Identifying and Reducing Errors in Perioperative Anesthesia Medication Delivery

NIH RePORTER · AHRQ · R18 · $585,233 · view on reporter.nih.gov ↗

Abstract

SUMMARY In the USA every year there are an estimated 200,000 to 2 million anesthetisa medication injuries in the USA with an estimated annual health care cost of $3.1-$46 billion and more than US$300 billion worldwide. Though sensible recommendations have been proposed by professional societies, they generally address preparation and delivery errors (Failures of Execution), with little attention paid to the decision making involved in diagnosing and prescribing (Failures of Intention), and may not address the complexity of the working environment, the physical workspace, or safety culture (Performance Shaping Factors). We will use a multi-disciplinary team of anesthesiologists, human factors professionals, biomedical engineers, pharmacists, and certified registered nurse anesthesiologists to engineer reductions in anesthesia medication errors in operating rooms (ORs) that address all three sources of failure. We will use wire-frame models and rapid prototypes, to develop and evaluate off-the-shelf and novel technologies, process improvements, teamwork, task allocation and management, checklists, clinical decision support, and engineering strategies to enhance performance and to help avoid errors before they happen, trap them before they reach the patient, or mitigate the effects to avoid serious consequences. These will be tested individually and in combination, in simulated and real clinical settings, at two sites, with adult and pediatric populations, in procedures of low and high clinicaly complexity. The Naturalistic Decision Making paradigm, will fame our understanding of this complex multi-factorial, real-work decision- making. Distributed Cognition theory will be used to understand how different information sources integrated cognitively by the anesthesiologist. Task analysis techniques will be used predict procedural weaknesses and develop new processes, while heuristic usability analysis of technology will identify error-producing designs and evaluate new candidates for testing. This will be the most comprehensive study of anesthesia medication safety systems ever conducted, delivered by an highly experienced multi-disciplinary team of clinicans, scientists and engineers, working at the intersection of clinical practice and academic endeavor, using a combination of innovative techniques, approaches and perspectives, across simulated and real environments, to solve one of the most frequently, costly and under-researched threats to safety in acute care.

Key facts

NIH application ID
10224614
Project number
5R18HS026625-04
Recipient
MEDICAL UNIVERSITY OF SOUTH CAROLINA
Principal Investigator
James Abernathy
Activity code
R18
Funding institute
AHRQ
Fiscal year
2021
Award amount
$585,233
Award type
5
Project period
2018-09-30 → 2023-07-31