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Ensure your healthcare data is Findable, Accessible, Interoperable, and Reusable with our comprehensive FAIR principles checklist. This practical tool helps you evaluate whether your datasets meet international standards for research impact, regulatory compliance, and data sharing.

The checklist covers all four FAIR dimensions with actionable criteria including persistent identifiers, metadata requirements, standardised formats (HL7 FHIR, DICOM, SNOMED CT), access controls, and compliance with HIPAA and GDPR. Whether you're preparing data for research collaboration, repository submission, or internal quality assurance, this resource provides clear guidance on best practices.

Ideal for data managers, research teams, and healthcare organisations looking to maximise the value and usability of their data assets.

Download your free checklist now—and if you need support with repository selection, metadata strategies, or full FAIR implementation, book an exploratory consultation with us.

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Deploying AI changes in clinical settings requires rigorous validation. This comprehensive testing framework guides you through designing, executing, and analysing A/B tests for AI healthcare software—ensuring both statistical validity and patient safety.

This detailed checklist covers twelve critical domains from pre-test planning through post-implementation monitoring. You'll find guidance on statistical power analysis, randomisation strategies, regulatory considerations (FDA/CE classification, IRB approval), clinical safety monitoring, bias prevention, and fairness metrics. The framework addresses the unique challenges of healthcare AI, including clinical endpoint selection, algorithmic fairness across patient subgroups, workflow integration, and real-time safety monitoring with defined stopping rules.

Essential for product teams, clinical informatics leaders, data scientists, and quality assurance teams validating AI-driven clinical decision support, diagnostic tools, or treatment recommendation systems.

Download your free framework now—and if you need support designing your A/B test strategy, selecting appropriate metrics, or interpreting results for regulatory submission, book an exploratory consultation with us.

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Planning a healthcare research project? This comprehensive checklist guides you through every critical aspect of building a robust data infrastructure—from initial planning to long-term data preservation.

Covering nine essential domains, this resource helps research teams establish solid foundations for data-driven studies. You'll find actionable criteria for project planning, regulatory compliance (IRB, HIPAA, GDPR), data quality assessment, security controls, and infrastructure setup. The checklist addresses common challenges, including data integration from multiple sources (EHR, imaging, wearables), de-identification workflows, version control, and reproducible analysis.

Whether you're launching a clinical trial, observational study, or multi-site research collaboration, this tool ensures nothing falls through the cracks. Ideal for principal investigators, research coordinators, data managers, and IT teams supporting healthcare research.

Download your free checklist now—and if you need expert guidance on data infrastructure design, vendor selection, or compliance strategy for your research project, book an exploratory consultation with us.