Published by The Rt Hon Norman Lamb MP, Chair, Science and Technology Select Committee on 11 January 2018
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This report illustrates the areas where artificial intelligence (AI) could help the NHS become more efficient and deliver better outcomes for patients. It also highlights the main barriers to the implementation of this technology and suggests some potential solutions.
Despite the hype around AI in healthcare, examples of it being implemented and deployed in the NHS are sparse. Broadly speaking, it is incumbent on individual providers to introduce new technologies into the NHS. This has resulted in piecemeal applications and patchy realisation of benefits. With a different approach to technological adoption, however, which would gradually embed AI in service transformation plans, the future could look quite different.
Potential of AI in the NHS
AI could support the delivery of the NHS’s Five Year Forward View, which aims to narrow three gaps in health provision. AI could help address the health and wellbeing gap by predicting which individuals or groups of individuals are at risk of illness and allow the NHS to target treatment more effectively towards them. The reduction of the care and quality gap could be supported by AI tools as they can give all health professionals and patients access to cutting edge diagnostics and treatment tailored to individual need. AI could help address the efficiency and funding gap by automating tasks, triaging patients to the most appropriate services and allowing them to self-care.
For AI to support a more efficient healthcare system that delivers better outcomes, it must overcome concerns of both the public and healthcare professionals. Public confidence and trust are vital for the successful development of AI. This also means increasing public confidence in the way data is shared both within the NHS and with external organisations.
Getting data right
The NHS will also need to get data right to truly harness the potential of AI in healthcare. This means collecting the right type of data in the right format, increasing its quality and securely granting access to it. The healthcare system is still heavily reliant on paper files and most of its IT systems are not based on open-standards. This limits the exchange of information across the health system. Increasing the quality of the data collected within the NHS is of crucial importance as the accuracy and fairness of AI algorithms are wholly dependent on the data they are being fed.
The ethics of building AI
Public safety and ethical concerns relating to the usage of AI in the NHS should be a central matter of interest for healthcare regulators such as the National Institute for Health and Care Excellence, the Medicines and Healthcare Products Regulatory Agency and Government. If industry is to use NHS data to design AI, as it does now, the NHS should make sure that it can reap the benefits in the long term. In addition, healthcare is a high-risk area, where the impact of a mistake could have profound consequences on a person’s life. AI systems are not infallible and are not devoid of biases. It is important for current regulations to be updated to make sure that the applications of AI in healthcare lead to a better and more efficient NHS, which reduces variations in the quality of care and healthcare outcomes.
Recommendation 1: NHS Digital and the 44 Sustainability and Transformation Partnerships should consider producing reviews outlining how AI could be appropriately and gradually integrated to deliver service transformation and better outcomes for patients at a local level. Caution should be taken when embedding AI within service transformation plans. It should not be regarded as tool that will decide what objectives or outcomes should be reached. AI is an enabler not the vision.
Recommendation 2: NHS England and the National Institute for Health and Care Excellence should set out a clear framework for the procurement of AI systems to ensure that complex to use and unintuitive products are not purchased as they could hamper service transformation and become burdensome of the healthcare professionals.
Recommendation 3: The NHS should pursue its efforts to fully digitise its data and ensure that moving forward all data is generated in machine-readable format.
Recommendation 4: NHS England and the National Institute for Health and Care Excellence should consider including the user-friendliness of IT systems in the procurement process of data collection systems and favour intelligent systems that flag-up errors in real-time.
Recommendation 5: NHS Digital should make submissions to the Data Quality Maturity Index mandatory, to have a better monitoring of data quality across the healthcare system.
Recommendation 6: In line with the recommendation of the Wachter review, all healthcare IT suppliers should be required to build interoperability of systems from the start allowing healthcare professional to migrate data from one system to another. This would allow for compliance with the EU’s General Data Protection Regulation principle of data portability.
Recommendation 7: NHS Digital should commission a review seeking to evaluate how data from technologies and devices outside of the health-and-care system, such as wearables and sensors, could be integrated and used within the NHS.
Recommendation 8: NHS Digital, the National Data Guardian and the Information Commissioner’s Office, in partnership with industry, should work on developing a digital and interactive solution, such as a chatbot, to help stakeholders navigate the NHS’s data flow and information governance framework.
Recommendation 9: NHS Digital should create a list of training datasets, such as clinical imaging datasets, which it should make more easily available to companies who want to train their AI algorithms to deliver better care and improved outcomes. It should also develop a specific framework specifying the conditions to securely access this data.
Recommendation 10: The Department of Health and the Centre for Data Ethics and Innovation should build a national framework of conditions upon which commercial value is to be generated from patient data in a way that is beneficial to the NHS. The Department of Health should then encourage NHS Digital to work with STPs and trusts to use this framework and ensure industry acts locally as a useful partner to the NHS.
Recommendation 11: The Medicine and Healthcare Products Regulatory Agency and NHS Digital should assemble a team dedicated to developing a framework for the ethical and safe applications of AI in the NHS. The framework should include what type of pre-release trials should be carried out and how the AI algorithms should be continuously monitored.
Recommendation 12: NHS Digital, the Medicines and Healthcare Products Regulatory Agency and the Caldicott Guardians should work together to create a framework of ‘AI explainability’. This would require every organisation deploying an AI application within the NHS to explain clearly on their website the purpose of their AI application (including the health benefits compared to the current situation), what type of data is being used, how it is being used and how they are protecting anonymity.
Recommendation 13: The Medicine and Healthcare Products Regulatory Agency should require as part of its certification procedure access to: data pre-processing procedures and training data.
Recommendation 14: The Medicine and Healthcare Products Regulatory Agency Review in partnership with NHS Digital should design a framework for testing for biases in AI systems. It should apply this framework to testing for biases in training data.
Recommendation 15: Tech companies operating AI algorithms in the NHS should be held accountable for system failures in the same way that other medical device or drug companies are held accountable under the Medicine and Healthcare Products Regulatory Agency framework.
Recommendation 16: The Department of Health in conjunction with the Care Quality Commission and the Medicine and Healthcare Products Regulatory Agency should develop clear guidelines as to how medical staff is to interact with AI as decision-support tools.