Measuring Adverse Events and Levels of Harm in Pediatric Inpatients With the Global Trigger Tool

Division of Hospital Medicine.
PEDIATRICS (Impact Factor: 5.47). 10/2012; 130(5). DOI: 10.1542/peds.2012-0179
Source: PubMed


To evaluate and characterize the Global Trigger Tool's (GTT's) utility in a pediatric population; to measure the rate of harm at our institution and compare it with previously established trigger tools and benchmark rates; and to describe the distribution of harm of the detected events.

Per the GTT methodology, 240 random inpatient charts were retrospectively reviewed over a 12-month pilot period for the presence of 53 predefined safety triggers. When triggers were detected, the reviewers investigated the chart more thoroughly to decide whether an adverse event occurred. Agreement with a physician reviewer was then reached, and a level of harm was assigned.

A total of 404 triggers were detected (1.7 triggers per patient), and 88 adverse events were identified. Rates of 36.7 adverse events per 100 admissions and 76.3 adverse events per 1000 patient-days were calculated. Sixty-two patients (25.8%) had at least 1 adverse event during their hospitalization, and 18 (7.5%) had >1 event identified. Three-quarters of the events were category E (temporary harm). Two events required intervention to sustain life (category H). Two of the 6 trigger modules identified 95% of the adverse events.

The GTT demonstrated utility in the pediatric inpatient setting. With the use of the trigger tool, we identified a rate of harm 2 to 3 times higher than previously published pediatric rates. Modifications to the trigger tool to address pediatric-specific issues could increase the test characteristics of the tool.

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    ABSTRACT: Although electronic health records (EHRs) have the potential to provide a foundation for quality and safety algorithms, few studies have measured their impact on automated adverse event (AE) and medical error (ME) detection within the neonatal intensive care unit (NICU) environment. This paper presents two phenotyping AE and ME detection algorithms (ie, IV infiltrations, narcotic medication oversedation and dosing errors) and describes manual annotation of airway management and medication/fluid AEs from NICU EHRs. From 753 NICU patient EHRs from 2011, we developed two automatic AE/ME detection algorithms, and manually annotated 11 classes of AEs in 3263 clinical notes. Performance of the automatic AE/ME detection algorithms was compared to trigger tool and voluntary incident reporting results. AEs in clinical notes were double annotated and consensus achieved under neonatologist supervision. Sensitivity, positive predictive value (PPV), and specificity are reported. Twelve severe IV infiltrates were detected. The algorithm identified one more infiltrate than the trigger tool and eight more than incident reporting. One narcotic oversedation was detected demonstrating 100% agreement with the trigger tool. Additionally, 17 narcotic medication MEs were detected, an increase of 16 cases over voluntary incident reporting. Automated AE/ME detection algorithms provide higher sensitivity and PPV than currently used trigger tools or voluntary incident-reporting systems, including identification of potential dosing and frequency errors that current methods are unequipped to detect.
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