Successful Digital Healthcare Solutions (INFO90012)

Graduate courseworkPoints: 12.5Not available in 2025

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Overview

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In spite of the many types of technologies that exist for bringing about practice change, such as virtual care, predictive analytics, and clinical decision support, most digital healthcare innovations are never implemented into clinical care.  This course aims to introduce learners who want to play an active role in the digital transformation of healthcare to the knowledge and skills needed for digital healthcare innovations to succeed in healthcare settings. 

Students learn through an extended case study from the rapidly advancing field of artificial intelligence (AI) enhanced clinical decision support (CDS). Students work progressively through the case to build evidence for an implementable digital healthcare solution, learning: 1) how to select a CDS solution that addresses a demonstrable clinical problem; 2) how to design, develop, and validate the solution; and 3) how to create an implementation, evaluation, and monitoring plan.  Key resources include published frameworks and real-world examples, and learning includes hands-on activities in multidisciplinary teams.  

Using the principles of a learning health system, the subject will prepare students to address real-world factors that prevent adoption in healthcare settings, including integration of digital health technologies into complex healthcare workflows, building effective teams, selecting fit-for-purpose technologies, and establishing processes to successfully develop and implement sustainable and scalable AI-enabled systems.

Intended learning outcomes

On completion of this subject, students should be able to:

  • Explain the key challenges in implementing and scaling digital interventions in the real-world context of care and services.
  • For a given example, analyse the potential value-add of a digital intervention and the feasibility of its adoption.
  • Develop a realistic prototype of an AI-enhanced clinical decision support system and create sociotechnical design specifications.
  • Describe how AI-enhanced clinical decision support is an enabling function of a broader work system consisting of digital applications, workflows and teams.
  • Demonstrate the principles of multidisciplinary collaboration while working on a team project

Generic skills

  • Have the ability to demonstrate advanced independent critical enquiry, analysis and reflection
  • Reach a high level of achievement in writing, research or project activities, problem-solving and communication
  • Be critical and creative thinkers, with an aptitude for continued self-directed learning
  • Be able to examine critically, synthesise and evaluate knowledge across a broad range of disciplines
  • Be able to initiate and implement constructive change in their communities, including professions and workplaces.

Last updated: 4 March 2025