Where regulatory readiness asks ‘are we compliant?’ — evaluation readiness asks ‘are we actually checking?’. The answer is most often no.
THRIVE™ Evaluation assessment maps your capacity to commission AI against local thresholds, monitor performance continuously, detect drift, manage incidents, audit independently, and decommission when warranted — closing the loop between procurement decisions and clinical outcomes.
Core capabilities
Acceptance & Commissioning Testing
MOVEDStructured execution of formal validation against local clinical requirements before authorising clinical use.
QA Programme Readiness
MOVEDCapacity to design and sustain quality control programmes including routine QC, case-specific QA, ad-hoc testing, and workflow audits.
Continuous Monitoring Readiness
MOVEDOperational capacity for ongoing real-world performance monitoring, dataset shift detection, and model drift assessment.
Override & Contingency Planning
MOVEDDefined processes for clinicians to flag and override AI outputs, combined with system-level contingency plans for AI downtime.
Incident Management
MOVEDDefined processes for detecting, classifying, reporting, and resolving AI-related adverse events.
Decommissioning Planning
MOVEDEnd-of-life protocols covering workflow identification, alternative solution selection, security revision, and continuity of care.
Real-World Performance Evaluation
NEWInstitutional capability to evaluate deployed AI against actual patient outcomes and feed findings back into procurement and configuration decisions.
Independent Audit & Internal Validation
NEWCapability to audit AI performance independently of the vendor and the deploying team.
How Evaluation connects
Evaluation readiness operationalises what technical readiness demands. T says ‘require local validation’. E determines whether you can actually execute it.
Post-market surveillance obligations (R) are regulatory requirements. Continuous monitoring capability (E) is how you meet them.
Monitoring, drift detection, and incident logging all require infrastructure (I) — pipeline instrumentation, logging architecture, and alert systems.
What the literature says
“Health-care workers and health systems must have detailed information on the contexts in which such systems can function safely and effectively, the conditions necessary to ensure reliable, appropriate use, and the mechanisms for continuous auditing and assessment of system performance.”
— WHO, Ethics & Governance of AI for Health (2021)
“Deployed models have the capability to be monitored in ‘real world’ use with a focus on maintained or improved safety and performance.”
— FDA, Good Machine Learning Practice (2021)
“It addresses the entire process, from the initial assessment of needs, through selection, commissioning, ongoing management and eventual decommissioning.”
— IAEA PC9134 (2025)