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At Susquehanna Health, a Data-Driven Approach to Meeting Healthcare’s “Triple Aim”

April 20, 2015
by John Morrissey
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Susquehanna Health had its hands full with unrelenting problems around poor throughput in surgery, from start to finish. Wait times were measured in hours before and after surgical cases. Patient satisfaction average scores were stuck at around 80 percent.

A year or so later, after deep analysis of available data, the turnaround was compelling at the Williamsport, Pa-based system of four hospitals. The system’s largest hospital, Williamsport Regional Medical Center, where most surgery took place, lowered wait pre-op waiting times from 200 minutes in July 2013 down to 90 minutes in July 2014. As it did, patient satisfaction scores rose inversely to hit 91 percent.

Data analytics were responsible for laying out all the reasons for the throughput problem and pointing the way to solve them, said Lori Beucler, vice president and chief nursing officer of Susquehanna Health. For starters, the hospital had built more operating rooms than it had the capacity to use, but not enough recovery rooms, “which created this gridlock which was unbelievable.”

Executives from Susquehanna Health explained their data-driven approach to meeting elements of healthcare’s “triple aim”--improving population health and patient experience while reducing cost-- at the HIMSS convention in Chicago.

Analysis showed a dire lack of enough room for both pre-op and post-op traffic, said Shailesh Patel, M.D., medical director of the anesthesia department. Besides the pre-op waits of up to four hours, holding time in the post-op area was running about 300 minutes. The lack of post-op beds forced patients to stay and recover in operating rooms, further gridlocking the throughput of surgical cases. Other inefficiencies, and sources of patient dissatisfaction, included a routine in which patients were moved four times prior to surgery, with insufficient transporter staff to handle it.

As part of an overhaul of the surgical management process putting the patient at the center, the hospital added 12 pre-op and post-op beds in an adjacent intensive care unit with available space, which helped lead to the big drop in waiting time, said Patel. With the findings of data analysis, “it was a no-brainer” to revamp the space, “but you have to be able to prove your business case to the C-suite,” Beucler said.

More and more, finding and reducing waste is a response to declines in hospital revenue, she added. “When you start to look at your monthly data, the statistical analytics [inform] how you’re performing at each hospital in respect to volume in the operating room, how long is it taking you to do those sets of cases, and are you spending too much time and too much money. Can you do the same amount in less time for less money?”

Another such analysis highlighted a situation in which a 25-bed critical-access hospital scheduled most surgery cases between 7 a.m. and 3:30 p.m. Monday through Friday but surgical staff had to be on call around the clock, “costing us a lot of money with no return on our investment,” Beucler said. The health system diverted the sparse number of cases from mid-afternoon on to Williamsport Regional, which had the capacity to take them, while saving on critical access staff.

“You have to understand where you’re getting your return on investment and where you’re not,” she said, “using your data to drill down to find out how efficient are you, what’s your productivity like, what are your outcomes.”