This analysis points to the need for systemwide implementation of human factors engineering-based approaches to work toward further eliminating anesthesia-related adverse events. Such actions include standardization of processes, forcing functions, separating storage of look-alike sound-alike medications, limiting stock of high-risk medication strengths, bar coding medications, use of cognitive aids such as checklists, and high-fidelity simulation.

Objectives:The Veterans Health Administration (VHA) lessons learned process for Anesthesia adverse events was developed to alert the field to the occurrences and prevention of actual adverse events. This article details this quality improvement project and perceived impact.Methods: As part of ongoing quality improvement, root cause analysis related to anesthesiology care are routinely reported to the VHA National Center for Patient Safety. Since May 2012, the National Anesthesia Service subject matter experts, in collaboration with National Center for Patient Safety, review actual adverse events in anesthesiology and detailed lessons learned are developed. A survey of anesthesiology chiefs to determine perceived usefulness and accessibility of the project was conducted in April 2018. Results:The distributed survey yielded a response rate of 69% (84/122).Most of those who have seen the lessons learned (85%, 71/84) found them valuable. Ninety percent of those aware of the lessons learned (64/71) shared them with staff and 75% (53/71) reported a changed or reinforced patient safety behavior in their facility. The lessons learned provided 72% (51/71) of chiefs with new knowledge about patient safety and 75% (53/71) gained new knowledge for preventing adverse events.Conclusions: This nationwide VHA anesthesiology lessons learned project illustrates the tenets of a learning organization. implementing team and systems-based safeguards to mitigate risk of harm from inevitable human error. Sharing lessons learned provides opportunities for clinician peerto-peer learning, communication, and proactive approaches to prevent future similar errors.