Selling AI Solutions to the NHS

1. Understanding NHS Needs and Priorities

• What specific problem does your AI solution solve for the NHS?
• How does it align with NHS Long Term Plan priorities (e.g., prevention, early diagnosis, efficiency)?
• Does it address current NHS challenges, such as workforce shortages, waiting times, or patient outcomes?

2. Clinical Effectiveness and Evidence

• Has the AI been validated in clinical settings? What evidence supports its effectiveness?
• Are there peer-reviewed studies or real-world data demonstrating its impact?
• How does it compare to existing solutions or standard care?

3. Regulatory Compliance

• Is the AI solution compliant with UK medical device regulations (e.g., MHRA, UKCA marking)?
• Does it meet GDPR and NHS Data Security and Protection Toolkit requirements?
• Have you conducted an equality impact assessment to ensure no bias in the AI?

4. Data Privacy and Security

• What data does the AI require, and how is it collected, stored, and processed?
• How do you ensure patient data is anonymized or pseudonymized?
• What measures are in place to prevent data breaches or misuse?

5. Integration with NHS Systems

• Can the AI integrate with existing NHS IT systems (e.g., Electronic Health Records like EPIC or Cerner)?
• What is the implementation process, and how disruptive is it to current workflows?
• Does it require additional training for NHS staff? If so, how is this supported?

6. Cost and Value Proposition

• What is the cost structure (e.g., upfront, subscription, or pay-per-use)?
• How does the solution deliver cost savings or efficiency gains for the NHS?
• What is the return on investment (ROI) timeline?

7. Ethical Considerations

• How does the AI ensure transparency in decision-making (e.g., explainability of algorithms)?
• How do you address potential biases in the AI model?
• What safeguards are in place to ensure patient safety?

8. Stakeholder Engagement

• Who are the key decision-makers and stakeholders in the NHS for your solution (e.g., clinicians, IT teams, procurement)?
• Have you engaged with NHS Trusts, Integrated Care Systems (ICSs), or Academic Health Science Networks (AHSNs)?
• Are there patient or clinician endorsements for your solution?

9. Scalability and Sustainability

• Can the solution scale across multiple NHS Trusts or regions?
• How do you ensure the AI remains up-to-date and effective over time?
• What is the environmental impact of your solution (e.g., energy use of AI models)?

10. Procurement and Funding

• Is your solution listed on NHS procurement frameworks (e.g., G-Cloud, Health Systems Support Framework)?
• Are there funding opportunities or grants (e.g., AI in Health and Care Award) that support adoption?
• What is your strategy for navigating NHS procurement processes?

11. Post-Implementation Support

• What ongoing support and maintenance do you provide?
• How do you handle updates, bug fixes, or changes in regulations?
• Do you offer training or resources for NHS staff?

12. Measuring Success

• What metrics will you use to measure the success of your AI solution (e.g., patient outcomes, cost savings, efficiency)?
• How will you collect feedback from NHS staff and patients?
• Can you provide case studies or testimonials from other healthcare providers?

Key Considerations

• NHS Culture: Understand the NHS’s focus on patient care, equity, and evidence-based decision-making.
• Partnership Approach: Position yourself as a partner, not just a vendor, by demonstrating a commitment to NHS goals.
• Patience: NHS procurement and adoption processes can be lengthy—be prepared for a long sales cycle.