Computerized surveillance of opioid-related adverse drug events in perioperative care: a cross-sectional study.
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BACKGROUND: Given the complexity of surgical care, perioperative patients are at high risk of opioid-related adverse drug events. Existing methods of detection, such as trigger tools and manual chart review, are time-intensive which makes sustainability challenging. Using strategic rule design, computerized surveillance may be an efficient, pharmacist-driven model for event detection that leverages existing staff resources. METHODS: Computerized adverse drug event surveillance uses a logic-based rules engine to identify potential adverse drug events or evolving unsafe clinical conditions. We extended an inpatient rule (administration of naloxone) to detect opioid-related oversedation and respiratory depression to perioperative care at a large academic medical center. Our primary endpoint was the adverse drug event rate. For all patients with a naloxone alert, manual chart review was performed by a perioperative clinical pharmacist to assess patient harm. In patients with confirmed oversedation, other patient safety event databases were queried to determine if they could detect duplicate, prior, or subsequent opioid-related events. RESULTS: We identified 419 cases of perioperative naloxone administration. Of these, 101 were given postoperatively and 69 were confirmed as adverse drug events after chart review yielding a rate of 1.89 adverse drug events/1000 surgical encounters across both the inpatient and ambulatory settings. Our ability to detect inpatient opioid adverse drug events increased 22.7% by expanding surveillance into perioperative care. Analysis of historical surveillance data as well as a voluntary reporting database revealed that 11 of our perioperative patients had prior or subsequent harmful oversedation. Nine of these cases received intraoperative naloxone, and 2 had received naloxone in the post-anesthesia care unit. Pharmacist effort was approximately 3 hours per week to evaluate naloxone alerts and confirm adverse drug events. CONCLUSION: A small investment of resources into a pharmacist-driven surveillance model gave great gains in organizational adverse drug event detection. The patients who experienced multiple events are particularly relevant to future studies seeking risk factors for opioid induced respiratory depression. Computerized surveillance is an efficient, impactful, and sustainable model for ongoing capture and analysis of these rare, but potentially serious events.
Published Version (Please cite this version)10.1186/1754-9493-3-18
Publication InfoEckstrand, Julie A; Habib, Ashraf S; Williamson, Abbie; Horvath, Monica M; Gattis, Katherine G; Cozart, Heidi; & Ferranti, Jeffrey (2009). Computerized surveillance of opioid-related adverse drug events in perioperative care: a cross-sectional study. Patient Saf Surg, 3(1). pp. 18. 10.1186/1754-9493-3-18. Retrieved from https://hdl.handle.net/10161/15924.
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Professor of Pediatrics
Data Warehousing and Health Analytics - Healthcare Business Intelligence Methodologies - Healthcare Data Warehousing - Health Analytics - Healthcare Information Exchange and Interoperability Standards - Computerized Physician Order Entry Implementation and Analysis for Quality Improvement - Web Services and Distributed Computing in Healthcare Pediatric Informatics
Professor of Anesthesiology
Postoperative nausea and vomiting. Acute postoperative pain Optimizing hemodynamic support during cesarean section performed under neuraxial anesthesia Prevention of nausea and vomiting during and after cesarean section performed under neuraxial anesthesia Predictors of severe acute pain and persistent pain following breast surgeryRisk factors for postoperative respiratory depression
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