Nadir Healthcare: Comprehensive AI Readiness Audit for Radiology

The challenge

Nadir Healthcare's radiology division was under pressure to adopt AI-assisted diagnostic tools. Vendors were pitching solutions that promised faster reads, reduced backlogs, and earlier detection of abnormalities. The clinical leadership was interested but cautious — they had seen other departments invest in technology that underdelivered because the underlying data, workflows, and governance structures weren't ready to support it.

The radiology director wanted an honest, independent assessment before committing budget. Key questions were unanswered: Was the imaging data standardised enough to feed AI models reliably? Did the existing PACS infrastructure have the capacity and integration points needed? Were there governance frameworks for validating AI-generated findings before they reached a patient's record? Without answers, any investment risked becoming expensive shelfware.

What we built

A structured AI readiness audit covering five dimensions — data maturity, infrastructure capacity, workflow integration, governance and compliance, and organisational capability:

• Data maturity assessment — evaluated imaging data quality, standardisation (DICOM compliance, metadata completeness), historical dataset volume, and labelling consistency across three reporting sites, identifying gaps that would undermine model performance
• Infrastructure review — mapped existing PACS architecture, network throughput, and compute capacity against the requirements of leading diagnostic AI platforms, highlighting specific upgrade paths and cost estimates
• Workflow integration analysis — observed radiologist workflows across all three sites, documenting where AI-assisted reads would fit naturally and where they would introduce friction, with recommendations for phased rollout sequencing
• Governance framework design — developed a draft clinical AI governance policy covering model validation protocols, clinician override procedures, audit logging requirements, and regulatory obligations under the Therapeutic Goods Administration's evolving AI guidance
• Capability gap report — assessed the team's familiarity with AI-augmented workflows, identified training needs, and recommended a structured upskilling programme for radiologists and reporting staff

Technology

Structured assessment methodology combining technical infrastructure audits, data quality profiling, workflow observation, and governance gap analysis. Delivered as a comprehensive report with prioritised recommendations, cost estimates, and a phased implementation roadmap. No software was deployed — the output was a decision-making framework and action plan owned entirely by Nadir Healthcare.