Intermountain healthcare big data analytics thrive with team input

'Hotspotting,' or improving care for high-cost patients via healthcare big data analytics, is the subject of a five-year Intermountain project.

Take a five-year, longitudinal study of a health system's most expensive patients; break them out by ZIP code, by treating facility and by the top 1% vs. the top 5%; and compare them to national-average benchmarks. Now, develop clinical interventions to both improve the patients' care and reduce its cost.

Scott PingreeScott Pingree

Sounds like a next-generation project for a provider's healthcare big data analytics team. Yet it describes an initiative called hotspotting that Utah's Intermountain Healthcare has been perfecting for the last five years.

In this podcast, Intermountain's Scott Pingree, director of strategic planning and chair of high-cost-patient hotspotting, discusses how data analysts must work together with IT and clinical leaders -- along with staff from other departments -- to create a successful big data project that produces usable results. He also discusses how, in conjunction with his clinical colleagues, Intermountain tracked down and addressed unusually high incidents of renal failure through examining the data it collected for the analytics project.

Pingree offers advice on how to work as a health system to set up a big data project, and gives his opinion on the future of healthcare big data analytics technology: how far new technologies can automate it versus how much data scientists will always have to roll up their sleeves and work themselves to help clinicians achieve outcomes' goals.

Let us know what you think about the story; email Don Fluckinger, news director or contact @DonFluckinger on Twitter.

More on healthcare big data analytics:

Much has been said about the potential of big data analytics. Is there finally concrete proof to share with those who don't believe the hype?

Some schools of thought believe that massive quantities of data will reveal actionable trends in patient data. Our expert explains why insights should be drawn from high-quality data.

Providers share their experiences with big data analytics and tell why they feel it's best to deploy it in specific situations.

This was first published in May 2014

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