Small hospital makes minor investment in analytics and reaps big rewards
A hospital does not need a staff of 50 and a $3 million budget to achieve real return on investment with healthcare analytics.
That’s according to Karen Reff, manager of decision support at Union General Hospital, a small facility in Georgia — and she speaks from experience.
In 2015, the 45-bed facility invested $50,000 and launched an analytics program that freed staff from time-consuming data-crunching activities. The analytics program enabled Union General clinicians to spend more time on delivering care, uncovering best practices that helped reduce readmissions, and pinpointing the best location for an outpatient clinic for COPD and congestive heart failure patients.
“It all started with our CEO finally crossing his pain threshold for what he could tolerate when it came to trying to manage and mine data in Excel, having everyone working from the same page,” Reff explained. “From there it was a very organic process. I read tons of literature, including Gartner’s magic quadrant reports, and other information online to see what other organizations were doing. I investigated quite a few vendors, narrowed it down to six, and asked for RFPs.”
It took several months of examining those RFPs and seeing demonstrations to find the vendor that met the organization’s needs and was in the hospital’s price range, Reff said.
Reff added that as a result of the analytics program, Union General has been able to substantially reduce readmissions.
“In the past, 30-day readmissions were reviewed and reported quarterly due to the time-intensive process required to gather the information needed for analysis,” Reff said. “Now the information is reviewed and considered on a monthly basis by the case management team to identify opportunities to reduce readmissions. Additionally, since readmission information is now available to case managers in real time, interventions can be implemented in a more timely manner.”
Further, hospital staff now have the ability to view data that previously was impossible to amass and review; the amount of time it would have taken in past to compile and wade through this same data to find anything relevant was overwhelming, she said.
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