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University of Melbourne

  • 37% international / 63% domestic

Transforming Healthcare with Data Analytics and AI

  • Non-Award

Harness the power of data analytics and artificial intelligence (AI) to revolutionise healthcare. This course equips participants with tools to navigate healthcare data complexities, leverage AI technologies, and translate data into solutions for clinical practice and operational efficiency.

Key details

Degree Type
Non-Award
Duration
12 - 12 weeks full-time
Study Mode
Online

About this course

Leverage data-driven insights and AI to transform healthcare

Harness the power of data analytics and artificial intelligence (AI) to revolutionise healthcare. This course equips you with the tools to navigate the complexities of healthcare data, leverage AI technologies, and translate healthcare data into solutions that improve clinical practice and operational efficiency.

This is a 12-week online short course consisting of weekly self-paced online learning modules and engaging live Zoom workshops, facilitated by interprofessional specialists and AI experts. Each week, you will engage in 3 hours of asynchronous pre-class learning and 2 hours of interactive, live sessions, designed to enhance your understanding of healthcare data analytics and AI concepts.

By the end of this course, you will:

  • Appraise the benefits and limitations of implementing emerging healthcare solutions in data analytics and AI.
  • Translate data project ideas into clinically relevant questions that empower data and EMR teams to generate actionable datasets.
  • Demonstrate foundational data concepts, including healthcare data sources, collection, standards, preparation, and analysis.
  • Illustrate how to derive insights from data to improve clinical practice through computable and shareable knowledge.

Entry requirements

There are no formal prerequisites, but participants should be ready to engage with online material and participate in Zoom workshops.

  • Strong interest in the topic and commitment to complete the course and engage in real-time sessions
  • No healthcare expertise required.
  • No data analytics required.

This course is tailored for individuals eager to leverage data analytics and AI to transform healthcare, including:

  • Hospital and Primary Care Professionals: With a focus on hospital-based participants.
  • Clinicians: Interested in specialising or acquiring essential skills for future projects.
  • Novice Data Analysts: Those at the beginning of their analytics journey.
  • Emerging Researchers: Including Master's and PhD students, as well as clinical researchers.
  • Non-Clinicians: Such as EMR team members, managers, and quality and safety staff.

Study locations

Online

What you will learn

What you will learn

This course equips you with the tools to navigate the complexities of healthcare data, leverage AI technologies, and translate healthcare data into solutions that improve clinical practice.

  • Appraise the benefits and limitations of implementing emerging healthcare solutions in data analytics and AI.
  • Translate data project ideas into clinically relevant questions that empower data and EMR teams to generate actionable datasets.
  • Demonstrate foundational data concepts, including healthcare data sources, collection, standards, preparation, and analysis.
  • Illustrate how to derive insights from data to improve clinical practice through computable and shareable knowledge.

Course structure

This course offers a structured series of interconnected modules, blending 3 hours of self-paced online learning with 2 hours of live sessions, to guide you through the process of transforming healthcare using data analytics and AI.

  • Week 1: Why Data?
    • Explore how data analytics and AI can save time and improve patient care.
  • Weeks 2-4: Healthcare Data and Processes
    • Learn about available data sources and the data journey within healthcare organisations.
    • Understand data ethics, governance, and storage.
  • Weeks 5-7: Data Selection and Preparation
    • Craft clinically-relevant data questions and develop digital phenotypes.
    • Create and communicate data queries and perform data preparation and quality checks.
  • Weeks 8-11: Data Analysis
    • Dive into descriptive analysis, hypothesis testing, regression analysis, machine learning, and AI techniques.
  • Week 12: Communication
    • Master the art of storytelling to present clear and persuasive findings.
  • Expert insights: Learn from leaders in healthcare, AI, and data science.
  • Hands-on learning: Work with real healthcare data to solve practical challenges.
  • Ethical focus: Address equity and ethics in designing AI solutions.
  • Collaborative projects: Engage with interdisciplinary peers and stakeholders.
  • Real-world impact: Develop solutions with practical applications in healthcare.

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