Mercy Builds a Healthy Data Framework

Improving treatment quality while reducing costs is at the center of healthcare. Yet achieving outstanding results can be difficult because so many interrelated factors come into play.

At Mercy, the fifth largest Catholic healthcare system in the United States—with 43 acute care and specialty hospitals and more than 700 physician practices and outpatient facilities in Arkansas, Kansas, Missouri and Oklahoma—all roads lead to data. “We wanted to gain deep visibility into processes and procedures so we could make better informed decisions,” says Jamie Oswald, manager of data analytics and engineering.

In the past, using a collection of ad hoc tools, health practitioners and administrators couldn’t get fast, clear answers to a number of essential questions.

For instance, “If an operating room director wanted to know what type of knee or hip implant we were using, it would go to our decision support team or the finance department,” Oswald explains. “They would provide some of the needed data, but it was nearly impossible to get the full story. In some cases, by the time people could get answers, the data was obsolete.”

About four years ago, the organization began using SAP BusinessObjects Explorer as a self-service tool. This allowed practitioners and administrators to “poke around and find answers to questions,” he says. But that alone did not solve the data problem.

“There is so much data out there,” Oswald adds, “and we were limited to 25 columns and about a million rows. In our perioperative space, that amounted to only about three weeks of data. We couldn’t drill down to individual supply level data and other valuable information.”

Big Data Analytics Solution Processes Data In Real Time

In mid-2014, Mercy moved to the SAP HANA platform and introduced a big data analytics solution that processes data in real time. Among other things, the solution helps manage costs in the operating room, refine scheduling and procedures to improve care quality, and deliver evidence-based personalized medicine through structured data. The system connects to electronic health records (EHRs) and an array of other systems to take data interaction to a higher level.

“We can view and model data to a high level of granularity,” Oswald explains. For example, it’s now possible to view a breakdown of the supplies used for a patient and compare the data to different locations, different days of the week and even different surgical teams.

This delivers deep visibility into cost per case, supply cost trends, waste patterns and more. “We are able to understand issues and trends and make far more informed decisions,” he reports.

Dr. Todd Stewart, vice president for clinical integrated solutions at Mercy, says that the analytics capabilities are driving improvements in both the quality of care and the costs. “We are able to better understand factors that would have been nearly impossible to understand in the past,” he says.

In addition to using HANA for perioperative analysis, Mercy is expanding the system for use with pharmacy data. That will include predictive analytics that helps ensure that supplies are maintained at optimal levels.

The data analytics system has helped Mercy achieve impressive results. Most important, it reduced the mortality rate for patients suffering from heart failure to about half of the national average, while cutting costs for those patients by $800 per case. The data has also resulted in about $9.42 million in perioperative supply cost savings.

“Data and analytics have allowed us to do some pretty amazing things,” Stewart reports. “From here, the possibilities are wide open.”


Samuel Greengard
Samuel Greengard
Samuel Greengard writes about business, technology and other topics. His book, The Internet of Things (MIT Press) was released in the spring of 2015.

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