The journey of healthcare leaders towards optimally leveraging data and analytics for population health strategies remains a long and daunting one for leaders in both Canada and the United States. That was the consensus of discussants during a panel entitled “Data & Analytics: Driving Population Health Management,” which was held on Friday, Sep. 18, during the Health IT Summit in Vancouver, sponsored by the Institute for Health Technology Transformation (iHT2, a sister organization to Healthcare Informatics, under the Vendome Group, LLC corporate umbrella).
The panel was moderated by Trevor Strome, manager, Informatics & Process Improvement, in the Emergency Program at the Winnipeg Regional Health Authority (Winnipeg, Manitoba). Strome was joined by Jat Sandhu, Ph.D., regional director in the Public Health Surveillance Unit at Vancouver Coastal Health Authority (Vancouver, British Columbia), Alyssa Daku, vice president for strategy, quality and risk management at eHealth Saskatchewan (Regina, Saskatchewan); Larry Svenson, director, epidemiology and surveillance, Alberta Health Services (Edmonton, Alberta); and Eugene Kolker, chief data officer at Seattle Children’s (Seattle, Wash.).
One of the initial challenges that discussants talked about was that around defining the population health concept to begin with. “I don’t get the sense that we have a really clear understanding and agreement of what population health management really means,” Strome said, as he opened the discussion Friday afternoon.
“I agree, I think the term population health management is overused, particularly in the U.S.,” Sandhu responded. “In Canada, we think about it rather differently. It’s about the organization or management of health delivery focused on achieving outcomes that are effective and safe,” Sandhu said. “And at the crux, it’s about being proactive about the management of at-risk populations, making sure they don’t become too expensive to the system. So it’s about managing clinically defined populations or those that are frequent users of the system. And to be effective, we need to obtain good longitudinal data.”
Seattle Children’s Kolker said, “I would agree that it’s very important to understand populations at risk, and it’s not good to limit ourselves geographically; at the same time, we need to understand scale. It’s not only about the population that are frequent flyers. In an ideal case, looking at this with a ten-year perspective, I’d like us to keep populations healthy lifelong.”
One of the elements to consider, said eHealth Saskatchewan’s Daku, is that, “When you talk population health, it very much sits in the health sphere. As soon as you add ‘management’ into that,” she said, “there are so many variables that exist outside the healthcare system that exist outside our purview. It becomes incumbent on social services, education, and government, to become involved.”
Public health perspectives are important in that regard, said Alberta Health Services’ Svenson. “Because I work primarily in public health,” he said, “we’re always thinking about these non-health entities and how you integrate that in. Looking at children in care—they have lots of mental health visits and other interventions. Once the interventions take place, their utilization actually drops off. The other part of this is that, in Alberta, 5 percent drive 65 percent of costs; but that is not a stable, permanent group. It changes over time. And there’s valuable data coming from many places. Our biggest challenge in government is how you derive meaningful policy decisions that turn into meaningful care decisions.”
“Yes, as soon as you begin pulling educational, and social services, and correctional data, that adds tremendously to the complexity,” Daku agreed.
“I agree,” seconded Sandhu. “We need that other kind of data. And our challenge is that it’s not necessarily easily collected; so we tend to rely a lot on aggregate geographical data with which to do analysis. But that shouldn’t stop us. The methodologies around managing some of that data have been around for a long time. I’ve heard here a few things about how you incorporate new kinds of data, beyond the in-hospital and EMR data,” he continued. “We do have this much broader ecosystem, from which we need to be able to grab data to much better understand situations. So what are some of the best strategies for making the data analytics work for population health?”
Strategies for pursuing population health: a complex subject
Responding to that question, Seattle Children’s Kolker said that “I think that that’s a big question. I can share some of our experience,” he added. “At some point, we became focused on the backgrounds of our patients, our minority patients, for example. We talked to many people, including in social service organizations, and then our team was working on something completely unrelated, and realized we were seeing an unexpected slowing in our business. And we found that we needed to look specifically at our Asian population, to better understand what was going on with them.” Kolker explained that while the Asian-American population is the fastest-growing ethnic population in Seattle Children’s service area, the organization had a cultural perception problem in that community. “We discovered through research based on focus groups, that we needed to present ourselves as not only serving the sickest of the sick,” in order to better capture market share in that ethnic community. “So we changed our image over time. We accidentally uncovered a problem and were able to combine internal and external data of different types to understand our relationship with a specific population.”