Data-Driven Innovation Guiding Digital Health in the NHS

Qure AI
3 min readAug 24, 2021


Surabhi Srivastava

The COVID-19 pandemic has impacted every aspect of global life, none more so than healthcare. The last 2 years have witnessed a paradigm shift in digital health, led by accelerated adoption of services like disease management systems for tracking outbreak clusters, contract tracing, vaccine rollouts, etc. Nationwide lockdowns and the heavy burden on tertiary health care systems due to rapid infections have also resulted in a significant uptake in teleconsultation services.

As the digital health ecosystem continues to develop and improve, large repositories of healthcare data are being built with the potential to transform healthcare innovation.

Prior to widespread digital adoption, healthcare data was fragmented, and non-standardised, making it challenging for health researchers and med-tech innovators to access high-quality data for analytics, training, or validation. Within the UK, the NIHR has championed structured data sharing for clinical research in healthcare, targeted at systemic benefits to patients and the NHS health and care system.

NHS England is in the process of building a centralised database as part of a new scheme called the General Practice Data for Planning and Research (GPDPR) which would comprise the medical records of the 61 million users benefiting from the services of the England Public Health System.

According to NHS Digital, the data will be used to:

  • Inform and develop health and social care policy
  • Plan and commission health and care services
  • Take steps to better protect public health
  • Enable research, and provide individual care in exceptional cases

The creation of a centralised database of patient health metrics and sharing of this data for research brings with it concerns around patient privacy and confidentiality in alignment with the GDPR framework. The data must:

Creating a centralized database of patient health metrics and sharing of this for research brings with it concerns around patient privacy and confidentiality in alignment with the GDPR framework.

The data must:

  • Protect the confidentiality and privacy of individuals
  • Respect the terms of consent by individuals who are involved in research
  • Be consistent with relevant legal, ethical, and regulatory frameworks
  • Guard against unreasonable costs

From a patient privacy perspective, the key points of this database are:

  • Anonymous Data: All patient identifiable features such as name, address, etc. would be removed prior to addition to the database
  • Confidentiality: Since only anonymous data would be shared with third parties, it would maintain patient privacy
  • Opt-Out Facility: Citizens can opt out of the system by contacting their GP or avail of the National Data Opt-Out facility
  • Data Sharing: Data will only be accessible to research or health organisations with legitimate needs (Clinical research, Med-tech innovation etc.) who meet stringent criteria
  • Non-Commercial Use: Database strictly not for use by insurance or marketing services, sales, market research or advertising

Information about patient health and care helps the NHS to improve individual care, speed up diagnosis, plan local services and research new treatments. Access to high-quality, standardised data can play a significant role in healthcare, especially for med-tech innovations based on data analytics, machine learning and Artificial Intelligence. The NHS has been actively involved in contributing to this sector with its work across NHSX and NHS Digital for both training and evaluation of such technologies.

The following is a guide for clinicians and researchers within the NHS, looking at collaborations with AI companies for R&D or Evaluation or Adoption

  • Data Access Request Service (DARS) can offer clinicians, researchers, and commissioners the data required to help improve NHS services
  • Data Sharing Agreement (DSA) with research partners for R&D collaborations or clinical performance studies for Pseudonymous data
  • Data Protection Impact Assessment (DPIA) process to help NHS Trusts identify and minimise the data protection risks of a project
  • Data Security and Protection Toolkit (DSPT) is an online self-assessment tool for partner organisations to measure their performance against the National Data Guardian’s 10 data security standards

Some key Data-driven projects of the NHS in the Artificial Intelligence domain:

NCCIDNational Covid Chest Imaging Database is a centralised UK database containing chest images to support a better understanding of the COVID-19 virus and develop technology that will enable the best care for patients hospitalised with a severe infection.

NHSX AI Award– Increasing the impact of AI systems in helping to solve both clinical and operational challenges across the NHS

NHS AI Lab — Skunkworks projects are an opportunity for clinicians in the UK public healthcare sector to pitch their data-rich problems and test solutions



Qure AI is a breakthrough AI solution provider that is disrupting the radiology ‘status quo’ by enhancing imaging accuracy and improving health outcomes.