Background:
Current approaches to addressing central venous catheter (CVC) identification and discontinuation in the general medicine population remain largely ineffective. A recently published observational study reported that 26% of teaching physicians and 31% of hospitalists were not able to accurately identify the presence of a CVC in a patient under their care. Since preventable line-related complications such as central line-associated bloodstream infections (CLABSI) increase over time, an automated approach to accurate detection and timely removal of CVCs would have a significant impact on patient care.
Purpose:
Our goal was to design and implement a user friendly function within our existing electronic medical record that would automate daily central line identification and provide physician decision support in the removal of any CVC without an institutionally approved indication for use.
Description:
Through an institutional initiative focused on implementing several effective approaches to CLABSI reduction, a multidisciplinary group was organized to create an automated central line identification tool for daily physician use. The tool is linked to robust nursing assessment data and alerts providers to all active CVCs from a central patient list. Once the alert icon is selected, the user is directed to a decision support tool that requests documentation of necessity, based on a list of institutionally approved CVC indications. If no active indication exists, the physician is prompted to remove the CVC. This process is completed for every active CVC on a daily basis – the tool resets every midnight. From July 1 to October 31, 2014, we identified a total of 17741 patient census days and 3020 central line days, resulting in a CVC prevalence of 17.02% in the general medicine population. We collected sample data on 200 unique CVCs comprising a total 857 central line days. Lines selected for investigation were triple lumen catheters, PICCs, hemodialysis catheters and subcutaneous chest ports. Internal controls confirm that an automated physician alert was generated for all identified central line days. Based on system logic, a CVC indication was identified 674 times, resulting in an overall physician compliance rate of 78.65%.
Conclusions:
We describe the novel development of an automated daily central line identification tool with an interactive decision support function aimed at timely CVC removal. Our tool can be easily integrated in the daily physician workflow with a high compliance rate. Future efforts are aimed at demonstrating an absolute reduction in central line days within a general medicine population.