Artificial intelligence #02: a code of conduct for AI

In February the government released a new code of conduct for artificial intelligence and other data-driven technologies in the NHS; one of its reported objectives was to ‘make sure the NHS gets a fair deal from the commercialisation of its data resources’. It will ensure that only the best and safest data-driven technologies are used by the NHS and will protect patient data.

The aim is to make it easier for suppliers to develop technologies that tackle some of the biggest issues in healthcare, such as dementia, obesity and cancer. It will also help health and care providers choose safe, effective and secure technology to improve the services they provide. The code will:

  • promote the UK as the best place in the world to invest in healthtech;
  • provide evidence of what good practice looks like to industry and commissioners;
  • reassure patients and clinicians that data-driven technology is safe, effective and maintains privacy;
  • allow the government to work with suppliers to guide the development of new technology so products are suitable for the NHS in the future;
  • make sure the NHS gets a fair deal from the commercialisation of its data resources.

The code will also mean the NHS is fairly rewarded for allowing companies access to its data pool to build life-saving artificial intelligence systems.

The code encourages technology companies to meet a gold-standard set of ten principles – which have been drawn up with the help of industry, academics and patient groups – to protect patient data to the highest standards. The principles set out how the government will make it easier for companies to work with the NHS to develop new technologies – and what the NHS expects in return. It will be regularly updated in partnership with industry and stakeholders to ensure it keeps pace with the market.

The 10 code of practice principles

1. Understand users, their needs and the context

Understand who, specifically, the innovation or technology will be for, what problems it will solve for them and what benefits they can expect. Research the nature of their needs, how they are currently meeting those needs and what assets they already have to solve their own problems. Consider the clinical, practical and emotional factors that might affect uptake, adoption and ongoing use.

2. Define the outcome – and how the technology will contribute to it

Understand how the innovation or technology will result in better provision and/or outcomes for people and the health and care system. Define a clear value proposition with a business case highlighting outputs, outcome, benefits and performance indicators.

3. Use data that is in line with appropriate guidelines for the purpose for which it is being used

State which good practice guideline or regulation has been adhered to in the appropriate use of data, such as the Data Protection Act 2018. Use the minimum personal data necessary to achieve the desired outcomes of the user’s needs and the context.

4. Be fair, transparent and accountable about what data is being used

Utilise data protection-by-design principles with data-sharing agreements, data flow maps and data protection impact assessments. Ensure all aspects of the Data Protection Act 2018 have been considered.

5. Make use of open standards

Utilise, and build into the product or innovation, current data and interoperability standards to ensure it can communicate easily with existing national systems. Programmatically build data quality evaluation into AI development so that harm does not occur if poor data quality creeps in.

6. Be transparent about the limitations of the data used and algorithms deployed

Understand the quality of the data and consider its limitations when assessing if it is appropriate for the users’ needs and the context. When building an algorithm, be clear about its strengths and limitations and give clear evidence of whether the algorithm you have published is the algorithm that was used in training or in deployment.

7. Show what type of algorithm is being developed or deployed, the ethical examination of how the data is used, how its performance will be validated and how it will be integrated into health and care provision

Demonstrate the learning methodology of the algorithm being built. Aim to show in a clear and transparent way how outcomes are validated.

8. Generate evidence of effectiveness for the intended use and value for money

Generate clear evidence of the effectiveness and economic impact of a product or innovation. The type of evidence should be proportionate to the risk of the technology and its budget impact. An evidence-generation plan should be developed using the evidence standards framework published by NICE.

9. Make security integral to the design

Keep systems safe by safeguarding data and integrating appropriate levels of security into the design of devices, applications and systems, keeping in mind relevant standards and guidance.

10. Define the commercial strategy

Purchasing strategies should show consideration of commercial and technology aspects and contractual limitations. Consider only entering into commercial terms in which the benefits of the partnerships between technology companies and health and care providers are shared fairly.


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