Healthcare organizations know there’s value in data and value in analyzing it. But many times, they don’t know how to practically implement analytics in their organizations to make real change.
Here’s proof: while two-thirds of organizations have an executive dashboard to support strategic decision-making, only one-third of those that have a dashboard use it on a daily basis to make decisions. That means that in aggregate, less than 1 in 4 healthcare organizations leverages its data at an executive level daily.
So how do you move from theory to practice when it comes to analytics? It can be a journey, so it’s important to take it one step at a time. Here are 3 projects that delivered healthcare analytics ROI that you can emulate at your organization.
Decreasing readmissions among newborns
No one wants to go back to the hospital after being discharged, least of all new parents with their baby. Yet data published in Pediatrics shows that at one large healthcare system, 1.8% of newborns were readmitted within 28 days of discharge. Of these babies, 41% had feeding problems, 35% had jaundice, and 33% had respiratory distress.
Munson Healthcare, based in Northern Michigan, wanted to decrease the number of newborn readmits in its Women & Children’s department. It turned to analytics to help provide new insight that would help it address the problem.
With analytics, Munson Healthcare was better able to see which issues were bringing babies back to the hospital after discharge, such as hyperbilirubinemia (jaundice). They then worked to understand the root causes, such as were mothers learning how to feed their babies correctly? Were some babies being discharged too early? Once staff members investigated the reasons, they could then take steps to make process changes.
As a result of this effort, the number of readmissions started to go down. The decrease in the hyperbilirubinemia readmits was part of the overall reduction of readmits in the department by 24% in two years.
Decreasing antimicrobial usage in the hospital
We’ve all heard about the dangers of over-prescribing antibiotics, especially as it relates to the development of “super” strains of bacteria. This can be especially harmful in hospitals, as patients already have compromised immune systems. In addition, overprescribing antimicrobials can prove costly to health systems.
EvergreenHealth, based in Kirkland, Wash., about 15 miles outside of Seattle, is required to report its antimicrobial numbers to the Centers for Disease Control (CDC) and the Washington State Hospital Association (WSHA). To do so, EvergreenHealth is bringing clinical data from its Cerner EHR into Diver Platform on antimicrobial usage within the hospital. With Diver, EvergreenHealth can easily meet its monthly reporting requirements to the CDC and WSHA.
In addition, the health system has an Antimicrobial Stewardship Committee that reviews the data to see where the health system can stop certain kinds of treatment or convert patients from an IV antibiotic to an oral version.
With the Diver dashboard, EvergreenHealth’s administration can drill down by physician to see which providers are prescribing which medications. If necessary, administrators can then intervene to educate the physician on alternate approaches – for example, prescribing a less powerful antimicrobial.
Since EvergreenHealth started this dashboard, it decreased the average number of treatment days for its antimicrobials from 92 days per 1,000 patient days to 39 days per 1,000 patient days, a 58% decrease.
Featured resource: 10 Healthcare Analytics Projects with Real Results – Read it
Saving the lives of cardiac patients
According to the World Health Organization (WHO), 17.9 million people across the globe die each year from cardiovascular disease. Heart attacks (myocardial infarctions) and strokes are responsible for 85% of those deaths.
Hospitals are always looking for ways to reduce mortality rates among cardiac patients. In addition to improving ways of treatment, what if data could help?
Huangshi Central Hospital, based in Hubei Province in Central China, found that it could use data to improve the time it takes for a cardiac patient to be seen, and greatly reduce the mortality rate as a result.
There are several key time metrics that Huangshi Central Hospital tracks when a cardiac patient is brought into the facility. Among those is the overall door-to-balloon time (D2B), which measures the time from when a patient enters the emergency department to the time a blocked artery is opened in the cath lab. Huangshi Central Hospital targeted a D2B time of less than 90 minutes.
With Diver, the hospital developed a time-tracking system enabled by RFID scanning to monitor the D2B process. It tracked seven steps along the way and aimed to improve and more tightly control each step. The Diver dashboard provided the insight that enabled hospital staff to identify ways to shorten the D2B process.
With Diver’s help, Huangshi Central Hospital reduced its D2B time from 95.63 minutes to 57.19 minutes, a 40.2% improvement. As a result, the mortality rate decreased from 10.9% to 3.55%, a 67.4% reduction.
Conclusion
It is possible to drive significant, real results from healthcare analytics. Oftentimes, healthcare organizations just need some projects to model to spark their own ideas.
If you’d like to see some more healthcare analytics projects to emulate, download our white paper, “10 Healthcare Analytics Projects with Real Results.”
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