Subjects taught in 2022 will be in one of three delivery modes: Dual-Delivery, Online or On Campus.
From 2023 most subjects will be taught on campus only with flexible options limited to a select number of postgraduate programs and individual subjects.
To learn more, visit COVID-19 course and subject delivery.
There are no specifically prescribed or recommended texts for this subject.
- Subject notes
LEARNING AND TEACHING METHODS
This subject is offered in semester 2 each year, as a 3 hour class one evening each week over a 12 week period, including lectures, tutorials and small group activities. Opportunities are provided for online interaction during class using students’ personal internet-connected devices.
Classroom teaching is complemented by a subject website in the University Learning Management System. Students unable to attend class on campus can participate each week, by going online to access lecture slides and recordings, undertake practical activities, and complete assessable work.
INDICATIVE KEY LEARNING RESOURCES
This subject has no textbook. Students have access to electronic full-text of recommended readings, including current journal articles, government documents and industry reports. Examples:
Dahlin, S., Eriksson, H., & Raharjo, H. (2019). Process mining for quality improvement: propositions for practice and research. Quality Management in Healthcare, 28(1), 8-14.
Enam, A., Torres-Bonilla, J., & Eriksson, H. (2018). Evidence-based evaluation of eHealth interventions: systematic literature review. Journal of Medical Internet Research, 20(11), e10971. Full-text open access URL: https://www.jmir.org/2018/11/e10971
Gianfrancesco, M. A., Tamang, S., Yazdany, J., & Schmajuk, G. (2018). Potential biases in machine learning algorithms using electronic health record data. JAMA Internal Medicine, 178(11), 1544-1547.
Van de Velde, S., Kunnamo, I., Roshanov, P., Kortteisto, T., Aertgeerts, B., Vandvik, P. O., & Flottorp, S.(2018). The GUIDES checklist: development of a tool to improve the successful use of guideline-based computerised clinical decision support. Implementation Science, 13(1), 86.
Wong, A., Young, A. T., Liang, A. S., Gonzales, R., Douglas, V. C., & Hadley, D. (2018). Development and validation of an electronic health record–based machine learning model to estimate delirium risk in newly hospitalized patients without known cognitive impairment. JAMA Network Open, 1(4), e181018. Full-text open access URL: https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2695078
This subject provides advanced knowledge and practical skills to work in digital health. This subject is offered jointly by the Faculty of Engineering and the Faculty of Medicine, Dentistry and Health Sciences, and makes local and international links with accomplished researchers and with experts from public and private sector organisations.
- Related Handbook entries
This subject contributes to the following:
Type Name Course Doctor of Philosophy - Engineering Course Graduate Certificate in Health Informatics and Digital Health Course Master of Philosophy - Engineering Course Ph.D.- Engineering
- Available through the Community Access Program
About the Community Access Program (CAP)
This subject is available through the Community Access Program (also called Single Subject Studies) which allows you to enrol in single subjects offered by the University of Melbourne, without the commitment required to complete a whole degree.
Entry requirements including prerequisites may apply. Please refer to the CAP applications page for further information.
Additional information for this subject
Subject coordinator approval required.
- Available to Study Abroad and/or Study Exchange Students
This subject is available to students studying at the University from eligible overseas institutions on exchange and study abroad. Students are required to satisfy any listed requirements, such as pre- and co-requisites, for enrolment in the subject.
Last updated: 28 April 2022