor Johanna Westbrook spoke on her research on electronic medications management (eMM) systems in Australian hospitals. Specifically, she discussed her findings from an innovative stepped-wedge trial designed to determine whether medication error rates significantly declined following implementation of an eMM system in a tertiary paediatric hospital. From there, Associate Professor Shlomo Berkovsky discussed his work on the use of AI for categorising frail elderly patients. Frailty is a clinical state in which the ability of people to cope with everyday stressors is compromised by age-associated declines. The goal of this work is to improve decision-making about treatments and procedures appropriate for each category of frail elderly patient.
The conversation was moderated by Farah Magrabi, Associate Professor at the Australian Institute of Health Innovation, Macquarie University.
Speakers
Professor Johanna Westbrook is Lead Investigator for the PCHSS’s Shared Health Information Research Stream and the Director of the Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University. Her research has led to significant advances in our understanding of how clinical information systems deliver (or fail to deliver) expected benefits and supported translation of this evidence into policy, practice and IT system changes.
Associate Professor Shlomo Berkovsky is an investigator on the PCHSS’s Big Data and the Quality, Effectiveness and Cost of Care Research Stream and the leader of the Precision Health Research Stream at Macquarie University. His research focuses on the use of machine learning methods to develop patient models and personalised predictions of diagnosis and care. Shlomo also studies how sensors and physiological responses can predict medical conditions and how clinicians and patients interact with health technologies.