This report presents 5 archetypes in relation to how artificial intelligence (AI) technologies are developed, implemented and used in health setting.

Interviewees for this research described various roles and responsibilities that relate to how artificial intelligence (AI) technologies are developed, implemented and used in health settings. Following an analysis of these descriptions, this report presents 5 groupings, referred to as archetypes, as detailed in Figure 2.

The 5 archetypes include Shapers, Drivers, Creators, Embedders and Users. Individuals acting as each archetype will have different knowledge and skills requirements to confidently develop, implement or use AI technologies, and hence specific educational needs as detailed in Chapter 3.

The archetypes do not necessarily align with traditional professional groups (for example, doctors, nurses, allied health professionals), specialisms or levels of seniority, but depend solely on an individual’s role in the development, implementation and use of AI technologies. A specific archetype may include individuals from various professional and managerial backgrounds.

Figure 2 provides examples of individuals who may take on each archetype role, including references to existing professional roles at the NHS. There is no implied hierarchy of responsibilities or strict mapping between existing roles and the archetypes. The examples provided for individuals who may take on an archetype role are not exclusive and will depend on the individuals’ skills, local team structure and project scope.

Although most healthcare workers will have responsibilities associated with one archetype, the archetypes are not mutually exclusive. For example, a clinician could be involved in co-creating AI technologies (as a Creator), validating AI technologies procured for use within their setting (as an Embedder) and undertaking clinical work using AI technologies (as a User). These responsibilities may involve single or multiple projects.

Roles and responsibilities will also vary according to the specific requirements of AI technologies, including the level of clinical risk, the intended use of the product and the impact on clinical workflows.

The table also includes roles that are not widely seen in healthcare settings – particularly in relation to the Creator and Embedder archetypes. These new specialist roles will need to be established through job creation, recruitment, and data-science specific training programmes, as discussed in section 4.2.

The 5 archetypes relate to a person’s role, however it is common for people in similar roles to respond quite differently to new ways of working; some are enthusiastic early adopters, others much later adaptors, and most will fall somewhere in between. There are numerous motivational and practical issues that explain this, and NHS England is conducting research in 2022 to better understand these motivational factors and how they might inform new ways to support, educate and train the workforce.

Figure 2

Archetype description Set the direction for AI policy and governance at a national level

Example responsibilies
  • Decide on AI policies within healthcare at a national level
  • Author and enforce regulation for AI technologies, for professionals creating and using AI and for healthcare settings implementing AI
  • Create guidelines for the creation, procurement, deployment and use of AI
  • Produce national procurement frameworks for AI technologies
  • Guide training of healthcare professionals
Examples of individuals who may take on this archetype role
  • NHS leadership and policymaking teams
  • Executives at arm’s length bodies (ALBs)
  • Product regulators
  • Regulators of healthcare workers
  • Regulators of healthcare settings
  • Developers of healthcare technology standards
  • Developers of procurement guidelines
  • Developers of product development and implementation guidelines
  • Developers of clinical guidelines
  • Professional educators

Box 2 provides a fictional case study that demonstrates example roles and responsibilities for each archetype for a fictional AI technology.

Box 2: AI workforce archetypes - fictional case study
Case study This fictional case study involves the creation and deployment of an imaging-based AI technology within a healthcare setting. The aim of this case study is to outline examples of the possible roles and responsibilities for different archetypes during the development and deployment of an AI technology. Only some of the responsibilities listed below will be applicable to other products and implementations.

All product and organisation names used in this case study are fictional.

Summary: A chest X-ray triaging AI product (tri-X) is co-designed between Linchester Integrated Care System (ICS) and an industry innovator, Triadix. The technology is designed to help manage the radiology workflow by prioritising chest X-rays for review by radiologists. Following related regulatory approvals and evidence of robust performance, the technology is later procured and deployed by Nymouth ICS.
Professional AI archetype Shaper
Example responsibilities Create the AI development standards including AI product development, data security and information governance that are followed by the tri-X developers at Triadix and Linchester ICS.

Create the regulatory framework used to classify and appraise tri-X.

Produce evidence and validation standards used to evaluate tri-X.

Develop guidelines for conducting clinical trials of AI products.

Develop clinical guidelines for the use of tri-X.

Determine the NHS deployment strategy used in the national roll out of tri-X.

Develop national procurement guidelines and frameworks used to purchase tri-X.

Professionally regulate clinicians involved in developing, validating and using tri-X.

