A very robust discussion around the challenges, opportunities, and incentives around population health management took place on Oct. 26, during the Health IT Summit in Washington, D.C., sponsored by Healthcare Informatics, and held at the Ritz-Carlton Tysons Corner in the Washington suburb of Tysons Corner, Virginia. Not only did panelists engage in lively discussion, numerous audience members gave impassioned comments and asked many questions, leading to a discussion that all those involved in agreed was a meaningful one at this moment in the evolution of the U.S. healthcare system.
Drexel DeFord, CEO of the consulting firm Drexio Digital Health, led the panel discussion, entitled “Population Health Strategies to Improve Outcomes and Coordination of Care.” He was joined by Sule Calikoglu Gerovich, Ph.D., director of the Center for Population-Based Methodologies at the Maryland Health Services Cost and Review Commission, in Baltimore; and Robert S. Rudin, Ph.D., an information scientist, at the Washington-based RAND Corporation.
“I want to start with the term population health,” DeFord said in opening the discussion. “Sometimes, it’s good just to define population health. And I think back to being a ‘recovering CIO,’ spending a lot of time talking about big data. I like the joke about population health being like teenage sex,” he said: “everyone talks about it, and everyone thinks everyone else is doing it, but a lot fewer people are doing it than people think.”
Gerovich said she is fully aware of some of the contradictions in creating population health strategies. “My background is in public health,” she noted, “and at Hopkins”—she received her doctorate from Johns Hopkins University—“the School of Medicine was on one side of the street, and the School of Public Health was on the other side, and we used to say that that was the widest street ever,” she said. “To me, population health is the seeping of public health concepts down to the provider level. Everyone is defining population health differently, but in the end, it’s thinking about prevention and outcomes.”
What’s more, Gerovich added, “In Maryland, we have all-payer rate-setting: Medicare, Medicaid, private payers, all pay the same prices. And the commission that I work with looks at the costs that different hospitals face, and regulates the charges and ChargeMasters at the hospitals. We’ve been thinking about population health, and how we can incentivize providers when they come to their door and beyond.”
(l. to r.) Drexel DeFord, Sule Calikoglu Gerovich, Ph.D., and Robert Rudin, Ph.D.
“I agree with that,” Rudin said. “The key aspect of what makes something a population health program is that it is proactive, as opposed to reactive. It’s about not waiting to treat a patient, but devising strategies to identify [at-risk] patients ahead of time. In our study, we found three goals within this population health strategy. The first basic goal is to identify high-risk patients; the second goal is to identify the subset of patients who can be helped and treated—and there’s a big drop-off between who’s doing #1 and #2. And goal three would be to identify subsets of patients and match them to specific interventions. Very few are yet doing #3. In a lot of models, the variable most predictive of hospitalization is previous hospitalization; but the whole goal is to try to prevent the first hospitalization. So the predictive capability is still very poor; really, no one’s doing it really well right now.
“I’ve always thought of it as being like a soccer field,” DeFord said: “you’re trying to intercept the patient before they cross the mid-field line—and if you can keep them far, far away from the sick end of the soccer field, even better. Meanwhile, Bob, you mentioned analytics—can you talk more about that?”
“There’s a near-consensus here: in terms of predicting who the high-risk patients are, most everyone agrees that the big bottleneck is in the data, not the analytics.,” Rudin responded. “The gap is in the data: there are major problems in using data to predict. We found three categories for data. Claims data has some advantages: it’s longitudinal, it covers multiple doctors; but it’s very limited in terms of the advanced kinds of information needed. EHR data is richer, but it’s limited by organization. It’s also very dirty. Problem-list data is pretty well known to be wildly inaccurate and incomplete. So EHR [electronic health record] data’s got a lot of problems. Also, there’s a lot of missing data. And as Dr. Williams said a little while ago, some of the most predictive data is around social rather than clinical variables. The third type of data would be around the patient him/herself. The problem is that there are so many types of data that might be relevant. For example, use of a wood-burning stove is very predictive of respiratory illness. Now, are you going to ask all of your patients whether they use wood-burning stoves? There are probably not a lot of patients in New York City that are going to answer affirmatively. There are also problems not only with input but also data output. Change, the dreaded ‘c word.’ This requires training and other elements as well. So we have a long way to go to become truly predictive.”
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