One story and one story only has dominated the public health headlines this year. But as the months have gone on, so the discussions of Covid-19 have become more multifaceted. The battle to control the spread of the virus, and the race to develop a vaccine, might still be the most high-profile elements of the Covid-19 conversation. But just below the biggest and boldest headlines are a raft of are other debates and discussions – including many pertaining to personalised medicine.
It has become increasingly apparent that Covid-19 is a disease with considerable variability in symptoms, severity and clinical course. Whilst most who have caught the disease in the UK have recovered relatively quickly, the ‘long Covid’ phenomenon is having a dramatic effect on some people’s lives, and remains poorly understood by clinicians. Risk factors are becoming better understood, with age and obesity recognised as among the most significant – but some young and otherwise fit and healthy individuals are still suffering with severe instances of the disease.
Through treating many thousands of Covid-19 patients over recent months, healthcare practitioners have developed vastly more sophisticated understandings both of this variation, and of the treatments most likely to have a positive effect. At its simplest level, this is learning based on the experience of the past few months – doctors, nurses and other clinicians have ‘determined through doing’ which treatments are likely to be most effective. Myriad formalised clinical trials have also taken place, determining with greater precision which interventions are working best. This is truly is evidence-based medicine in action.
However, evidence-based studies require large enough groups of patients who are alike enough to make the study reliable and rational – and the very heterogeneity of both Covid-19 patients, and the apparent pathways of the disease, makes this particularly challenging. Indeed, that heterogeneity is underlining the complementary value of personalised medicine, which takes into account the individuality of each patient. Yes, a particular patient might be in their sixties – but they might also have a particular set of pre-existing conditions, and have presented with a particular set of symptoms, which mean that they are better treated with a slightly different set of interventions to a broad ‘over sixties’ category.
Personalised medicine, just like evidence-based medicine, relies on data. It depends on the ability to collect a large enough volume of data from a large enough number of patients – and the ability to analyse that data efficiently. This is where the Internet of Things (IoT) era comes in. Connected healthcare devices are making it quicker, cheaper and easier than ever before to collect individualised data on an ongoing basis – such as key medical information like blood pressure and heart rate. As we learn more about Covid-19, so such information can be used to understand why a particular patient might respond differently to both the disease and its treatments, even if they fit into a broader category such as an age band.
Both evidence-based and personalised medicine are, and will continue to be, critical in the fight against Covid-19. And this means that access to clean, comprehensive and meaningful data – and the ability to analyse and make sense of that data en masse – are crucial too.