Australian Digital Health Agency Case Study

As steward of Australia’s National Digital Health Strategy 2023–2028, the Agency is responsible for national systems and services that underpin a safe, secure and efficient digital health system.

This includes managing diverse data domains such as:

• Clinical data from My Health Record (MHR)
• Operational metrics from digital services such as My Health Application (MHA)
• Internal organisational data

As the data environment expanded, the Agency faced challenges including:

• Limited standardised data quality requirements across domains
• Inconsistent reconciliation practices
• Limited visibility into emerging data quality issues
• Insufficient tools and resources to assess and improve quality proactively

Without a stronger enterprise approach, there was a risk that critical data could not consistently support clinical care, research, policy development and service delivery.

Evinact undertook a structured review of the Agency’s data quality environment, combining stakeholder engagement, process analysis and industry best practice review.

This included:

• Consultation with key stakeholders across the organisation
• High-level review of existing data analysis and reporting processes
• Assessment of current-state data quality and reconciliation practices
• Identification of enterprise data quality dimensions and requirements

Evinact then developed three core artefacts:

Discovery and Analysis Summary to capture findings, core data domains and requirements
Data Quality Framework and Reconciliation Plan defining responsibilities, processes and controls across the organisation
Toolkit providing practical guidance, checklists and techniques to assess, monitor and improve data quality

Together, these deliverables established a clear and repeatable model for enterprise data quality management.

The engagement provided the Agency with a stronger and more proactive approach to managing data quality across critical systems and services.

Evinact delivered:

• Standardised data quality and reconciliation processes
• Clearer accountability for quality management activities
• Practical tools and guidance for staff
• Improved visibility of data quality issues and remediation pathways

This positioned the Agency to:

• Improve the accuracy, completeness and consistency of key data assets
• Increase confidence in data used for reporting and decision-making
• Identify and resolve quality issues more efficiently
• Strengthen the reliability of data supporting clinical care, research and policy outcomes

The framework established a stronger foundation for trusted, evidence-based decision-making across the organisation.