Automating Ambulatory Central Line Data Capture and Calculations.

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.