Public Services > Central Government

The potential of AI to transform the NHS

Published 11 January 2018

Report from political think-tank Reform discusses the opportunities and challenges of artificial intelligence within the NHS


Artificial intelligence (AI) could be used by NHS to target treatment by predicting which individuals or groups might be at risk of illness, to sending patients to the most appropriate services and to delivering better outcomes for patients.

This transforming potential of AI in healthcare was outlined at an event held today by the political think-tank Reform to explore its revolutionary potential for algorithms and data in healthcare.

Reform’s report illustrates the areas where 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.

The conference discussed how AI could help the NHS deliver its sustainability and transformation partnerships (STPs) as well as tackle the challenges that will need to be addressed to make this a reality.

Parliamentary Science and Technology Committee chair Norman Lamb said, “We are on the brink of a major transformation in the way we diagnose, treat, and even prevent ill health. Whether it is wearable devices, AI surgical robots, or AI algorithms that can detect certain conditions with unprecedented speed and accuracy, these advances have the potential to propel the health and social care system into the 21st century – improving care both in the hospital and at home, and making much more efficient use of resources.”

Lamb said, “There is still more to do for AI to win the hearts of all healthcare professionals, and these are just some of the issues that will occupy policymakers in the years ahead”.

“Infrastructure for collecting, sharing and accessing data need to be improved. Resolving the ethical questions surrounding AI in healthcare settings will be crucial, including setting the right regulatory framework” Lamb added.

Recently, the Science and Technology Committee has been examining the increased use of algorithms in decision-making, both in the public and business sphere. It launched an inquiry that aims to understand how they are created, the scope for unwanted bias and the impact they may have on individuals.

The report maintains that “the NHS 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.”

It continues, “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 report presents sixteen major recommendations:

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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