Data Analytics Helped Mercy Slash $4.3M in Nursing Costs
Combining data from the hospital's existing SaaS scheduling application with predictive analytics is helping schedulers identify and stop nurse leakage.
At Saint Louis-based health system Mercy, like most other hospitals and health systems, nursing labor is one of the top expenses.
According to Curtis Dudley, vice president of Mercy's integrated performance solutions, that number tops $800 million annually.
Executives realized that gaining control of the cost was imperative. It had full-time equivalent (FTE) leakage among nurses across its 630 units in 44 hospitals that reached 80,000 hours per year and forced an overreliance on agency nurses to fill the gaps.
The solution was a tool that combines data from the hospital's existing software as a service (SaaS) scheduling application with predictive analytics to help schedulers identify leakage and fill those gaps with their own employees.
This tool has saved $4.3 million since it was deployed in September 2016, Mercy reports.
Mike Gillen, vice president of Mercy's system of operations, says using technology made sense here because of the great variation in scheduling practices across the organization, not to mention the "sheer magnitude" of the work involved with existing templates and paper tools.
Mercy knew it needed to not only improve its scheduling processes, but to do it in a uniform way.
It also knew that it needed to start where the nurses already were.