Identification of device-associated infections utilizing administrative data.

Area of Science:

• Healthcare quality improvement
• Infection control and prevention
• Health services research

Background:

• Healthcare-associated infections (HAIs) are a major cause of patient morbidity and mortality in U.S. hospitals.
• Changes in healthcare policy now link HAI surveillance data to public reporting and administrative data to Medicare reimbursement.

Purpose of the Study:

• To compare the accuracy of hospital administrative data with infection surveillance for identifying HAIs.
• To evaluate the sensitivity and specificity of administrative data in detecting specific HAIs.

Main Methods:

• Compared infection surveillance data (gold standard) with hospital administrative data for catheter-associated urinary tract infections (CAUTI), central line-associated bloodstream infections (CLABSI), and ventilator-associated pneumonia.
• Calculated sensitivity, specificity, and positive predictive values for administrative data codes.

Main Results:

• Administrative data showed very low sensitivity for detecting CAUTI (0%), CLABSI (21%), and ventilator-associated pneumonia (25%).
• Adding more diagnosis codes slightly improved sensitivity but minimally impacted specificity.
• Positive predictive values for Centers for Medicare and Medicaid Services (CMS)-defined conditions were 0% for CAUTI and 41% for CLABSI.

Conclusions:

• Hospital administrative data significantly under-identified HAIs compared to surveillance methods in this study.
• While hospitals are incentivized to improve care quality, continuous evaluation of the appropriateness of quality measures is essential.
• Findings highlight a discrepancy between data used for public reporting/reimbursement and actual infection detection.