Geisinger CEO gives tips for BI
Glenn D. Steele Jr., MD, president and chief executive officer of Geisinger Health System, says the integrated delivery network's pioneering population health programs depend on insightful use of data to drive behavior change.
Serving 43 counties over some 20,000 square miles of northeastern Pennsylvania, Geisinger provides care for more than 2.6 million people; its innovative efforts to combat diabetes, coronary disease, COPD, hypertension and other chronic ailments have been held up as examples for the rest of the country.
When Steele delivers the keynote at the HIMSS Media Healthcare Business Intelligence Forum, April 16-17 in Washington, D.C., he'll offer lessons from Geisinger about deploying analytics tools to improve patient care and the bottom line. He'll also stress the importance of making sure data changes behavior and thought processes – not just of patients but of providers and payers, too.
In a Q&A with Healthcare IT News, Steele discussed Geisinger's strategies for deploying analytics for care coordination, emphasized the "critical" importance of integrating clinical and financial data, pointed to specific cases where BI has borne fruit for Geisinger and offered advice for smaller providers looking to make the most of their data.
Asked how is healthcare in general doing these days when it comes to using business intelligence and analytics, he chuckled. "Well, we're probably about to enter the 19th century."
What's standing in the way of smarter use of data?
"All the reasons that you and I could talk about for a week," said Steele. "We have legitimate regulatory concerns, and I think they've always taken precedence over true innovation in terms of how we look at our data, how we analyze it, how we distribute it, how we use it to change behavior. I don't think the balancing act between innovation and regulation is correct in most areas of healthcare data."
He added, “We also have the intrinsic structural issue in healthcare, where it's been compartmentalized on both the payer and provider sides, and each of those seems to strive for an optimal function without actually any integrated structural aspirations. And that's changing as well, but obviously when you sell IT enabling into all those compartments, you're kind of handicapped right from the start."
Thankfully, improvements in technology, coupled with shifts in attitudes and awareness, are changing the way analytics is put to work in healthcare.
Geisinger, of course, is an early leader in making the most of its clinical and financial data, and while it's clearly a unique case, its experiences could perhaps prove illustrative to other, smaller providers looking to make some inroads of their own.
"Our top strategic aim is innovation and quality. Our structural advantage that we talk about internally and externally over the past 15 years is this payer-provider sweet spot," said Steele. "We've tried to figure out how our version of vertical integration between payer and provider can really optimally mesh the information and the use and analysis of it from both side of the house."
None of that data analysis is worth anything, however, unless it effects "change in behavior," he said. "We realize that in order to capture the 30 to 40 percent of value which is now lost because of stuff in healthcare that doesn't help human beings, actually hurts them, in order to capture that, we have to fundamentally change behavior, and we have to change it on behalf of our providers as well as our patients – and on the insurance side of the house."
One of the biggest advances at Geisinger has been the success of its ProvenCare program, which puts evidence-based standards and patient engagement to work in the service of fixed-price procedures. Data has been key.
"We have come to believe that a huge amount of our value reengineering, whether it's hospital-based care episodes or whether it's taking care of patients with multiple chronic diseases, does two things," said Steele. "Number one, it gives better outcomes, both near-term as well as long-term. Number two, it decreases a lot of the cost.
"There's a lot of things that realization has allowed us to do," he added. "One of them is it allows us to look at cost as a surrogate for bad outcome. So if on the insurance company side you see a cohort of high-cost patients, we pinpoint and target, and have both payer and provider engaging in a discussion of how can we improve those outcomes for that cohort of patients."
One key to reaping the most benefit has been Geisinger's consolidation of its Clinical Decision Intelligence System, which brings many types of data sources – EHR, insurance claims, even patient satisfaction scores – together in one place.
"It's critical; it's absolutely critical," said Steele, "but we made the commitment to do the data warehousing because we wanted to ask questions about how many of the people we were responsible for – in our 41- or 42-county service area at that time – how many people had osteoporosis? How many people had been tested with DEXA (bone density scans) for osteoporosis? And how many of those had actually had a primary care physician list osteoporosis as one of the entities on the problem list? And then how many of those folks had been treated for osteoporosis appropriately? You can't ask any of those questions without a data warehouse."
What about smaller providers, who might not have the resources or wherewithal of a giant like Geisinger? Does Steele have any practicable tips for putting BI tools to work?
"I think the biggest piece of advice is to look at the functionality you want first," he said. "Understand the nuance of the various systems, whether they're hospital-based or ambulatory, or what have you, and then work backwards, from, 'What is it going to take to get a different functional outcome than the one I'm getting now?' – and there's almost no outcome that can't be improved."
He added, "The second thing to consider, which I think is very important, is, 'What is my capacity, based on my structure, based on my size, based on my access to capital, what is realistic to me? And if I can't achieve the optimal function, how am I going to have to change? What can I do that has a great probability of success, early on?'"