2021 Brilliant women in Digital Health – Farah Magrabi

The CRE in Digital Health congratulates Associate Professor Farah Magrabi for being recognised in the Telstra Health’s 2021 Brilliant Women in Digital Health Awards. In its inaugural year, the 2021 Brilliant Women in Digital Health award initiative set out to recognise and celebrate women in digital health for their outstanding achievements, while raising awareness about

Wang Y, Coiera E, Magrabi F. Can Unified Medical Language System–based semantic representation improve automated identification of patient safety incident reports by type and severity? Journal of the American Medical Informatics Association. 2020.

Objective The study sought to evaluate the feasibility of using Unified Medical Language System (UMLS) semantic features for automated identification of reports about patient safety incidents by type and severity. Materials and Methods Binary support vector machine (SVM) classifier ensembles were trained and validated using balanced datasets of critical incident report texts (n_type = 2860, n_severity = 1160) collected

Why is it so difficult to govern mobile apps in healthcare?

In a recent article led by CRE Investigator, Associate Farah Magrabi published in BMJ Health and Care Informatics, Magrabi discusses the difficulty of governing mobile apps in healthcare and how these issues can be addressed. Mobile apps have become a convenient way to provide health information and communication services directly in the hands of clinicians

Wang Y, Coiera E, Magrabi F. Using convolutional neural networks to identify patient safety incident reports by type and severity. Journal of the American Medical Informatics Association. 2019; 26(12):1600-8.

Objective To evaluate the feasibility of a convolutional neural network (CNN) with word embedding to identify the type and severity of patient safety incident reports. Materials and Methods A CNN with word embedding was applied to identify 10 incident types and 4 severity levels. Model training and validation used data sets (n_type = 2860, n_severity = 1160) collected from

Akbar S, Coiera, E, Magrabi F. Safety concerns with consumer-facing mobile health applications and their consequences: a scoping review. Journal of the American Medical Informatics Association. 2019.

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