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The Thorniest Barriers to Robust Data Analytics? Panelists Uncover a Tangle of Them

August 19, 2015
by Mark Hagland
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Panelists at iHT2-Seattle take on some of the thorniest barriers bedeviling attempts at robust data analytics

How can data really be made useful to efforts to improve patient care outcomes and engage in population health initiatives? Panelists participating in a discussion around data analytics plunged into some very thorny issues in healthcare, during an afternoon panel discussion on Aug. 18 at the Health IT Summit in Seattle on Aug. 18, being held at the Seattle Marriott Waterfront, and sponsored by the Institute for Health Technology Transformation (iHT2, a sister organization to Healthcare Informatics, under the corporate umbrella of parent organization Vendome Group, LLC).

The panel, entitled “Analytics: Integration, Standards, and Workflow,” ended up tackling some of the most vexing issues facing healthcare leaders who are attempting to fully leverage data analytics for clinical performance improvement, cost reduction, population health management, and other purposes.

Zachery Jiwa, former innovation fellow at the U.S. Department of Health and Human Services, led the discussion. The other panelists were David Chou, M.D., chief technology officer at UW Medicine (Seattle); Dean Field, M.D., vice president for informatics and operations at the Tacoma-based CHI Franciscan Health; Steve Weiss, R.N., CNIO for the Seattle-based Swedish Region of Providence Health & Services; Sean Kelly, M.D., vice president and chief medical officer at the Lexington, Mass.-based Imprivata and a practicing emergency physician at Beth Israel Deaconess Hospital in Boston; and Erik Giesa, senior vice president for informatics and operations at the Seattle-based ExtraHop Networks.

panelists (l. to 4r.) Geisa, Chou, Field, Kelly, Weiss, and Jiwa

Among the problems inherent in the current struggle to leverage data for analytics purposes, Weiss noted, is the fact that such efforts have been relatively recent overall, and have followed a number of years focused on electronic health record implementation and on the creation of some informatics foundations, including the creation of data warehouses. “Early on,” Weiss noted, “we were really working on the EHR, and we weren’t necessarily capturing data discretely; instead, we were focusing on getting people on board. And as we progressed, we focused on moving onto enterprise data warehouses and registries, and beginning to work on data definitions. That’s about where we are now,” he said. “It would be great to move into data definitions in communities,” he added. “We want to continue to work on population health. The problem is that definitions in medicine are difficult.”

We’ve been live on our current EHR for two years now,” Field reported. “And while we’re still in our infancy on our implementations, now suddenly, we’re realizing we need to be able to pull data out of it. And now that we’re in this adolescent phase, we’re still very much reactive, reacting to CMS [policy mandates from the federal Centers for Medicare & Medicaid Services], reacting to other external pressures, and not necessarily following our own vision.”

“Two things are necessary” to begin to leverage data analytics robustly, Chou asserted : “a useful vocabulary, and understanding data context. I don’t think either of them are at a satisfactory level yet,” he said. “And the consistent practice of medicine isn’t there yet, either.” In fact, Chou said, one fact that should sober any leaders attempting to move forward to robustly leverage data analytics, is this one: “There are something like 690 definitions of glucose” in EHRs and other clinical information systems, he noted. “And that’s a disaster. And that’s assuming that they all mean the same thing, which they don’t. So you have to decide what you’re going to map to and map to. And every time I go through an interface, I lose information. And with regard to, for example, blood pressure, I don’t even know what the information is around the blood pressure, I don’t have the context. So,” he said, looking at an analytics landscape that encompasses clinical, technological, policy, and practice challenges, “you have to understand the practice of medicine, and you have to have the context. And eventually, without that, you’re going to drive the clinician crazy.”

EHRs never designed for analytics work

A very simple reality is also very important to keep in mind, Giesa said. “When you look back at the design, from as much as 30 years ago, of the EHR, and you look at how we’re now trying to apply it to analytics, it’s like trying to turn a square into a wheel now. And when I hear terms like data mapping, I want to note that the practice of medicine isn’t standardized or structured,” he emphasized. “When they built these applications, they did not anticipate using them for analytics or informatics. So there’s a new paradigm emerging now around structuring data in unstructured data stores, giving you the flexibility to not necessarily have to do data mapping.”

It might seem like a stretch to apply such informatics concepts to patient care, Giesa said but he noted that “That’s something that applications like LinkedIn and Facebook do: the same principles apply. You have one user who might be doing five, 20 different things, interacting with all sorts of different applications, but at the end of the day, that user wants to see what they want to see. All of that relies on structured data being put into unstructured contexts for end user use. I don’t believe that the structures around EHRs were designed to do what we’re trying to accomplish.”


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