AI can power the next generation of personalised healthcare
In recent years, personalised experiences have become commonplace in many aspects of life, from retailers recommending certain products based on your online purchasing history, to streaming platforms sharing recommended shows based on what you've most recently watched. The ability to offer a personalised experience hinges on collecting and analysing data – and the healthcare industry is no different.
We are now able to monitor our own heath very easily – through wearable devices – in a more detailed and personalised way than ever before. In fact, the pharma industry is also making more effective use of patient data collection. For example, to design individualised therapies and treatments that will help predict and manage health conditions amongst certain patient groups more accurately.
While the industry has made significant progress in this area, there is more to be done before we can say that healthcare or medicine is tailored for the requirements of each person. To reach that point, huge quantities of patient data is needed – beyond what can be collated or analysed by manual processes today. Advanced technology like AI and machine learning is therefore critical to help solve this problem, by quickly and efficiently managing the ‘data-boom’ that healthcare is experiencing with automated solutions while being cognisant to the particular sensitivities and robust and privacy requirements associated with healthcare data.
Luckily, we are in a position where this technology is already available to us. We just need to apply it in the right manner to take full advantage of its use and the insights it can provide. From there, the potential to save lives and revolutionise healthcare as we know it is huge.
Applying patient data
It’s fair to say that genuinely personalised medicine at scale is on the horizon, and AI is playing a key role in getting there. The data that exists, such as genetic information and electronic patient records of their medical history, have provided healthcare professionals a gold mine of data to gather greater insight into individual patients and their conditions. With this, they can use machine learning to identify trends, patterns and anomalies in the data that can help experts make better-informed decisions.
Data analysis is fundamental to help personalise clinical trials, in comparison to the traditional approach, where lots of different people are given the same drug or treatment and results are determined using a statistical approach, focusing on how the majority react. This is not a ‘personalised’ solution, as every human being has a unique genetic make-up and specific biomarkers. Therefore, drug efficacy can differ from person to person – and this should be reflected in the way clinical trials are carried out.
The importance of a 360-degree patient view
Every person has a unique variation of the human genome. So being able to understand which genetic differences cause a specific illnesses or condition, is key for clinicians to predict what health issues a person may suffer from and prevent it from developing.
To be able to provide personalised medicine to this extent, a full view of every patient must be available. However, the ability to do this depends on being able to collect, map and analyse insights from vast amounts of data across disparate sources, which importantly, requires the help of technology. For context, it would take the equivalent of the sun’s output power for a whole week just to model a single human’s genome. Clearly, personalised healthcare at scale isn’t a manual undertaking.
AI is crucial for the next level of personalisation
This is where AI can offer huge benefits, by solving the key challenges healthcare providers face when it comes to big data. With AI and machine learning capabilities, pharma companies can collect, store and analyse large data sets at a far quicker rate than by manual processes. This enables them to carry out research faster and more efficiently, based on data about genetic variation from many patients, and develop targeted therapies effectively. In addition, it gives a clearer view on how specific groups of patients with certain shared characteristics react to treatments, helping to precisely map the right quantities and doses of treatments to prescribe.
As a result, clinicians can provide a better level of care to patients. In an ideal world, we want to prevent disease, and by having more information about why, how and in which person diseases develop, we can introduce preventative measures and treatments much earlier, sometimes even before a patient starts to show symptoms.
Evidently, personalised medicine has the potential to improve and even save lives. AI and machine learning are a driving force behind making future breakthroughs. There are still many challenges that lie ahead for personalised medicine, and still a way to go for it to be perfected, but as AI becomes more widely adopted in medicine, a future of workable, effective and personalised healthcare will certainly be achievable.