Transforming Australia's Electronic Health Record

Challenge

Without a redefinition of its features and experience, MHR risked becoming a relic in a healthcare landscape.

My Health Record (MHR), Australia's centralised opt-in medical record system, faced a pivotal challenge. Despite its potential, the system was under-utilised, with stagnating adoption rates, untested features, and an unclear future direction. For example, of 4.67 million active health records, only 1.1% were accessed monthly, with just 0.1% accessing records (2017). Australian Digital Health Agency (ADHA) faced a critical choice - either revitalise MHR through strategic enhancements and a modern, user-centric experience, laying the groundwork for a mandatory opt-out model (called Return to Government), or risk losing ground in a rapidly evolving healthcare landscape and jeopardising crucial public trust.

Early Service Mindsets

Approach

Designing MHR for Real People

As the design lead, my goal was to dismantle the frustration barrier often associated with government services. With MHR, I led an approach focused on understanding users' emotional journeys and pain points. By addressing key behavioural patterns and attitudes, I led the team to envision a MHR that resonated with diverse users, making it an essential tool for health awareness and engagement.

This was achieved by:

  • Competitive landscape reviews identifying trends and evolving user expectations of healthcare solutions
  • An in-depth analysis of user interaction patterns to identify their true needs and expectations from the MHR platform.
  • Prototyping the ideal state for the platform, exploring functionalities like NLP-powered chat interfaces.
  • Innovative cognitive load tests on proposed features, ensuring efficient navigation and comprehension.
  • Outlining a strategic roadmap that guided feature development and the MHR platform enhancements.

ADHA Multi-Sprint Timeline and Directions

"Big Rocks" identified for My Health Record

Outcomes

Paving the Way for Enhanced Healthcare Management

I guided MHR to better position itself for success with their opt-out model, with these achievements:

  • A series of platform upgrades that significantly improved usability and broadened MHR's capabilities, laying the groundwork for wider adoption.
  • A vision for an NLP-enhanced chatbot, streamlining onboarding, record management, and overall user experience.
  • Product roadmap guiding the introduction of new features, ensuring each aligned with user needs and contributed to a comprehensive healthcare solution.
  • Enhancing Fjord and Accenture's tech portfolio by introducing EEG-driven insights from quantitative cognitive workload assessments, optimising usability and accessibility.
  • Having ADHA empowered with a resource informing a full backlog of improvements and future service and design directions, supported by extensive research and a series of validated service concepts
  • Solidifying ADHA's collaboration with Fjord and Accenture, guaranteeing continued innovation and evolution in Australia's digital healthcare landscape.

Usability Analysis For Health Professional's Portal

Results Extract of Alpha and Beta tests

Extract of Final Handover Website to ADHA

User tests:

  • Record access: Initial tests: 34% positive experience, participants struggled with speed and clarity. Subsequent tests: 77% positive experience, improved recognition and access points.
  • Setup: Initial tests: Low relevance and confusing prioritization, low completion rates. Subsequent tests: Improved guidance & value propositions, clearer prioritization, higher completion rates.

Alpha test:

  • Chatbot: 100% positive responses, users praised its clarity, ease, and friendliness.
  • Traditional form: 0% positive responses, users found it confusing and less reassuring than the chatbot.

Quant evidence:

  • User test satisfaction: 34% vs. 77% positive experience (with enhancements)
  • Setup completion rates: Significant increase after design changes

Qual evidence:

  • User test quotes:
    • First test: "Confusing," "Slow," "Difficult to find records"
    • Second test: "Clear," "Easy to use," "Value proposition makes sense"
  • Alpha test quotes:
    • Chatbot / NLP concepts: "Straightforward," "Short and sweet," "Reassuring"
    • Traditional form: "Confusing," "Less trustworthy," "Less engaging"