The Health Service Executive (HSE) for Ireland has published an AI implementation framework outlining a five phase approach for AI projects from identification and prioritisation through to deployment and ongoing monitoring. It is designed to support with the implementation of the government’s AI for Care strategy, published earlier this year.
The first of the five phases focuses on opportunity identification and prioritisation, assessing whether opportunities are suitable use cases for AI. Here, it notes gathering high-level requirements and engaging with relevant teams or stakeholders, as well as a preliminary assessment for clinical safety, ethical considerations, regulatory compliance, and analysis of risk through the HSE Risk Matrix
The planning and approval phase will review the necessary budget, staffing, and infrastructure, with the next phase design and readiness to ensure technical feasibility and that the right data is in place, with procurement where required and the development of a solution integration plan. Here, the framework notes to consider solution design, integration with existing systems, data collection, compliance, and change management.
On integration and deployment to validate AI solutions, testing in a “safe and secure environment” is required, according to HSE, alongside clinical and ethical assessments, and post-implementation support to ensure seamless transition to operational use. Governance groups will develop a set of criteria to evaluate success.
Finally, operations and monitoring will continue to check solutions are functioning optimally and it’s delivering expected benefits, with routine checks, performance assessments, and regular evaluations to identify potential issues early. This phase is also to include benefits realisation and post-market surveillance for clinical projects.
In support of these five steps, HSE has reportedly developed a number of tools and resources, such as an Opportunity Registration Platform for AI opportunities to be registered, a Fundamental Rights Impact Assessment to identify and mitigate risks from the deployment of AI systems, and an AI Inventory to register AI projects which will act as a follow-up mechanism allowing project registrants to view ongoing initiatives and outcomes.
Governance will be supported by a digital for care oversight group whose primary responsibilities cover monitoring the progress of the AI strategy and promoting projects for innovation funding; and an AI for care steering group, responsible for reviewing strategic alignment and high-risk AI projects. A clinical advisory group will manage clinical safety issues and integrate AI into clinical workflows, whilst a technical advisory group will offer expertise and guidance on AI development and implementation.
An AI and Automation Centre of Excellence will take responsibility for maintaining an AI inventory, facilitating knowledge sharing, providing training programmes around monitoring of AI hallucinations, and encouraging collaboration with external partners, HSE shares. Patient and public representatives will further be “actively involved”, it states, in relevant governance groups to ensure their perspectives are included in decision making.
Wider trend: Health AI
The National Commission into the Regulation of AI has put forward a series of recommendations for a future regulatory framework, finding strong support for the use of AI in healthcare being “conditional, rather than automatic”. The central conclusion, the commission goes on, is that future regulation needs to be more proportionate, lifecycle-based, and system-wide. “Current approaches were largely designed for products that are more static and easier to reliably assess at a single point in time,” it states. “AI-enabled products may iterate rapidly, perform differently in different settings and depend on the data, workflows, people and organisations around them.”
An AI spin-out from the University of Birmingham has launched, with an aim to speed up the process of extracting data from EHRs to support medical researchers and reduce the time for drug safety and effectiveness studies. Dexter AI is based on a software developed by a team from the Birmingham Department of Applied Health Sciences, Professor Krishnarajah Nirantharakumar, Dr Krishna Gokhale, and Professor Joht Singh Chandan. Its website outlines the potential for use in the design of epidemiological studies, cohort identification, emulating clinical trials with real world data, automating clinical audits, and enabling population health management.
An AI spinout from Moorfields Eye Hospital NHS Foundation Trust has completed a seed round to help accelerate the translation of AI from research to clinical care. Cascader, a partnership between Moorfields, Topcon Healthcare, and UCL Ventures, is a medical technology company applying AI in the field of oculomics, and in the detection and management of eye disease. According to the company website, its ambitions are to ensure patients and clinicians are able to benefit sooner from innovations in eye care, medical imaging, and data science.
