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Making the clinical data warehouse relevant again

Brian Eastwood | June 4, 2014
The clinical data warehouse used to represent what was wrong with healthcare IT: An incomplete data source that was siloed to boot. But Texas Children's Hospital has turned its data warehouse into a valuable tool for clinical and operational analytics.

The hospital called an off-cycle, summer quality board meeting. Davis and her peers "threw ourselves out there" in sharing their vision, asking for an investment and committing to a nine-month timetable. "It was risky," Davis says, "but it worked."

Two years later, the Texas Children's enterprise data warehouse pulls from 12 systems, including supply chain, payroll and finance, and enables operational reporting. "This is very beneficial," Davis says, "because organization leaders were making executive, operational decisions with stale data, and the way I interpreted data in financial reports wasn't the same way that other leaders would." In the process, the reporting team, no longer fielding lengthy report requests, made the transition to an architecture team.

Future plans for the data warehouse include studying workforce productivity and expenses, Davis says. Meanwhile, on the clinical side, Texas Children's plans to target segments of the patient population with better care based on specific diseases and conditions.

Healthcare Innovation More Than Just Technology

Use cases like that drive Health Catalyst, Barlow says. The disruptive innovation that healthcare needs involves technology, yes, but also the quality improvement that technology can bring. As Barlow sees it, this involves an analytics system to track care standards, a deployment system for that care and an evidence-based content engine to redefine care standards. That's more than a data model, he says.

"Organizations who really want to make headway in population management and quality improvement as we move to a value-based purchasing model, an analytics solution alone won't solve the problem," Barlow says. "There also needs to be a robust data methodology and deployment system."


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