Making the most of data for personalised medicine

Sobia Raza

8 November 2018

Innovations across a range of technologies are driving the evolution of personalised medicine. PHG Foundation’s latest report – The Personalised Medicine Technology Landscape – reviews these advances as part of an evidence synthesis for the NHS in England.

Data is a common theme across many of the technologies covered in this report. Some technologies – from genome sequencing, mobile health, wearables, to medical imaging – can produce massive amounts of data. Others, including gene therapies, artificial intelligence, and 3D printing, are informed or driven by data.

The explosion in health data could be a significant enabler of the greater personalisation of medicine by providing more detailed information on patients or their disease. At the same time its effective utilisation presents a major challenge for the health system, which has never before had to contend with the high-speed production of large volumes of a wide variety of health data.

A new era in health data?

Data about our health has long been generated and recorded in some form, whether this is blood pressure readings, prescribed medications, doctors’ notes, or scans and medical images. As such, the need for better ways to store, view, secure, share and analyse health data is not new. Upgrading digital infrastructure and digitising heath records has been an ongoing ambition of the health system. However, as our report points out, developments in science and new technologies are placing even greater, and often new demands on the health system’s digital infrastructure, policies, and data skills.

Why is this?

  • Firstly, the nature and complexity of health data is fast changing. For example ‘omics technologies are enabling the detailed assessment of the various molecules that make up our cells. Whether this relates to DNA, RNA, protein or metabolites, it can ultimately result in many measurements (i.e. data points) and hence large data files. Significant computer memory and processing power are typically needed to store and analyse these datasets.
  • Secondly the frequency with which certain data can be captured is increasing, again, resulting in more data points. Innovations in portable health devices, smartphone technology, and wearable technologies are allowing some types of data (e.g. heart rates, ECG) to be collected more routinely and even outside of the healthcare setting. As well as managing more data, this also raises the challenge of integrating data produced in different locations.
  • Thirdly, a more personalised, ‘whole person’ approach to medicine, requires better health data linkage. This demands a change from the prevalent fragmented digital systems that are unable to talk to each other and exchange information – an issue when data is collected and stored in different places.
  • Finally, in addition to collating, storing and integrating data, ongoing developments in analytic solutions are needed to derive novel insights to inform better healthcare. Apart from infrastructure, this requires skills in data-science such as bioinformatics or machine learning.

Unlocking the potential of data for better healthcare

The urgency to transform the digital and data capabilities of the NHS has come into sharp focus very recently with the publication of the Health Secretary’s technology vision The Future of Healthcare. The policy paper sets out the priorities and changes needed to unlock the potential of innovative technologies to support care. A number of these priorities chime with the key considerations raised in our report the Personalised Medicine Technology Landscape. This includes:

  • Interoperability and open standards for technology: to allow data to be transmitted across different systems; to avoid being locked into commercial arrangements with inflexible ‘closed’ software or hardware systems that have often resulted in legacy IT; and to ensure the health system can be sufficiently agile to respond to the rapidly evolving capabilities of digital health technologies.
  • Secure safeguarded systems: to protect data and to foster pubic trust. Cyber security standards, and provisions to contend with the ever-increasing sophistication of internet malware and cyber-attacks are crucial.
  • Skills and culture: this includes building and acquiring skills in data science and analytics (e.g. bioinformaticians, ‘omics analysts, data curators, and AI expertise), to develop or apply tools and analytical approaches to process the increasing amounts of data. More broadly there will need to be a culture shift across the health system and workforce for the digital age.

In addition to the Health Secretary’s technology vision, earlier in October came the announcement of the ambition to sequence five million genomes in the UK over the next five years. These initiatives are examples that highlight the broader desire for bigger, better datasets. This now needs to be supported, in parallel, with the commitment towards the underlying infrastructure, policies, and people that are essential for transforming these bigger datasets into better healthcare. 

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