Macquarie University is seeking an experienced senior researcher to establish a new AI in healthcare research stream. You’ll be part of the Centre for Health Informatics (CHI), at the Australian Institute of Health Innovation, which leads the Centre of Research Excellence in Digital Health (CRE). You’ll collaborate with a highly experienced team of
Yin K, Jung J, Coiera E, Laranjo L, Blandford A, Khoja A, Tai W, Phillips DP, Lau AYS. Patient Work and Contexts – A Scoping Review. JMIR Preprint
Background: Self-management (or self-care) is widely promoted but many patients struggle to practise it effectively. Moreover, few studies have analysed the nature and volume of work required from patients in self-care and how such work fits into the context of their daily life. Objective: To review the characteristics of patient work in adult patients. Patient
Come and study Consumer Digital Health through the PhD Scholarship – Digital health for patients and healthcare consumers. The Phd opportunity is part of the CRE in Interactive Digital Technology to Transform Australia’s Chronic Disease Outcomes led by The University of Melbourne. You’ll be supervised by Dr Annie Lau, CRE Chief Investigator, and Consumer Informatics
The Journal of the American Medical Informatics Association (JAMIA) published recent work by Enrico Coiera and colleagues from Macquarie University and the University of Health Sciences (Austria) of a narrative review of literature on research replication challenges. The review concluded that the cost of poor replication is a weakening in the quality of published research
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
Kocaballi AB, Coiera E, Tong HL, White S, Quiroz JC, Rezazadegan F, Willcock S, Laranjo L. A network model of activities in primary care consultations. Journal of the American Medical Informatics Association. 2019; 26(10):1074-82.
Objective The objective of this study is to characterize the dynamic structure of primary care consultations by identifying typical activities and their inter-relationships to inform the design of automated approaches to clinical documentation using natural language processing and summarization methods. Materials and Methods This is an observational study in Australian general practice involving 31 consultations
Just as the map is not the territory, so too an algorithm is never the care that is given. Algorithms, neural networks, guidelines, and protocols—these all can only ever model aspects of a reality that is always more complex and fickle. As we move into a world dominated by algorithms and machine-learned clinical approaches, we
Kocaballi AB, Berkovsky S, Quiroz J, Laranjo da Silva L, Tong HL, Rezadegan D, Briatore A, Coiera E. The Personalization of conversational agents in healthcare: a systematic review. Journal of Medical Internet Research. 2019; 21(11):e15360.
Background: The personalization of conversational agents with natural language user interfaces is seeing increasing use in health care applications, shaping the content, structure, or purpose of the dialogue between humans and conversational agents. Objective: The goal of this systematic review was to understand the ways in which personalization has been used with conversational agents in
Kocaballi AB, Laranjo L, Coiera E. Understanding and measuring user experience in conversational interfaces. Interacting with Computers. 2019; 31(2):192-207.
Although various methods have been developed to evaluate conversational interfaces, there has been a lack of methods specifically focusing on evaluating user experience. This paper reviews the understandings of user experience (UX) in conversational interfaces literature and examines the six questionnaires commonly used for evaluating conversational systems in order to assess the potential suitability of
Quiroz, J.C., Laranjo, L., Kocaballi, A.B. et al. Challenges of developing a digital scribe to reduce clinical documentation burden. npj Digit. Med. 2, 114 (2019)
Clinicians spend a large amount of time on clinical documentation of patient encounters, often impacting quality of care and clinician satisfaction, and causing physician burnout. Advances in artificial intelligence (AI) and machine learning (ML) open the possibility of automating clinical documentation with digital scribes, using speech recognition to eliminate manual documentation by clinicians or medical
Yin K, Laranjo L, Tong HL, Lau AY, Kocaballi AB, Martin P, Vagholkar S, Coiera E. Context-Aware Systems for Chronic Disease Patients: Scoping Review. J Med Internet Res 2019;21(6):e10896
Background: Context-aware systems, also known as context-sensitive systems, are computing applications designed to capture, interpret, and use contextual information and provide adaptive services according to the current context of use. Context-aware systems have the potential to support patients with chronic conditions; however, little is known about how such systems have been utilized to facilitate patient
Magrabi F, Ammenwerth E, McNair JB, De Keizer NF, Hyppönen H, Nykänen P, Rigby M, Scott PJ, Vehko T, Wong ZS-Y, Georgiou A. Artificial Intelligence in Clinical Decision Support: Challenges for Evaluating AI and Practical Implications. Yearb Med Inform. 2019; 28(01):128-34.
OBJECTIVES: This paper draws attention to: i) key considerations for evaluating artificial intelligence (AI) enabled clinical decision support; and ii) challenges and practical implications of AI design, development, selection, use, and ongoing surveillance. METHOD: A narrative review of existing research and evaluation approaches along with expert perspectives drawn from the International Medical Informatics Association (IMIA)
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
Magrabi F, Habli I, Sujan M, Wong D, Thimbleby H, Baker M, Coiera E. Why is it so difficult to govern mobile apps in healthcare? BMJ Health and Care Informatics. 2019.
Mobile apps have become a convenient way to provide health information and communication services directly in the hands of clinicians and consumers. Apps can be used to support consumers in a variety of health tasks to manage chronic diseases, support lifestyle changes and in self-diagnosis. For clinicians, they can improve access to patient information and