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

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.

Analyse this

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.

Real time

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.

AI aboard

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.

Back in 1665 when Robert Hooke published Micrographia, he was highlighting the extraordinary details of the natural world his microscope had revealed. Now, data scientists and analysts are uncovering details of a different kind – through data.

Streamed in real time and measured by intelligent analytics, this data has the power to enhance human understanding beyond what was thought to be possible. Think of the transformative effect such powerful data could have on, for instance, our further insight into pre-eclampsia; a condition that affects one in ten pregnancies and causes the deaths of at least 50,000 mothers and 500,000 babies worldwide each year.

With greater understanding, this serious pregnancy complication and diagnosis will become far less challenging for doctors. Using cloud-based technology, a team based at University College Dublin has now developed a prototype AI-based machine learning tool that has shown great promise in identifying pre-eclampsia when it develops.

Clinicians can use the tool to hopefully make life-saving decisions for babies and mothers in real time.

Using machine-learning algorithms to combine unique biochemical signals with clinical information about patients – such as blood tests, demographics and medical opinion – this rapid pre-eclampsia prototype test utilises the SAS Viya analytics engine running on Microsoft Azure.

Not only will the test hopefully more accurately diagnose pre-eclampsia, but it may also predict the future severity of disease.

Data day

While advances in data-driven research can certainly bring huge benefits, the deployment of this technology should be carefully considered.

Without reliable data-gathering mechanisms the world would have been unable to tackle the COVID-19 pandemic as efficiently. Scientists would not have known when and where new variants were emerging or the rate at which they were spreading and the number of people being infected. Similarly, doctors would have been unable to effectively track immunity levels or the number of people choosing to have the vaccine.

Should we find ourselves facing another pandemic, data and analytics tools need to be ready for deployment immediately, anywhere in the world. But, for this to happen, there does need to be a number of state-led observation tools in place, something many people remain ardently opposed to.

With great power comes great responsibility, and we must know how to handle both sides of this equation.

Simon Tilley is Global Lead for Healthcare and Life Science at SAS. Go to