OBJECTIVES: Currently there are few practical methods to identify and measure harm to hospitalized children. Patients in NICUs are at high risk and warrant a detailed assessment of harm to guide patient safety efforts. The purpose of this work was to develop a NICU-focused tool for adverse event detection and to describe the incidence of adverse events in NICUs identified by this tool. METHODS: A NICU-focused trigger tool for adverse event detection was developed and tested. Fifty patients from each site with a minimum 2-day NICU stay were randomly selected. All adverse events identified using the trigger tool were evaluated for severity, preventability, ability to mitigate, ability to identify the event earlier, and presence of associated occurrence report. Each trigger, and the entire tool, was evaluated for positive predictive value. Study chart reviewers, in aggregate, identified 88.0% of all potential triggers and 92.4% of all potential adverse events. RESULTS: Review of 749 randomly selected charts from 15 NICUs revealed 2218 triggers or 2.96 per patient, and 554 unique adverse events or 0.74 per patient. The positive predictive value of the trigger tool was 0.38. Adverse event rates were higher for patients <28 weeks' gestation and <1500 g birth weight. Fifty-six percent of all adverse events were deemed preventable; 16% could have been identified earlier, and 6% could have been mitigated more effectively. Only 8% of adverse events were identified in existing hospital-based occurrence reports. The most common adverse events identified were nosocomial infections, catheter infiltrates, and abnormal cranial imaging. CONCLUSIONS: Adverse event rates in the NICU setting are substantially higher than previously described. Many adverse events resulted in permanent harm and the majority were classified as preventable. Only 8% were identified using traditional voluntary reporting methods. Our NICU-focused trigger tool appears efficient and effective at identifying adverse events.
OBJECTIVES: Currently there are few practical methods to identify and measure harm to hospitalized children. Patients in NICUs are at high risk and warrant a detailed assessment of harm to guide patient safety efforts. The purpose of this work was to develop a NICU-focused tool for adverse event detection and to describe the incidence of adverse events in NICUs identified by this tool. METHODS: A NICU-focused trigger tool for adverse event detection was developed and tested. Fifty patients from each site with a minimum 2-day NICU stay were randomly selected. All adverse events identified using the trigger tool were evaluated for severity, preventability, ability to mitigate, ability to identify the event earlier, and presence of associated occurrence report. Each trigger, and the entire tool, was evaluated for positive predictive value. Study chart reviewers, in aggregate, identified 88.0% of all potential triggers and 92.4% of all potential adverse events. RESULTS: Review of 749 randomly selected charts from 15 NICUs revealed 2218 triggers or 2.96 per patient, and 554 unique adverse events or 0.74 per patient. The positive predictive value of the trigger tool was 0.38. Adverse event rates were higher for patients <28 weeks' gestation and <1500 g birth weight. Fifty-six percent of all adverse events were deemed preventable; 16% could have been identified earlier, and 6% could have been mitigated more effectively. Only 8% of adverse events were identified in existing hospital-based occurrence reports. The most common adverse events identified were nosocomial infections, catheter infiltrates, and abnormal cranial imaging. CONCLUSIONS: Adverse event rates in the NICU setting are substantially higher than previously described. Many adverse events resulted in permanent harm and the majority were classified as preventable. Only 8% were identified using traditional voluntary reporting methods. Our NICU-focused trigger tool appears efficient and effective at identifying adverse events.
Authors: Paul J Sharek; Gareth Parry; Donald Goldmann; Kate Bones; Andrew Hackbarth; Roger Resar; Frances A Griffin; Dale Rhoda; Cathy Murphy; Christopher P Landrigan Journal: Health Serv Res Date: 2010-08-16 Impact factor: 3.402
Authors: L Dupree Hatch; Peter H Grubb; Amanda S Lea; William F Walsh; Melinda H Markham; Patrick O Maynord; Gina M Whitney; Ann R Stark; E Wesley Ely Journal: Pediatrics Date: 2016-09-21 Impact factor: 7.124
Authors: Anne G Matlow; G Ross Baker; Virginia Flintoft; Douglas Cochrane; Maitreya Coffey; Eyal Cohen; Catherine M G Cronin; Rita Damignani; Robert Dubé; Roger Galbraith; Dawn Hartfield; Leigh Anne Newhook; Cheri Nijssen-Jordan Journal: CMAJ Date: 2012-07-30 Impact factor: 8.262