Regulate healthcare settings using AI products used to assess the settings deploying tri-X.

Produce guidance on liability to educate those deploying and using tri-X.
Professional AI archetype Driver
Example responsibilities Advocate for budget for digital health/AI development at Linchester ICS.

Decide on the ICS strategy for co-creation projects including commissioning arrangements for commercial partners, financial agreements, data sharing arrangements.

Ensure the ICS workforce plan addresses data and technical skills and roles.

Establish an ICS AI multi-disciplinary team (MDT) consisting of clinical specialists, data scientists, clinical scientists, regulatory and safety teams to work on AI co-creation projects.

Create a process for the selection and prioritisation of AI projects within the ICS.

Establish pathways and infrastructure for piloting and clinically evaluating AI products within the ICS.

Commission Triadix as a commercial partner to co-create tri-X.

Formulate an intellectual property agreement with Triadix.

Formulate a commercial commissioning agreement for the wider roll out of tri-X.

Ensure that appropriate regulatory standards are being adhered to when creating tri-X and the product development aligns with appropriate guidelines.

Nymouth ICS

Decide on an ICS procurement strategy used to guide the purchase of tri-X.

Establish an ICS AI MDT consisting of clinical product specialists, data scientists, regulatory and safety teams to work on AI co-creation projects.

Conduct horizon scanning to assess possible digital solutions to clinical problems.

Evaluate tri-X alongside other AI and non-AI products to determine the best solution to clinical problems with advice and guidance from the AI MDT.

Verify that tri-X has met appropriate regulatory and safety standards and aligns with technical and clinical guidelines for the proposed use case.

Agree on contracts to procure tri-X including financial agreements and data sharing arrangements.

Ensure that the ICS AI MDT have the resources to monitor tri-X over time and to analyse the impact of the product on system efficiency and clinical outcomes.

Lead and manage the systems changes required to implement tri-X within clinical pathways including the workforce impact, technological infrastructure and user training.
Professional AI archetype Creator
Example responsibilities Linchester ICS and Triadix

Undertake problem discovery sessions with numerous stakeholders, map out workflows and explore the clinical issue to be solved by tri-X.

Ensure that there is an appropriate data set available to address the problem, evaluating the data for potential bias and generalisability.

Work within an AI MDT alongside Triadix to create the tri-X AI model.

Co-create tri-X with users. Conduct regular user research sessions to get feedback on how to improve the product and add value to users.

Ensure tri-X aligns with safety and regulatory standards working closely with Linchester ICS safety and regulatory team from the outset.

Test the algorithm using internal and external validation methods, iterating and improving the model until accepted accuracy and safety levels are reached (alongside Embedders).

Conduct prospective clinical studies of tri-X in line with Linchester ICS requirements and national evidence standards to demonstrate efficacy (alongside Embedders).

Conduct post market follow up to assess and monitor performance, and act upon any model drift (alongside Embedders).
Professional AI archetype Embedder
Example responsibilities Nymouth ICS

Lead the technical implementation of tri-X including systems integration and information governance.

Conduct a clinical safety review of tri-X and ensures there are appropriate protocols in place for systems failure and for reporting errors.

Review and critically appraise the internal and external validation results for tri-X.

Review and critically appraise prospective clinical studies of tri-X.

Evaluate the need for local product validation, and conduct this analysis if required.

Assess the regulatory compliance of tri-X.

Advise ICS Drivers regarding AI product procurement decisions.

Advise on, and assist with, the systems change processes involved in the introduction of tri-X.

Create and deliver product-specific user education for tri-X both independently and through collaboration with Triadix. Continue to deliver education and training updates to address product iterations.

Provide advice and support for users of tri-X.
Professional AI archetype User
Example responsibilities Use tri-X (as a radiographer or radiologist within Linchester ICS and Nymouth ICS) to prioritise X-rays for review.

Undertake appropriate product-specific user training for tri-X and keeps this up to date.

Use tri-X in accordance with clinical guidelines and guidance from professional regulators.

Identify and act upon situations in which tri-X may be prone to error, overriding tri-X decisions where appropriate.

Use tri-X to prioritise cases appropriately, with awareness of the potential for bias in the human-AI interaction for clinical decision making.

Action fall-back clinical pathways if the tri-X product fails.

Report suspected errors and any safety concerns identified when using tri-X.

Page last reviewed: 18 April 2023
Next review due: 18 April 2024