Sevoflurane: Practice Change Integration Quality Improvement for Pharmacoeconomic Accountability
Bibliographic record
Abstract
Background: Pharmacoeconomic accountability is increasingly of concern to patients, institutions, and healthcare payors alike. Annual sevoflurane expenditure at a large university hospital exceeds $300,000 (personal communication, Eric Likar). Sevoflurane is a liquid, volatile anesthetic delivered from a vaporizer, through a breathing circuit to a patient. Multiple studies focused on reducing sevoflurane use note significant cost savings were attained with a variety of techniques. Moreover, using less sevoflurane reduces the environmental impact of volatile agents which are potent greenhouse gases. Through simple interventions, such as a brief staff education and reminders, Lethbridge et al. (2007) demonstrated over a fifty-percent reduction in sevoflurane use. At the beginning of a case and during the surgical or maintenance phase of anesthesia, vaporized sevoflurane induces and maintains unconsciousness in the patient. Objective: The project aims were to increase provider knowledge, attitude, and skills to decrease expenditures on sevoflurane in an anesthesia department at a large university hospital. Methods: An educational presentation, highlighting the potential benefits of using reduced fresh gas flows to deliver sevoflurane, was given to all anesthesia providers at a large university hospital. Lewins’ Change Theory provided the scientific underpinning for the educational intervention about sevoflurane providing information to motivate anesthesia providers to change their practice. The educational program was followed by academic detailing throughout the study period to reinforce the goal of lowering sevoflurane average fresh gas flows. Data collection focused on provider knowledge and attitude using a pre/post education survey. Before and after survey responses were analyzed using a paired t-test. Review of a convenience sample of 300 consecutive same-day-surgery pediatric cases, that used sevoflurane for induction and maintenance of anesthesia, separated into three groups of 100 cases each. These sets-baseline and 1 month and 3 months-were compared for average fresh gas flow, procedure length, and minutes to airway and analyzed using ANOVA and Tukey’s test. Results: Provider knowledge and attitude measured using a Likert scale increased across all providers. Average fresh gas flow use decreased with each consecutive group and reached statistical significance at month 3, with a decrease in average fresh gas flows of 15%. Discussion: Excess sevoflurane is carried into a waste gas system where it is exhausted into the environment. Fresh gases used to deliver sevoflurane are frequently higher than necessary and increase the production of sevoflurane as a waste anesthesia gas. The opportunity for reducing sevoflurane use and institutional expenditure on sevoflurane exists while increasing sustainability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".