I was very struck by the example of a breakthrough population health initiative reported by my colleague Boris Rachev in his recent blog on the data deluge in healthcare.
By Lisa R. Esch, Population Health Innovation, CSC
In an early echo of what today we call big data and analytics, English physician John Snow used real-time data to identify the source of a cholera outbreak in London in 1854. Snow even identified the importance of “volume” and “velocity” in analytics, regretting that he wasn’t able to collect sufficient data quickly enough.
What was crucial about Snow’s analysis is that he allowed the data to lead him to an unpalatable conclusion. Sceptical of the existing anecdotal theories about the causes of cholera, he used data to build a hypothesis linking cause to effect. A hypothesis that was initially rejected by politicians, but which has come to have a profound impact on public hygiene and public health.
There’s a strong argument that public hygiene was one of the single biggest contributors to the rise in wellbeing and life expectancy in the 20th Century. That contribution was underpinned by pioneering work in epidemiology, with Snow’s activity perhaps the earliest example.
So, there’s nothing new about population health. We’re building on principles that have been established for nearly two hundred years. What is new though is the confluence of a rich fabric of health data driven by the increasingly widespread adoption of electronic medical records, and the availability of cognitive analytics tools.
What would John Snow make of the opportunities available to enable public health in the 21st Century? We can only speculate, but I’d guess at three things.
- He’d view health data and analytics capabilities as the most important clinical tools available to him.
- He’d be looking for unexpected patterns.
- And he’d be impatient at the rate of adoption of sophisticated, data-driven approaches to population health.
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