The 2013 Healthcare Informatics Innovator Awards: First Place Winner: NorthShore University HealthSystem

The team created five algorithms that would go into NorthShore’s enterprise-wide database to identify patients at risk for undiagnosed hypertension. One was the traditional criterion of three elevated blood pressures on separate occasions. Others included the mean blood pressures of the three most recent encounters; and a single blood pressure of greater than 180/100. All of the algorithms were fairly close in predicting hypertension, Rakotz says. “We found that using all five algorithms at the same time helped us identify most of the patients, with the least amount falling through the cracks,” he says.

The algorithms identified about 1,600 patients who were actively being seen in the health system and who satisfied at least one of the algorithms. The next step was to reach out to these at-risk patients and invite them for an in-office blood pressure reading. This posed another challenge: knowing the limitations of traditional in-office blood pressure readings, how could they trust the results?

With that in mind, Rakotz suggested using an ambulatory blood pressure monitor known as the Automated Office Blood Pressure (AOBP) machine. The AOBP machine takes six blood pressure readings in six minutes with only the patient in the room. It throws out the first reading and averages the next five. The machine is extremely reliable and its results correlate well with 24-hour ambulatory blood pressure monitoring, the industry gold standard, Rakotz says.

With the decision to rely on the AOBP machine, the team was ready to reach out to the patients who satisfied the algorithms to see if they were truly hypertensive. Through the Practice-Based Research Network, the team was able to recruit a large number of NorthShore primary care physicians who were willing to participate and reach out to their patients.

Of the 1,600 patients who were flagged by an algorithm, 475 came in for the AOBP test. Of those, 38 percent were verified as truly hypertensive (about 80 percent were found to have either pre-hypertension or hypertension). Rakotz says the results, verified by the AOBP machine, uncovered a large number of patients who had falsely elevated blood pressures from traditional in-office blood pressure readings. “One of the benefits of using this more accurate system was we were no longer identifying patients with hypertension who didn’t have it,” he says.

A Two-Pronged Approach

Encouraged by the results, the team considered how to turn the information into an intervention that could be used in real time on a wider basis. In discussions with the participating primary care physicians, some expressed a concern that they would have to call three patients into the office for every one patient who would end up having true hypertension. The upshot was an agreement with the primary care physicians that 50 percent would be an acceptable threshold.

The informatics team went to work optimizing the algorithms to meet that goal, by raising the manual blood pressure limit that would result in a higher predictive value of hypertension. Yet by making the algorithm more stringent, some patients would be missed at the lower end. “Our chief quality officer felt that it was important not to miss any from a population health standpoint,” Rakotz says.

This led to a two-pronged approach to a surveillance system, using two sets of algorithms. One is to notify physicians of patients in their panel who have been identified as high-risk of hypertension using the more stringent set of algorithms. The names of those patients are included a monthly “opportunity for improvement” report, recommending that the physician call in the patient for an AOBP blood pressure reading. The other is a “best practice advisory” for patients who come in for scheduled visits for any reason. These patients are flagged with the original (non-optimized) set of five algorithms, incorporated into the electronic medical record, which fires off a real-time alert that a patient is at-risk and should undergo an AOBP reading.

Rupesh Mandala; Meredith Sefa; Nilesh Patil; Raman Jathar; Joseph Alan Simmons; Mekhala Patwardhan Photo by Jon Hillenbrand, NorthShore University HealthSystem

Following the successful pilot, Kenneth Anderson, D.O., NorthShore’s chief medical quality officer, took the idea to the hospital administration for approval. The surveillance system is now up and running in all of NorthShore’s primary care offices, which are equipped with 30 AOBP machines.

Since going live with the system in January 2011, the system has been used to identify, test and diagnose more than 500 patients with previously undiagnosed hypertension, according to Rakotz. In addition, it allows NorthShore to identify newly hypertensive patients as soon as they meet the criteria, essentially shutting off the flow of new undiagnosed hypertensive patients entering the system. He also notes that 94 percent of patients who have been diagnosed using this methodology have been started on anti-hypertensive medication by their primary care physicians within 90 days of diagnosis.

The project has been a factor in NorthShore’s achievement of HIMSS stage 6 certification last year, he notes, adding that the project succeeded as a collaboration of clinical, informatics, quality, and administration areas, all with a single focus to provide the best care for the patient.

Next Steps and Broader Implications