The value of patient data can be harnessed in new and innovative ways using data-driven technologies such as machine learning and artificial intelligence (AI). Rapid advances in data science are creating a great deal of interest in the health sector, because it is thought that there is enormous potential to apply these techniques and technologies to transform patient health and the health system in the coming years. 

Such technologies include machine learning, image recognition, natural language processing and text mining. Patient data can be used to 'train' software to identify or recognise certain patterns more efficiently, quickly and effectively than humans can, which results in a wide range and scale of applied uses. There's a lot of hype around these technologies but also some real potential to transform healthcare and research.


New technologies could bring benefits right across the spectrum:

  • For patients – to deliver better care and treatment; and increase self-empowerment to manage health conditions.
  • For clinicians and the health service – to create a more efficient, effective and manageable system.
  • For policymakers – to enable better decisions about health funding and public health.
  • For researchers – to facilitate greater understanding of disease causes and treatments.
  • For industry – to develop and hone products and services for the life sciences and health research sector; speeding up drug discovery and repurposing; and to ensure drug safety and efficacy.

But it's really important to consider the ethics, governance and regulatory issues, to ensure these benefits can be realised in a responsible way.

Mapping new digital technologies

We scoped out some of the new technologies that make use of patient data in their development and applications in healthcare and research, to identify where data-driven innovations are already being introduced. This slide deck shows different types of uses and impacts of new technologies.

The Royal Society has also produced some excellent general resources on machine learning and how it can be used.

Reports and resources

The UK government’s Industrial Strategy has identified data and AI as a key sector for growth in the UK. Many resources exploring future technologies and uses of data have been published in recent years, including the following:

Government and Parliamentary reports

  • AI in the UK: Ready, willing, able? (2018)

An inquiry from the House of Lords Select Committee on Artificial Intelligence which considers implications for the economy, ethics and society due to advances in artificial intelligence.

  • Algorithms in decision-making (2017)

An inquiry from the House of Commons Science and Technology Committee into algorithm uses in decision making in both the business and public sectors.

  • Growing the artificial intelligence industry in the UK (2017)

An independent review for the Department for Business, Energy and Industrial Strategy and the Department for Digital, Culture, Media and Sport recommending actions to strengthen AI in the UK.

  • The big data dilemma (2016)

A House of Commons Science and Technology Committee inquiry outlining opportunities and challenges for big data in the UK and making recommendations for government on skills shortages, infrastructure and regulatory changes. 


Other resources

  • Artificial Intelligence in Healthcare (2019)

The Academy of Medical Royal Colleges describes AI's potential impacts across twelve domains in healthcare from a clinical, ethical and practical perspective, and provides seven recommendations.  

  • Towards trusted data sharing: guidance and case studies (2018)

The Royal Academy of Engineering illustrates data sharing enablers and constraints through ten case studies and provides a data sharing best-practice checklist for organisations.   

  • Our data driven future in healthcare (2018)

The Academy of Medical Sciences outline a set of principles for the development, evaluation and deployment of future technologies in healthcare. 

  • Growing the national institute for data science and artificial intelligence (2018)

The Turing Institute identifies 8 challenges as key research topics to build research capacity in, including ‘Revolutionise healthcare’.

  •  The Lord Darzi review of health and care: Interim report (2018)

This independent review published by IPPR examines the NHS and makes funding and reform recommendations, including around future technologies.

  • Machine learning: the power and promise of computers that learn by example (2017)

The Royal Society makes recommendations for developing machine learning in the UK, including emerging applications in healthcare.   

Articles on data use in the NHS from The King’s Fund can be found here

Find more information about public attitudes to new technology here.

Future technologies and the use of patient data workshop

Conversations about new technologies within the NHS and the health research sector are happening at every level, but are not always joined up. This means different areas or hospitals may put in place different rules or requirements around using data, which makes it difficult to scale up ideas that work, or provide a clear picture to the public.

Bringing it all together

To try and address some of these issues, in June 2017 we brought together healthcare professionals, policymakers, industry and academics to hear each other's views and learn from other sectors. We wanted to understand what people collecting, controlling and using data do in the first instance - which is why we didn't include patient representatives at this initial stage (but we will be talking to patients next).

We aimed to share perspectives and collectively think through some practical solutions for working together. The aims were:

  1. To help understand the opportunities and benefits of these technologies using patient data.
  2. To collectively think through some of the challenges to realising this potential. This included:
    • learning from other sectors that are also grappling with fast-moving technologies, such as autonomous vehicles;
    • considering some of the longer term social and ethical implications of using patient data in innovative ways.

Read a summary of the meeting.