Alan Payne, CIO at Sensyne Health discusses how greater access to data is key for achieving personalised medicine.
We live in an age where personalised experiences are now both expected and commonplace. From Spotify suggesting playlists based on our music choices, Netflix giving us a recommend list of movies to watch and online retail stores pushing adverts linked to our last purchase. We take for granted our preferences to not just be taken into consideration but remembered and used to tailor the services we receive. My Amazon purchases increase is a reflection of this!
It’s the same story when it comes to the way we care for ourselves. With wearable devices such as fitness trackers continuing to grow in popularity, interrogating our heart rate and activity levels is simple, and we can use the information to inform how we eat, sleep and exercise. Beyond that, an Apple Watch can even provide its wearer with an ECG heart report at the push of a button.
The pharmaceutical industry is also taking an increasingly personalised approach to how therapies and treatments are designed, and to predict and manage what health conditions may arise amongst certain patient groups. While the industry is starting to achieve this, there is still a way to go for healthcare to be tailored to the needs of every individual. And this isn’t surprising, as the amount of data and insights needed to create truly personalised medicine and care is too vast and complex to be collected and analysed by traditional means of data processing. This, combined with the complexities of human biology, mean we still have a very poor understanding of how the human body works. This is where sophisticated technology such as machine learning, is essential.
Luckily, we are in a position where this technology is available to us. We just need to apply it in the right manner to take full advantage of the insights it can provide to potentially save lives and revolutionise healthcare as we know it.
The current state of personalisation
Achieving truly personalised medicine at scale is less than a decade away and AI technologies will play a fundamental role in this. There has been a boom in data generation in recent years and the rate of data collection is only on the rise. In fact, IDC research predicts that the global datasphere will grow from 33 zettabytes of data in 2018, to 175 zettabytes by 2025. To put that into perspective, to download 175 zettabytes of data on the average internet connection speed, it would take 1.8 billion years!
This huge dataset includes healthcare data, in the form of genetic information and electronic health records (e.g. medical history, demographics, allergies etc), which has given clinicians the opportunity to look more closely at individual patients and their conditions, in combinations they could never have done before. They are now able to leverage machine learning to spot trends, patterns and anomalies in the data that can…