Development of a Standardize Case Review Process Across a Health System

Background

Catheter-associated urinary tract infections (CAUTIs) and central line-associated bloodstream infections (CLABSIs) lead to increased length of stay, costs, morbidity and mortality. They are included in the Centers for Medicare and Medicaid Services Value Based Purchasing Program which can lead to financial penalties for hospitals. Each of the 5 hospitals within our health system performed a case review for CLABSIs and CAUTIs using varied methods. A standardized process and tools were developed.

Methods

Our Quality & Safety department facilitated a group comprised of the Infection Prevention (IP) Director, an Infection Preventionist from each hospitals and the Data Analyst. Each hospitals' case review process was mapped using a flowchart and a consensus standard operating procedure (SOP) was developed for system adoption. Multiple case review tools were reviewed and a consensus case review form was developed for system use. An electronic database aligning with the case review form was developed and a communication was sent to Nursing and Medical Director to advise them of this change.

Results

The consensus SOP, tools and communication were successful in implementing the revised process and helping our frontline clinical leaders understand their role in this process. The questions on the case review form assess compliance to our insertion and maintenance bundles along with the culturing algorithm. Being able to track and trend this data has provided guidance for focused improvement efforts both at the hospital and system levels.

Conclusions

Standardizing processes across a healthcare system is challenging as the operations and resources vary between institutions. This standardized process, tools and database allows our IP and PI team to track and trend the elements that may contribute to infections and to identify opportunities for improvement efforts. In the future, developing a dashboard for these metrics would increase accessibility to the data.