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Duke Health Leaders Build the Predictive Analytics Foundations to Improve Patient Care

October 10, 2017
by Heather Landi
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Across the country, leading health systems are building the data architectures that are necessary to leverage business intelligence tools and analytics for effective population health and proactive disease prevention. At the Durham, N.C.-based Duke University Health System, health IT leaders are moving forward on many fronts to leverage data analytics to improve patient care, such as early sepsis detection.

“It’s a very exciting time in healthcare, as more and more people understand and appreciate the power of analytics. We are certainly going beyond the traditional flavors of business intelligence with pre-defined reports, which of course will have its place, and migrating to using predictive modeling and machine learning to figure out how to tailor care for both individual patients and populations of patients,” says Eric Poon, M.D., chief health information officer at Duke University Health System. Poon also is a professor of medicine at Duke University School of Medicine and he practices general internal medicine at Durham Medical Center, part of Duke Primary Care. Poon also earned a Master’s in Public Health from Harvard University.

The Duke University Health System encompasses three hospitals—Duke University Hospital, Durham Regional Hospital and Duke Raleigh Hospital—as well as physician practices, home hospice care and various support services across North Carolina.

In his CHIO role, Poon aims to align technology solutions with organizational objectives, the clinical and analytic information systems that impact patient care. For Poon, a big focus right now is forging the necessary collaborations and partnerships across Duke Health in order to bring the power of data science to bear in the context of healthcare.

“Traditionally, the folks who we need to bring together include the clinical and operational folks who have insights into the challenges that they themselves face, or they see their patients face. And then you need to marry that up with the data scientist who can apply a variety of methods to help solve the problem, and then both parties need to come together and access the right data in order to do any meaningful data science. That collaboration is essential, and we are making a lot of inroads in helping all those three parties understand each other’s capabilities and provide the glue to keep those projects going,” Poon says.

Eric Poon, M.D.

And, he adds, “I think these areas, which are exciting and new, require people with different skillsets to come together and those skillsets typically sit in different pockets of the organization. How to support the collaboration and building the trust, that, I think, has to be a continued focus for those in leadership positions.”

Poon will join other healthcare IT leaders to discuss this topic, as well as other topics related to healthcare IT in North Carolina and the region of the Southeast U.S., October 19 and 20, during the Health IT Summit in Raleigh, sponsored by Healthcare Informatics, and held at the Sheraton Raleigh Hotel Downtown.

In the past six months, Duke Health’s IT and analytics leaders have made significant progress in their work to build the foundations for predictive modeling, Poon notes. “Making sure that we provide high-quality data sets to inform the model building is a key step. In addition, making sure that there is the infrastructure to house these data sets in a secure way and putting it behind a secure storage environment, making sure we have the appropriate tools and computational power to support the activities of the data scientists, these are issues that we are very actively working on and I think we’re making a lot of progress,” he says.

The next mile of the journey, Poon notes, is to get the insights from those predictive models back into the hands of clinicians at the point of service. “After all, a data science project that results in a manuscript is not, by and of itself, going to help any individual patients. So, we are having a lot of conversations about how to effectively get those insights back into the hands of the right clinicians at the right time and at the right point in the workflow. And, then, think about how to do it robustly and think about how it articulates with state of art in terms of the standard of care, and it’s something that I’m very excited to be working on,” Poon says.

Poon and other IT leaders at Duke Health are currently working to leverage predictive analytics models to help clinicians identify the early signs of patient deterioration and to identify patients developing early signs of sepsis. “Both projects are at a point where they have built really promising predictive models and they are actively working with the data scientists to figure out how do you get those predictive analytics back into the right workflow,” Poon says. “I think it’s very important to make sure that we understand how these predictive scores can and should be used in the processes of care, and we want to make sure that we adhere to standards of care, and where the standards of care do not give further guidance, this is where these predictive scores can be very helpful.”

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