Data with destiny

How we recognise and respond to disease is evolving – our future is inseparable from it

We know disease exists – where it comes from and how it evolves – but we don’t yet know when changes will occur.

With a range of tactics now being deployed to help answer these questions and identify future threats to human health, the role data and analytics play is becoming far more prominent.

Pre-COVID-19, vaccine development was always deemed an arduous process, taking on average between ten and 15 years to accomplish. The scale and rapid spread of the COVID-19 virus in 2020, however, forced vaccine manufacturers to fast-track a number of key processes, while also investing in ground breaking new technologies.

Data should be viewed as the medium through which we can understand what is happening in the world. When we don’t have data available, we are far less aware – and this was very much the case in the early days of the pandemic.

Make no mistake, data allows us to see further and more clearly into the future, helping doctors and scientists to better predict threats to human health and how they might develop. Data can also be used to improve understanding throughout the clinical trial process, one of the biggest hurdles the medical profession has to overcome before a vaccine or medicine is deemed viable.

With more data, those conducting a clinical trial will be equipped with a deeper understanding of the differentiating factors between each patient. Similarly, data can be used to highlight the wider contextual factors that may impact a patient’s suitability for a medical or therapeutic course.

Whether it’s looking at which patient groups are most likely to drop out of a study, or the steps needed to prevent this from happening, data can bring previously unchartered intelligence to the clinical trials process.

The ability to extract and analyse this unprecedented level of data will save pharmaceutical companies an enormous amount of money and time each year. The last thing those looking to develop new vaccines want is to invest in a trial that is eventually deemed non-viable – so ‘failing fast’ is key.

Equipped with more data-driven insight, doctors and nurses will no longer have to work half-blind and will benefit from wisdom beyond their own experience.

The process currently followed to select kidneys for transplantation is a prime example of clinicians having to rely on educated estimation, rather than empirical evidence, often making hopeful assumptions about the health of the donated organ.
The benefits would be significant if a data-driven approach to assessing the health of a kidney was available on a wider scale. Those in critical need of a donated kidney would be given a much greater chance of success, reducing the need for more severe or life-saving treatment.

Doctors could also operate at a far greater level of success, both in terms of the outcomes and survival rates for their patients.

Seeking to transform the renal transplant process, SAS UK & Ireland and the University of Cambridge have embarked on a partnership aiming to achieve the prime objective of ‘identifying ways in which the process of selecting kidneys for transplantation could be better delivered through augmenting AI interpretation of renal biopsies’.

The partnership – backed up by the University’s Office for Translational Research (OTR) – was also asked to research the potential for a first-of-its-kind digital histopathology service, fit for purpose on a national scale.

This transformative step forward is just one example of data, digitisation and AI helping the medical profession see further and more clearly to improve patient outcomes.

It’s no exaggeration to say that when harnessed effectively, data and AI could prove as transformative as the microscope in advancing the biological and medical science professions.