Conclusion: Looking ahead

Concerns about the lack of basic awareness and knowledge about AI amongst most healthcare workers highlight the urgent need for an accessible foundational AI education programme delivered in a scalable format.

The diversity of skills, roles and educational needs in the existing workforce demands a flexible approach to delivering continuing professional development education. HEE, in collaboration with other partners in the public sector, academic and industry, can work towards producing and collating materials that can be accessed online and potentially personalised through self-assessment. These can be organised according to this report’s archetypes, and the individual’s experiences and interests, and could be available through a centralised online learning hub and/or other platforms like the NHS Digital Academy, the NHS Learning Hub, and AnalystX.⁴³ Curated education journeys will be required to guide learners to the appropriate information to meet their AI learning needs.

A collaborative effort between industry innovators and NHS staff in health settings will enable product-specific training to better reflect the local workflows and clinical settings and meet NHS user needs. The product specific training and a strategy for its use should be present from procurement through to workflow integration.

Shapers will largely work in national organisations that have traditionally been involved in regulation or guidance for healthcare, including within and beyond the remit of HEE and NHS. Both foundational education in AI technologies and more advanced training regarding the governance, validation and implementation of AI technologies would improve Shapers’ abilities to translate their activities into the domain of data-driven algorithms and AI. This can include a healthcare-focussed educational offering for Shapers in national roles.

Interviewees for this research highlighted challenges relating to information technology, interoperability, and data governance as major barriers to deploying AI technologies in their settings. Prioritising education for the Driver archetype to support them in making the right strategic and governance decisions relating to AI may help to address these challenges. This can include development of specific educational resources or programmes for senior leaders to enable them to make informed decisions around specific technologies and prepare their organisations for deployment of AI technologies. It should also include engagement and support of ICS leaders to develop workforce plans that ensure appropriate digital, data and technology skills are being developed within the workforce.

Establishing and expanding training opportunities for Creators and Embedders should be considered a priority to fill the significant skill gaps highlighted by this research relating to DDaT data family and clinical informatics skills. These skills are vital not only to support creation and implementation of AI, but also for ongoing monitoring, assurance and audit of AI technologies. New opportunities should be established alongside efforts to upskill existing professionals with digital and data skills and establish flexible training opportunities for digital specialist clinicians. These efforts can be supported by the professionalisation and accreditation of these roles, the development of AI MDT teams, and the establishment of clear professional development and career pathways.

Development of user-related education and training will rely on engagement with undergraduate and postgraduate education providers. HEE does not directly provide education to healthcare professionals in training, but works with organisations like the Royal Colleges, national schools, and universities to advise on educational priorities. Further work is needed to work with these organisations and incorporate AI education into undergraduate and postgraduate curricula and to ensure these are aligned with other areas of education reform.