The wider context and workforce transformation

Executive summary

The wider context of workplace transformation and the change towards a digital-ready workforce.

Interviewees for this research noted that the success of artificial intelligence (AI)-related education and training will be dependent on wider requirements for transformation and change towards a digital-ready workforce.

These involve the development of novel team structures, the recruitment, training and retaining of individuals with specialist AI skills (including clinical informatics and digital, data and technology professionals), and new leadership roles that can support health settings to deploy AI technologies.

Workforce transformation, as defined by Health Education England (HEE), is a process to improve the recruitment, retainment, deployment, development and ongoing support of the healthcare workforce, to meet the growing and changing needs of local populations - ensuring high quality care for the patients of today and the future.

Workforce transformation efforts to support AI-related education and training can be grouped against the five enablers of the HEE Star framework, as illustrated in Figure C.

Specific considerations when developing Integrated Care System (ICS) workforce plans can include:

  • establishment of AI multidisciplinary teams (MDTs)
  • development of a plan to increase the digital, data and technical (DDaT) data family workforce to meet future demand. This can include new clinical informatics specialist roles, supported training opportunities, upskilling existing staff into technical roles and joint digital/clinical training opportunities for clinical staff. If deemed necessary, it could also include routes for external expertise to be brought into the healthcare sector
  • creation of senior (non-managerial) technical roles with expert AI-related knowledge and skills
  • expansion of professional digital leadership positions to lead AI development and deployment

Figure C: Workforce transformation to support AI-related education

Supply
Action Establish clear job roles and career pathways for digital, data and technology specialists.

Expand the specialist digital, data and technology (DDaT) data family professions and clinical informatics workforce through targeted recruitment, increased education and training opportunities, competitive renumeration and flexible equivalence pathways for those with skills from experience outside the NHS.

Support professionalisation and accreditation of the DDaT data family professions and clinical informatics workforce through recognised and trustworthy national bodies.
Upskilling
Action Maximise the potential of the workforce through recognised and accredited digital career and education pathways.

Support ongoing CPD (Continuing Professional Development) frameworks for development and validation of digital professionals.

Provide protected education time for digital skill development supported by flexible hybrid training pathways for digital specialist clinicians.

Provide equitable access to training and support, including special efforts to engage and support the digitally unengaged or unconvinced.
New roles
Action Identify gaps that may be filled by development or implementation of new roles.
New ways of working
Action Establish and support AI multi-disciplinary teams (MDTs) involving clinical and technical roles to lead the evaluation, deployment and product-specific user training for AI technologies. A diverse team and a flat organisational structure should be encouraged to avoid hierarchy and minimise bias.

Through innovative placements and recruitment, promote an integrated workforce that creates new relationships and networks and a working environment that embraces intrapreneurship and collaboration.
Leadership
Action Develop a new cadre of digital leadership roles with recognition of the value of specialist skills at a senior level for individuals with DDaT data family and clinical informatics skills.

Set out clear training pathways and career trajectories to achieve a specific set of competencies required for digital leadership.

Developing and sustaining specialist AI-related skills will require the development of appropriate education, accreditation and career pathways as illustrated in Figure D. This should be supported by ongoing and dynamic evaluation and improvement of educational efforts, professional accreditation routes and career pathways.

Current initiatives that address aspects of this workforce development cycle include Health Education England establishing the Data Science Programme, industry roundtables that explore the potential for collaborative training opportunities with academia and industry innovators, and the creation of Informatics Skills Development Networks (ISDNs).

Further, the ‘Data saves lives’ strategy outlines existing efforts to build analytical and data science capabilities, including building the profile of data and analysis as a profession through competency frameworks, networks, training, career opportunities and status, and the work of the Developing Data and Analysis as a Profession Board. Current initiatives include the NHSE Digital Workforce Programme within the System CIO/Director of Levelling-up Directorate, and the development and testing of a National Competency Framework for Data Professionals in Health and Care that will include a set of core competencies for health and care data professionals.

The figure’s outline is a square, and it shows four circles positioned at each corner. Arrows point from one circle to the next to convey that these circles form a continuous loop. The circle to the upper right displays the words ‘Produce educational/training content’. The next circle to the bottom right displays the words ‘Educate/train individuals to professional standards’. The next circle to the bottom left displays the words ‘Offer professional accreditation’. The last circle at the top left displays the words ‘Enhance: Create new roles and career pathways’. A fifth circle is positioned in the middle of the figure. It displays the words ‘Ongoing and dynamic evaluation and improvement’.
Figure D: Workforce development cycle

A key part of this report’s suggested educational approach involves continuation of the broader efforts to enable change and innovation in healthcare settings, as well as efforts to advance digital skills and capabilities within the workforce as important foundations for specific education and training in AI. When considering wider digital health and data-driven technology education, it may be appropriate to apply the approach this report has taken to structure the workforce into archetypes.

Digital infrastructure and organisational digital maturity are also key enablers of transformation through data-science and AI, without which large-scale adoption of AI technologies will not be possible.

These broader efforts should be considered a priority that will support educational and training offerings to develop healthcare workers’ confidence in AI.

Page last reviewed: 17 April 2023
Next review due: 17 April 2024