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Automating Ambulatory Central Line Data Capture and Calculations
Elizabeth M Martinez1, Christopher A Grimes2, Belinda A Bordeaux3
1Children's Hospital of The King's Daughters, Department of Clinical Practice and Education, Virginia, United States, Norfolk.
Insights
Automating central line (CL) data collection via electronic health records (EHR) significantly improved accuracy for calculating ambulatory CL-associated bloodstream infection (A-CLABSI) rates. This enhances patient safety monitoring and quality improvement initiatives.
Area of Science:
- Healthcare Informatics
- Patient Safety
- Infectious Disease Epidemiology
Background:
- Ambulatory central line-associated bloodstream infections (A-CLABSIs) are a significant patient safety concern.
- Accurate calculation of A-CLABSI rates requires precise ambulatory central line (CL) day data, which is difficult to obtain.
- Existing manual methods for tracking CL data are often inaccurate and inefficient.
Purpose of the Study:
- To develop and implement an automated system for collecting CL data from electronic health records (EHR).
- To accurately calculate ambulatory CL days within a 5% variance of manual methods.
- To improve the monitoring and reduction of A-CLABSIs.
Main Methods:
- Analyzed existing documentation processes to identify key data fields for CL information.
- Developed and refined EHR fields to ensure accurate CL data capture.
- Created an algorithm to electronically track CL insertions/removals and calculate CL days.
- Conducted extensive manual chart reviews to validate automated data and correct errors.
- Implemented real-time error detection and correction for data sustainability.
Main Results:
- The automated EHR process achieved an average variance of 0.3% for patient identification and 3.2% for total CL days compared to manual methods.
- Specific variance rates for ambulatory CL day calculations were 4.5%.
- The average error rate in documentation (EID) for all CLs was 12.8% before real-time correction.
Conclusions:
- Real-time electronic data capture provides a more efficient and sustainable method for maintaining accurate CL data.
- This automated approach facilitates more reliable calculation of A-CLABSI rates.
- The improved data accuracy supports enhanced patient safety and quality improvement efforts.
Background:
Ambulatory central line-associated bloodstream infections (A-CLABSIs) cause significant morbidity, are costly, and are a relatively new patient safety target. To calculate A-CLABSI rates, the total ambulatory line-day denominator must be known; however, few hospitals can measure this accurately. Access to this data is vital to the quality improvement process.
Objectives:
The project aimed to automate the collection of central line (CL) information from the electronic health record (EHR) and the calculation of ambulatory CL days to within a 5% variance rate compared with the manual process to monitor and improve A-CLABSI rates.
Methods:
Existing documentation processes were analyzed, and appropriate fields were determined for CL data capture. Multiple revisions were needed to hard-wire accurate EHR documentation in identified areas. An algorithm was developed to identify and electronically track CL insertions/removals and calculate days. We completed 9,533 manual chart reviews, correcting retrospective data errors in documentation (EID), and implemented a process to address EID in real time to ensure accurate data and sustainability.
Results:
The EHR automated process identified an adjusted average of 98% of patients with a CL compared with the manual process with a variance of 0.3% between June 2024 and May 2025. The inpatient and ambulatory variance rates were 3.50 and 5.2%, respectively. The automated process identified an adjusted average of 96% total CL days compared with the manual process, with an average variance rate of 3.2% . Inpatient and ambulatory CL day calculations had variance rates of 0.7 and 4.5% respectively, compared with the manual process. The average EID rate for all CLs was 12.8%.
Conclusion:
Through real-time electronic data capture, a more efficient and sustainable process for maintaining accurate CL data and calculating A-CLABSI rates was developed.
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