AI Readiness Checklist for Healthcare Clinical Trials in Australia (2026)

Review whether Human Research Ethics Committee submission and approval workflows are digitised sufficiently to support AI-assisted protocol development and amendment tracking.

Survey participating trial sites to understand their technology infrastructure, connectivity, and staff digital capability, identifying sites that may struggle with AI-enabled trial processes.

Analyse historical query rates, protocol deviation frequencies, and data cleaning timelines to establish baselines for measuring AI-driven improvements in data quality management.

Document all systems used for safety data collection, adverse event reporting, DSMB communications, and regulatory submissions to identify AI integration points.

Establish current performance baselines for recruitment velocity, screen failure rates, and geographical distribution of enrolled patients to measure AI recruitment tool impact.

Evaluate AI vendors offering clinical trial solutions with Australian data hosting, TGA awareness, and experience operating within the local ethics committee and regulatory framework.

Define the data requirements, inclusion/exclusion criteria encoding, and matching algorithms for an AI system that identifies eligible patients across Australian healthcare databases.

Develop communication materials for trial sponsors, HRECs, and institutional review bodies explaining proposed AI applications, validation evidence, and participant protections.

Establish measurable KPIs for AI impact including recruitment acceleration, query reduction rates, safety signal detection speed, and site monitoring efficiency improvements.

Deploy AI tools that analyse proposed protocol criteria against Australian patient population data to predict recruitment feasibility, screen failure rates, and optimal site selection.

Implement AI tools that automatically identify data discrepancies, generate targeted queries, and suggest resolutions to reduce the data cleaning burden on site coordinators.

Implement risk-based monitoring enhanced by AI analytics that identify sites requiring on-site visits based on data patterns, deviation trends, and enrolment anomalies.

Deploy AI systems that predict participant dropout risk and trigger personalised retention interventions including appointment reminders, transport assistance, and follow-up scheduling.

Periodically review whether participant information sheets and consent forms adequately describe AI data processing, updating materials as AI capabilities expand or regulatory guidance changes.

Assess whether AI recruitment tools introduce bias in patient selection across age, gender, cultural background, geographic location, and socioeconomic status within the Australian population.

Maintain detailed metrics on how AI tools affect trial milestones, per-patient costs, and operational efficiency to justify continued investment and guide future AI adoption decisions.

Monitor and prepare for anticipated TGA, NHMRC, and international regulatory changes affecting AI use in clinical trials, maintaining organisational agility to adapt governance frameworks.