Rush CIO Lac Tran on using predictive analytics to improve outcomes, efficiency

Rush University Medical Center counts on predictive analytics for a number of quality and efficiency improvement efforts--from reducing readmissions to treating patients at risk for stroke and cardiac arrest quickly and efficiently to reducing wait times, diversions and boarding in the emergency department.

Rush's CMIO, Julio Silva, M.D., will talk about these programs and share results at an executive breakfast panel discussion, on Wed. March 6, during the HIMSS 2013 annual conference in New Orleans.

Co-hosted by FierceHealthIT and the College of Healthcare Information Management Executives, the panel will focus on using predictive analytics to improve care and efficiencies. (Pre-registration is required, so be sure to sign up today.)

In the meantime, we caught up with Lac Tran, Rush's CIO, (left) to get a sneak peek of how RUMC is using predictive analytics to improve care for patients at risk for stroke and cardiac arrest and to improve efficiency and throughput in the ED.

FierceHealthIT: How is Rush using predictive data analytics to improve patient care in the emergency room?

Tran: We did a case study for three years of people who were admitted to the emergency room and ended up admitted to the hospital because of cardiac arrest or a stroke. We used that data to detect patterns. When patients check into the emergency room with symptoms that could indicate that they have a minor stroke or heart attack, the emergency room docs pass that on directly to the stroke specialists and cardiology so that they can quickly treat the patient. With stroke and cardiac arrest, time is the most important thing. And a specialist is available 24/7.

FHIT: How does using this historic data compare to other forms of decision support or even doctors' own diagnostic experience?

Tran: Typically during an emergency the patient doesn't know where they are. In true emergencies they can't tell you their symptoms. But through the data that's in their electronic records and through the data from the study we can predict outcomes and risk based on their condition.

Yes, it seems like a decision support system, but it's a little bit more than that, because the predictive analytics has built the decision support system.

All the symptoms that indicate a heart attack or a stroke have been gathered before. It is in a data warehouse with information about patients who have had a stroke or cardiac arrest. Then you do the analytics on that and you form the predetermined patterns. And that pattern will build your EMR decision support. It's a continuing effort. With very solid predictive analytics you build a better decision support.   

FHIT: You've talked about the of benefits predictive analytics to quality of care. What's the business case?

Tran: There are a lot of benefits financially. Let's say that you look at the patterns of readmissions for the population. You can determine the common causes of the readmissions. You can really change that process and reduce readmissions.

FHIT: And how does it impact efficiency?

Tran: With predictive analytics you can zoom into the problem area and then fix that problem to improve throughput in the ED.

Here's the cause and effect: When the patient checks into the ED, it's typical that the nurse will start doing your vital signs. They move you to the exam room in the ED and you sit there and wait for the physician to come. And then the physician looks at you and reviews the vital signs and they probably order some tests, depending on the condition and diagnosis. Then you're lying there waiting for the results. Then the tests indicate that we need to admit you to be observed overnight or to be scheduled for surgery.

This is where the problem usually occurs. When they admit you, you sit there and sit there and you wait for the transport people to take you to admitting. If the communication between the three groups--the transport group, the nursing station and the emergency room--does not work out well, the patient as to sit there in the exam room for a long time.   

That's one scenario where you can use the analytics to detect where the bottleneck is, focus on that, and see the resolution to reduce the time. We can predict when those bottlenecks are likely to occur.

To complicate it a little more, let's say during the flu season the emergency room is really crowded. So if you have one of the patients in the exam room, the queue is not an ideal one. Because your exam room has to be empty before you can move another patient in. There's a lot of people sitting in the hallway. And that's not the safest thing to do. Because then you have a bunch of viruses flying around.

We can do the workflow analysis on that and we can see the number of patients and the number of docs on call and we can match them. And then we can connect them with the bed availability and the transport and schedule them to be admitted and if it's available move them right away. We reduce quite a bit of time.

They key thing is really the integration among these different systems. Typically, transport is on different resource management. And surgery is on different resource management. Available beds on the units is on a different module.

We integrate all of those together and we have, for lack of a better word, a workflow management system so we see where the bottlenecks are.

FHIT: What advice would you give your peers who are working on similar projects?

Tran: Definitely it's not going to be an IT product. You have to work very closely with the experts in the area of clinical quality, administration and finance. They need to co-own this product. And the researchers, too, for an academic institution such as ours. We have to work with the key stakeholders, such as in the ED workflow process--the emergency department is the key stakeholder on that, as well as the nursing unit.

So many times we've provided these fancy dashboards and people say "Oh, that looks nice," but nobody uses it.

FHIT: How exactly do you get buy-in from these different groups?

Usually these meetings with stakeholders are driven by an issue: "What if we reduce the time of a patient in the waiting room by 15 minutes?" Ask the doctors: "How will that help you?" You frame that benefit. And then turn it over to finance folks and ask, "If I reduce left without being seen and increase the throughput and avoid diversions and increase the number of visits by 10% or 10,000 patients a year admitted to the hospital, how will that impact the revenue?"

The best way to support people is to sympathize with what they have to go through operationally. If you speak the same language as them you get the collaboration very easily. We tend to not do that. The more you know about business practice, the better for you to communicate.

Learn more about how Rush University Medical Center and other healthcare organizations are using predictive analytics to improve outcomes and efficiency at the executive breakfast panel on Wed. March 6, during the HIMSS 2013 annual conference. The event is co-hosted by FierceHealthIT and the College of Healthcare Information Management Executives. Pre-registration is required, so be sure to sign up today.

Silva will be joined on the panel by Tina Buop, CIO at La Clinica de la Raza, Benjamin Horne, Ph.D., director of cardiovascular and genetic epidemiology at Intermountain Medical Center, Matt Siegel, vice president, strategy & corporate development, Verisk Health and Kaveh Safavi, M.D., managing director of Accenture Health Practice-North America.


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