Automated adverse event detection collaborative: electronic adverse event identification, classification, and corrective actions across academic pediatric institutions.

BACKGROUND: Historically, the gold standard for detecting medical errors has been the voluntary incident reporting system. Voluntary reporting rates significantly underestimate the number of actual adverse events in any given organization. The electronic health record (EHR) contains clinical and administrative data that may indicate the occurrence of an adverse event and can be used to detect adverse events that may otherwise remain unrecognized. Automated adverse event detection has been shown to be efficient and cost effective in the hospital setting. The Automated Adverse Event Detection Collaborative (AAEDC) is a group of academic pediatric organizations working to identify optimal electronic methods of adverse event detection. The Collaborative seeks to aggregate and analyze data around adverse events as well as identify and share specific intervention strategies to reduce the rate of such events, ultimately to deliver higher quality and safer care. The objective of this study is to describe the process of automated adverse event detection, report early results from the Collaborative, identify commonalities and notable differences between 2 organizations, and suggest future directions for the Collaborative. METHODS: In this retrospective observational study, the implementation and use of an automated adverse event detection system was compared between 2 academic children's hospital participants in the AAEDC, Children's National Medical Center, and Cincinnati Children's Hospital Medical Center. Both organizations use the EHR to identify potential adverse events as designated by specific electronic data triggers. After gathering the electronic data, a clinical investigator at each hospital manually examined the patient record to determine whether an adverse event had occurred, whether the event was preventable, and the level of harm involved. RESULTS: The Automated Adverse Event Detection Collaborative data from the 2 organizations between July 2006 and October 2010 were analyzed. Adverse event triggers associated with opioid and benzodiazepine toxicity and intravenous infiltration had the greatest positive predictive value (range, 47%- 96%). Triggers associated with hypoglycemia, coagulation disturbances, and renal dysfunction also had good positive predictive values (range, 22%-74%). In combination, the 2 organizations detected 3,264 adverse events, and 1,870 (57.3%) of these were preventable. Of these 3,264 events, clinicians submitted only 492 voluntary incident reports (15.1%). CONCLUSIONS: This work demonstrates the value of EHR-derived data aggregation and analysis in the detection and understanding of adverse events. Comparison and selection of optimal electronic trigger methods and recognition of adverse event trends within and between organizations are beneficial. Automated detection of adverse events likely contributes to the discovery of opportunities, expeditious implementation of process redesign, and quality improvement.

Recognizing hypoglycemia in children through automated adverse-event detection.

BACKGROUND: Automated adverse-event detection using triggers derived from the electronic health record (EHR) is an effective method of identifying adverse events, including hypoglycemia. However, the true occurrence of adverse events related to hypoglycemia in pediatric inpatients and the harm that results remain largely unknown. OBJECTIVE: We describe the use of an automated adverse-event detection system to detect and categorize hypoglycemia-related adverse events in pediatric inpatients. METHODS: A retrospective observational study of all hypoglycemia triggers generated by an EHR-driven surveillance system was conducted at a large urban children's hospital during a 1-year period. All hypoglycemia triggers were investigated to determine if they represented a true adverse event and if that event followed or deviated from the local standard of care. Clinical and demographic variables were analyzed to identify subpopulations at risk for hypoglycemia. RESULTS: Of the 1254 hypoglycemia triggers produced, 198 were adverse events (positive predictive value: 15.8%). No hypoglycemic adverse events were identified via the hospital's voluntary incident-reporting system. The majority of hypoglycemia-related adverse events occurred in the NICU (n = 123 of 198 [62.1%]). A total of 154 (77.8%) of the 198 adverse events hospital-wide and 102 (83%) of the 123 adverse events in the NICU occurred in patients who were receiving insulin therapy. CONCLUSIONS: Hypoglycemia is common in hospitalized children, particularly neonates and those who receive insulin. An EHR-driven automated adverse-event detection system was effective in identifying hypoglycemia in this population. Automated adverse-event detection holds great promise in augmenting the safety program of organizations who have adopted the EHR.