Population Maven
David Nash didn’t set out to be a rock star of population health, but he was in the right place at the right time with the right professional interests. A board-certified internist, he was chairing the health policy department at Thomas Jefferson University when its president floated the idea of building a school of population […]
David Nash didn't set out to be a rock star of population health, but he was in the right place at the right time with the right professional interests. A board-certified internist, he was chairing the health policy department at Thomas Jefferson University when its president floated the idea of building a school of population health-the first in the country when it opened its doors in 2008 (and still the only one, although several schools have departments devoted to the burgeoning discipline).
"We knew something big would happen eventually [with health reform], and we recognized that schools of public health weren't producing the right people," Nash says. The school has grown, with multiple graduate degree and certificate programs offered both on-site and online, and it's recently been promoted to a full-fledged college within Thomas Jefferson University. Meanwhile, Nash crusades for a population-based approach to quality care and improved health outcomes. He does so through participation on a multitude of committees and task forces, as well as writing and dozens of scholarly articles. He talked with Health Data Management recently about how provider IT departments should be preparing for the demands of population health.
On registries
Job One is the creation and use of a robust registry, one that a techno-dummy like me can use with no instruction manual. I still don't have the ability to flip open my laptop and bring up a list of all my patients who have diabetes and answer the question "How am I doing?" I want big print and lots of pictures.
On simplicity
There's an obsession with trying to connect every last tick of data and losing sight of the forest at the level of the leaf. We think we have to have everything: labs, inpatient records, claims and billing data. You don't have to connect every last dot when the first five dots will give you what you need to know. If you try to connect every dot, you risk creating something too complicated to work.
On outside data
We have to harness the disparate data sets that are not inside the Epics and Cerners: Do you own a car? Is there an adult at home during the day? Have you had prior hospitalizations outside our system? What food do you buy? What's the risk of diabetes in this ZIP code? Then I can put together a scary but highly predictive model of who's going to get pre-diabetes and then type 2 diabetes. And I can invite them in for an exercise class or nutrition counseling.
"We knew something big would happen eventually [with health reform], and we recognized that schools of public health weren't producing the right people," Nash says. The school has grown, with multiple graduate degree and certificate programs offered both on-site and online, and it's recently been promoted to a full-fledged college within Thomas Jefferson University. Meanwhile, Nash crusades for a population-based approach to quality care and improved health outcomes. He does so through participation on a multitude of committees and task forces, as well as writing and dozens of scholarly articles. He talked with Health Data Management recently about how provider IT departments should be preparing for the demands of population health.
On registries
Job One is the creation and use of a robust registry, one that a techno-dummy like me can use with no instruction manual. I still don't have the ability to flip open my laptop and bring up a list of all my patients who have diabetes and answer the question "How am I doing?" I want big print and lots of pictures.
On simplicity
There's an obsession with trying to connect every last tick of data and losing sight of the forest at the level of the leaf. We think we have to have everything: labs, inpatient records, claims and billing data. You don't have to connect every last dot when the first five dots will give you what you need to know. If you try to connect every dot, you risk creating something too complicated to work.
On outside data
We have to harness the disparate data sets that are not inside the Epics and Cerners: Do you own a car? Is there an adult at home during the day? Have you had prior hospitalizations outside our system? What food do you buy? What's the risk of diabetes in this ZIP code? Then I can put together a scary but highly predictive model of who's going to get pre-diabetes and then type 2 diabetes. And I can invite them in for an exercise class or nutrition counseling.
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