Computerized surveillance of opioid-related adverse drug events in perioperative care: a cross-sectional study.
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.


CONCLUSIONS
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.
Eckstrand
Julie A
JA
Duke Health Technology Solutions, Duke University Health System, Durham, North Carolina, USA.
Habib
Ashraf S
AS
Department of Anesthesiology, Duke University School of Medicine, Durham, North Carolina, USA.
Williamson
Abbie
A
Department of Pharmacy, Duke University Health System, Durham, North Carolina, USA.
Horvath
Monica M
MM
Duke Health Technology Solutions, Duke University Health System, Durham, North Carolina, USA.
Gattis
Katherine G
KG
Department of Anesthesiology, Duke University School of Medicine, Durham, North Carolina, USA.
Cozart
Heidi
H
Duke Health Technology Solutions, Duke University Health System, Durham, North Carolina, USA.
Ferranti
Jeffrey
J
Duke Health Technology Solutions, Duke University Health System, Durham, North Carolina, USA.|Division of Neonatology, Department of Pediatrics, Duke University School of Medicine, Durham, North Carolina, USA.