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  3. Digital Health and Informatics Methods
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Digital Health and Informatics Methods (INFO90001) // Further information

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Further information

  • Texts

    Prescribed texts

    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

    CAREERS/INDUSTRY LINKS

    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:

    TypeName
    CourseGraduate Certificate in Health Informatics and Digital Health
    CourseDoctor of Philosophy - Engineering
    CoursePh.D.- Engineering
    CourseMaster of Philosophy - 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: 6 March 2024