On Tuesday at the Health IT Summit in Nashville, sponsored by Healthcare Informatics and taking place at the Sheraton Downtown Nashville, attendees were offered a very vigorous discussion of the opportunities and challenges around the leveraging of data analytics in patient care organizations. The panel discussion, entitled “Forward-Thinking Examples of Analytics in Modern Healthcare Settings,” was moderated by Michael Hamilton, vice president, analytics, at Albany (New York) Medical Center.
Hamilton was joined on the panel by J.D. Whitlock, vice president, enterprise intelligence, at the Cincinnati-based Mercy Health; Bob Cawley, CIO at the Glens Falls, N.Y.-based Adirondack Health Institute, an independent, non-profit organization that partners with providers and patient groups to transform health in northern New York state; and William Paiva, executive director at the Stillwater, Oklahoma-based Center for Health Systems Innovation at Oklahoma State University.
“As we had agreed to do,” Hamilton said, “we wanted to discuss forward-thinking examples of analytics. A good place to start might be to discuss some of the challenges we all face in our current system of data.”
“There’s no shortage of challenges, in that regard,” Adirondack’s Cawley said. “The important thing to know is that we’re a network of providers, not a healthcare system. And that has a lot of implications for how we implement technology. We were formed in 1987, but underwent a significant transformation in 2007, when we started the Adirondack Home Health Network. It was then that we got everyone on interoperable EHRs [electronic health records], connected to a RHIO [regional health information organization], and established a regional clinical quality dashboard, and a regional all-claims database. In addition,” he said, “New York state stated a DSRIP program [Delivery System Reform Incentive Payment Program] in 2014. Adirondack joined as a participating provider system. Among the key areas of focus in the DSRIP program has been reducing avoidable high-cost utilization including ED utilization and inpatient admissions.”
What’s more, Cawley noted, “We’re also very rural, with only 700,000 people across 11,000 square miles. We have five or six hospitals; the largest is 300-350 beds, while most are small. Within the hospitals and primary care, we have pretty good adoption of primary care and adoption of the RHIO, but even within primary care, there are 15 different EHRs we’re dealing with. And nursing home, long-term care, behavioral health, if they are on an EHR, they’re not connected to the RHIO. And given that 25 percent of our providers are financially challenged,” that fact in itself is very challenging, he added. “We don’t have the resources to connect all those entities, so we’re relying on the RHIO to connect all of us. The other main source of data is the state government; since DISRIP is a Medicaid program, the primary payer for us is the state of New York, so we’re attempting to get data that way. So, trying to get all this information together is the first challenge,” he said. “If you’ve ever tried to integrate data from EHRs, you’re familiar with the amount of remediation work needed, and what’s needed to achieve the fantasy of marrying clinical and claims data and then use it.”
“I’ll answer that question a little bit differently,” Paiva said. “We’re blessed that by the time the data comes to us, it’s cleaned up as much as possible. So we don’t deal with all the informatics issues. So once we’ve got the data, what do we actually do with it? We’ve spent the last three years figuring out what to do with the data and how to provide tools meaningful to the healthcare system. A few things we tend to focus on, as it relates to getting value out of your data: whatever tool or product you develop needs to be clinically relevant and timely. You have to provide tools that allows people to do something they couldn’t do otherwise. So it needs to be clinically relevant and to help them to do something they couldn’t do before.”
(l. to r.:) panelists Whitlock, Paiva, Cawley, Hamilton
Further, Paiva said, “We need to stop thinking about it as an either/or—either the physician or the tool. It can be both. We were able to reduce error rates for diagnosing breast cancer by 85 percent, when an algorithm we developed was used together with a pathologist diagnosis process. These tools are really designed to help clinicians. And the last thing I would say is that you need some sort of benefit to physicians beyond the clinical—either financial, to help manage a population, or to manage some kind of MACRA or CPC+ credit,” he said, referring to the Medicare Access and CHIP Reauthorization Act of 2015, and the Comprehensive Primary Care Plus program. “Here’s one story of a tool we developed that has been successful,” he continued. “We had surveyed rural physicians on their challenges. And one of the ones they mentioned was managing diabetic patients in rural areas, where they don’t have many specialists. So we focused on diabetic retinopathy, because only 10 percent of diabetics in rural areas get eye tests. We asked, could we develop a tool from the data collected in the primary care setting, to predict whether the patient has diabetic retinopathy? So we built an algorithm based on primary care visits, comorbidities, and other data. It solves a rural-physicians problem and addresses requirements under MACRA and MIPS [the Merit-based Incentive Payment System]. That’s an example of how to build tools that work and are useful.”
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