Finding the Signal in the Noise
If you’ve ever been in hospital, you’ll know that nursing staff regularly come round to your bedside to take a set of routine observations – usually every hour. These typically consist of measuring and recording your blood pressure, temperature, heart rate, and oxygen levels. It’s always struck me as a terrible waste of a nurse’s time. Not because these observations aren’t important indicators about our health, but rather manual, repetitive tasks such as this can, and should, be automated – particularly in time poor and resource stretched environments such as healthcare. And the making of mistakes can be easily avoided.
Of course, it’s just a matter of time before they will be. In the not too distant future, we’ll see wearables on every patient designed to monitor such routine measurements, connected to personalised patient record systems, and capable of flagging sudden changes in our health or providing early warnings of identified patterns. This is just one example of how the Internet of Things will change medicine. Whilst you can’t really turn up to the doctors with the output of your Heart Rate Monitor, you’ll probably soon be able to.
Technology has simplified and automated just about every other area of our lives. Our spending habits, travel preferences, and search criteria are all now personalised, so it’s only logical that our medical care follows suit. But of course, before you can personalise at scale, you must digitise. And this starts by addressing the huge data proliferation that’s historically been the quagmire of the healthcare industry for so long.
Most of us know that managing big data doesn’t need to be a big challenge anymore – whether it’s increasing the effectiveness of cervical cancer screening in Kenya or managing the health queries of an entire country in Scotland’s NHS 24 – faster analytics and better insight means earlier diagnosis and higher success rates in patient outcomes.
Take NHS 24, for example. More than 1,600 full-time staff members answer about 1.5 million calls per year in centres across the country, serving a population of approximately five million people. NHS 24’s mission means they need to be able to manage and make effective use of enormous volumes of data resulting from hundreds of thousands of customer calls and patient records, as well as the knowledge assets within the organisation itself. With HANA, they can achieve this easily at the touch of a button with both speed and flexibility, and are anticipating significant cost savings as a result.
By streamlining the data, and empowering healthcare professionals with the right information, organisations such as NHS 24 can understand the micro requirements of both the individual patient, as well as the macro demands of meeting the government’s long term health objectives for its citizens.
It’s these sorts of analytics and visualisation techniques that are becoming increasingly important in finding ‘the signal in the noise’ for hospitals, medical researchers, NGOs and pharmaceutical companies alike. As IoT evolves, our data and devices will inevitably become more integrated. Real time data entered into a smart phone app will one day turn mobile phones into mainstream medical devices. Labs can send test results directly to patients’ phones, and sensors and apps can help doctors detect ailments. When combined with big data, mobile technologies could also enable scientists to better understand and treat disease, giving researchers greater insight into patterns across populations.
In the same way that consumers have become empowered, we as patients will also have more direct involvement in the handling of our medical data. In fact, in the near future, it will be us who will own our own data, rather than our GPs, clinicians or hospitals. We will gather and enrich our medical profiles to provide a much more comprehensive, accurate and unified view of our health (with the right security provisions in place of course).
I think we’re on the cusp of a new era in digital health. One that will ultimately provide us with better prevention, diagnosis, treatment, and outcomes. More importantly, the efficiencies of proactive personalised care, combined with previously untapped intelligence, and machine to machine learning will improve the quality, value and timeliness of our medical experiences as patients.