In an increasingly data-driven healthcare world, healthcare delivery organizations grapple with data complexity and often find it challenging to merge different data sources to drive real-time decision-making. To tackle these challenges, Blairsville, Ga.-based Union General Hospital recently developed and implemented a business intelligence strategy with the goal of putting data into the hands of case managers and clinicians to improve efficiency and quality of care and establish best practices across many departments.
The Union General health system includes the 45-bed hospital Union General Hospital, located in rural north Georgia, as well as Chatuge Regional Hospital, a 25-bed critical access hospital, two nursing homes and several outlying medical clinics.
According to Karen Reff, manager of decision support at Union General, hospital leadership recognized the need to enhance the organization’s analytical and reporting system for healthcare operations and care management as well as finance operations.
“The electronic health record (EHR) system was woefully lacking in its reporting capability and the initiative to start looking for another solution really originated with our chief financial officer when he finally crossed the pain threshold. He was doing a lot of Excel spreadsheets and pivot tables, which was extremely limiting and time-consuming,” she says. “That’s how the initiative originated, but we’ve seen tremendous benefits in numerous areas.”
As part of its business intelligence strategy, Union General worked with Sisense, a business intelligence software vendor, to develop and implement a data analytics technology platform that enables clinicians and case managers to pull together and analyze disparate data sources. The platform also provides a browser-based HTML data visualization tool that enables Reff and her team to build dashboards for various clinical and business analytics. The first dashboard got off the ground in August and there are now 10 departments across the system using dashboards.
The use of analytics in the healthcare delivery setting has increased rapidly in the past few years specifically in areas such as disease management, case management and performance monitoring. Larger hospitals and health systems have invested significant resources into analytics initiatives, as they move forward, and move more deeply into, the challenging work of setting up the data warehouses and business intelligence capabilities needed to support analytics.
Smaller healthcare delivery organizations, however, are often challenged with limited IT resources when tackling analytics projects. The data analytics market seems to be quickly expanding to provide healthcare delivery organizations tools that are agile and easy to use without a major IT investment.
When it came to vetting the vendors, Reff says they chose a company that could meet all of Union General’s core requirements, and “the time to deployment and the effort required was very compelling as well,” she says.
“One of our core requirements when we were looking for a BI (business intelligence) solution is that we couldn’t be dependent on or further burden our IT department to support it,” Reff says. “And that’s one area where the Sisense business intelligence platform excelled was the fact that I, not being an IT professional, could manage and build these dashboards and generate reports that have saved immense amounts of time and given us insights that we didn’t have previously.”
Sisense provided an end-to-end technology solution, referred to as self-service data analytics technology, which does not require significant IT resources or the creation of data warehouses and allows users to “drag-and-drop” data to combine large data sets. And, Reff says she can build interactive dashboards and business intelligence reports and the web-based dashboards can be shared so multiple users can access, monitor and interact with the same report.
For one initiative, Union General’s case management team is using the business intelligence platform and data analytics to examine variables in 30-day patient readmissions with the aim of driving readmission reductions and improve patient outcomes.
“When looking at 30-day readmissions and how we could reduce 30-day readmissions, there are a lot of data elements that go into assessing the high-risk readmissions and it would take me days to pull that data out of the EHR system. And, we had to do it individually, so I knew who the patients were and I had to log in and manually extract several data elements. I wanted to know, for example, whether the patient went home with a home health agency and which home health agency did they go home with? Who was their doctor? What was their diagnosis? Did they have a primary care provider when they came in? And there was no one report for this data.”
Knight continues, “With this business intelligence software, I’ve been able to reduce the time it takes me to gather that data and actually spend time analyzing the patients’ charts for more clinical things that I could drill down on that impacts that next potential readmission. So, when a patient goes home with a home health agency, I am able to drill down and compare that home health agency A has a reduced readmission rate versus home health agency B and that helps us when considering contracts and who we might potentially use as a home health agency.”