The past year has taught us many things about the healthcare sector – not least the extraordinary impact of big data analytics. As healthcare researchers and clinicians have gathered more data about Covid-19, from the genomic sequence of individual variants of the virus, to the typical progression of symptoms among different demographics, to the effects of various interventions, so the global response to the pandemic has become more intelligent and more efficient.
This is data analytics in healthcare in action – and as the world becomes ever more globalised, so it will be an increasingly important tactic in keeping populations healthy and well.
Let’s take a closer look at some of the applications of data analytics in healthcare.
Effective diagnosis depends on data – from patient medical histories, to interpretations of test results including X-rays and scans. Until recently, analysis of this data was down to individual clinicians, and their ability to keep up-to-date with the wider research landscape.
Data analytics in healthcare means that vast quantities of diagnostic information can be analysed and compared en masse. This enables patterns to be spotted, whether in symptoms, test results or scan images, and massively extends the opportunities for sensitive and proactive diagnosis.
One size does not fit all as far as health and medicine is concerned, and data analytics in healthcare can help fuel extraordinarily tailored, personalised approaches. By combining data on the effectiveness of different interventions on different patient groups, with detailed information on the profiles of individual patients, data analytics can make proactive suggestions as to precisely the right combinations of treatments. Medical history, physical profile and even environmental conditions can all be accounted for, as well as nuances like, as we have seen over the past year, the particular strain of a bacterium or virus affecting and individual.
Wearables and connected devices
Wearable devices for monitoring key health metrics such as heart rate, steps walked or run, sleep patterns and so on have well and truly hit the mainstream. But such wearables have implications for healthcare far beyond enabling individuals to keep better track of their health and wellbeing.
Data gathered from connected devices – whether consumer wearables or more specialist devices such as heart rate monitors, blood pressure monitors, insulin detectors and even implanted devices such as connected pacemakers can enable clinicians to monitor patient health anytime, anywhere. Connected devices can provide a window into individual health over time, and alert both individual and those who care for them if particular metrics hit a worrying level.
Indeed, in this way, wearable and connected devices overlap heavily with the social care sector, with myriad opportunities for big data analytics to monitor the health of the elderly and vulnerable, and proactively identify when they might need more support in the home.
In order to leverage these effects, healthcare organisations – and the technology providers that supply them – need to prioritise approaches and infrastructures which enable big data analytics smoothly and effectively. This means prioritising integration and interoperability. The healthcare sector is huge and complex, with hardware and software from myriad different suppliers. These technologies increasingly need to be able to interconnect and share information with each other. Data analytics cannot work effectively when the data in question is siloed. It also means implementing robust digital health platforms, which can effectively draw together healthcare data from multiple different sources, run detailed analysis and transform that data into tangible, actionable insights.
Data analytics in healthcare is not straightforward – datasets are complex, dynamic, and often highly sensitive – but getting it right can truly transform public health.