Related Experiment Video
Updated: Jul 6, 2026
Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
Validity and Reliability of Administrative Coded Data for the Identification of Hospital-Acquired Infections: An
Olga Redondo-González1, José María Tenías2, Ángel Arias1,3
1Research Support Unit, Hospital General La Mancha Centro, Ciudad Real, Spain.
Insights
Administrative coded data (ACD) shows high specificity for hospital-acquired infections (HAIs) but moderate sensitivity for many types. Algorithmic coding may improve accuracy for specific infections like prosthetic surgical site infections.
Area of Science:
- Healthcare Informatics
- Infectious Disease Epidemiology
- Health Services Research
Background:
- Administrative coded data (ACD) is increasingly used for healthcare surveillance.
- Assessing the accuracy of ACD for identifying hospital-acquired infections (HAIs) is crucial for patient safety and quality improvement.
- Previous studies have shown variable performance of ACD in detecting HAIs.
Purpose of the Study:
- To update the assessment of the validity and reliability of administrative coded data (ACD) in identifying hospital-acquired infections (HAIs).
- To compare the performance of ACD against manual chart review for various HAIs.
- To explore methods for improving ACD accuracy.
Main Methods:
- Systematic literature search for studies comparing ACD with manual chart review for HAIs.
- Meta-analysis of data for specific HAIs including surgical site infections (SSIs), Clostridium difficile infections (CDIs), ventilator-associated pneumonias/events (VAPs/VAEs), catheter-associated urinary tract infections (CAUTIs), and central venous catheter-related bloodstream infections (CLABSIs).
- Random-effects meta-regression model construction to analyze performance and heterogeneity.
Main Results:
- ACD demonstrated high specificity (≥93%) for HAI incidence and high sensitivity (95%) for prosthetic SSIs.
- Moderate sensitivity was observed for CDI (65%) and nonprosthetic SSIs (65%).
- ACD showed substantial agreement for CDI (κ=0.70) and strong diagnostic odds ratios for CDI (DOR=772.07) and SSIs (DOR=78.20). Performance varied by ICD coding system, with potential lower discriminative ability for ICD-10.
- Algorithmic coding improved SSI sensitivity by up to 22%.
Conclusions:
- Administrative coded data (ACD) may not be sufficiently accurate for most HAIs.
- Subgrouping and algorithmic coding show promise for enhancing ACD validity, particularly for prosthetic SSIs.
- Further investigation is needed into the performance of different ICD coding systems, especially ICD-10.
Objective:
To conduct an updated assessment of the validity and reliability of administrative coded data (ACD) in identifying hospital-acquired infections (HAIs).
Methods:
We systematically searched three libraries for studies on ACD detecting HAIs compared to manual chart review. Meta-analyses were conducted for prosthetic and nonprosthetic surgical site infections (SSIs), Clostridium difficile infections (CDIs), ventilator-associated pneumonias/events (VAPs/VAEs) and non-VAPs/VAEs, catheter-associated urinary tract infections (CAUTIs), and central venous catheter-related bloodstream infections (CLABSIs). A random-effects meta-regression model was constructed.
Results:
Of 1,906 references found, we retrieved 38 documents, of which 33 provided meta-analyzable data (N = 567,826 patients). ACD identified HAI incidence with high specificity (≥93 percent), prosthetic SSIs with high sensitivity (95 percent), and both CDIs and nonprosthetic SSIs with moderate sensitivity (65 percent). ACD exhibited substantial agreement with traditional surveillance methods for CDI (κ = 0.70) and provided strong diagnostic odds ratios (DORs) for the identification of CDIs (DOR = 772.07) and SSIs (DOR = 78.20). ACD performance in identifying nosocomial pneumonia depended on the ICD coding system (DORICD-10/ICD-9-CM = 0.05; p = .036). Algorithmic coding improved ACD's sensitivity for SSIs up to 22 percent. Overall, high heterogeneity was observed, without significant publication bias.
Conclusions:
Administrative coded data may not be sufficiently accurate or reliable for the majority of HAIs. Still, subgrouping and algorithmic coding as tools for improving ACD validity deserve further investigation, specifically for prosthetic SSIs. Analyzing a potential lower discriminative ability of ICD-10 coding system is also a pending issue.
Related Concept Videos
01:26Healthcare Associated Infections I: Iatrogenic, Exogenic and Endogenic
HAIs significantly increase the cost of health care. Extended stays in healthcare institutions, increased disability, increased costs of medications, including specialized antibiotics, and prolonged recovery times add to the patient's expenses and the healthcare institution and funding bodies. Common...
01:22Healthcare Associated Infections II: Preventive Measures
The best practices for preventing healthcare-associated infections include hand hygiene, patient risk...
01:03Data Validation
Nursing assessment guides are generally based on holistic models rather than medical...
01:10Documentation of Nursing Diagnosis
In some settings, data-driven computerized decision support systems are in place, allowing for more accurate nursing diagnoses. The database within one of these systems includes diagnostic labels defining characteristics, activities, and indicators for nursing. A nurse enters assessment...
01:11Purpose of Health Records I
Here's a breakdown of how health records serve these purposes:
01:27Nursing Clinical Information System
A Nursing Clinical Information System (NCIS) is a specialized type of healthcare information system tailored to meet the unique needs of nursing practice. It incorporates the principles of nursing informatics to streamline information management and improve the quality of care delivery.
Critical attributes of NCIS include: