A Deployment-Derived AI Readiness Tool for NHS Providers: Translating Real-World Safety and Implementation Gaps into a Pre-Implementation Checklist
ABSTRACT
Background:
Artificial intelligence (AI) is increasingly deployed across NHS services, yet many safety and implementation challenges emerge after go-live rather than during pre-deployment assessment.
Objective:
This study aimed to develop and provide initial validation of a deployment-derived AI readiness tool to support pre-implementation decision-making in NHS provider organisations.
Methods:
Design Pragmatic tool development with retrospective validation. Setting A UK NHS district general hospital. Empirical learning from two real-world AI deployments: (1) an AI fracture detection system and (2) an AI-supported prostate MRI pathway. Evidence sources included a Clinico-AI discordance study and a Quality, Service Improvement and Redesign (QSIR) programme using Plan–Do–Study–Act (PDSA) cycles. Safety, governance, operational, and adoption gaps identified during deployment were translated into auditable pre-implementation requirements to form the SID & ADE AI Pre-Implementation Checklist. The checklist was retrospectively applied to both deployments to assess readiness completeness at the point of go-live and alignment between unmet items and observed implementation challenges.
Results:
The checklist comprises eight domains spanning use-case definition, clinical safety and accountability, local validation, workforce readiness, operational integration, information governance, procurement, and post-deployment monitoring. Retrospective application demonstrated substantial variability in readiness at go-live, with recurrent gaps in workforce readiness, local validation, and monitoring and stop-rule definition. Domains with lower readiness aligned with areas requiring post-deployment remediation.
Conclusions:
A deployment-derived AI readiness checklist offers a pragmatic mechanism to institutionalise governance requirements and real-world implementation learning. Used prospectively, it may support safer and more consistent AI adoption by informing whether AI systems should proceed to clinical go-live.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.