Economic Impact of Standardized Orders for Antimicrobial Prophylaxis Program
Bibliographic record
Abstract
OBJECTIVE: To assess the effect and economic impact of an intervention aimed at standardizing the timing of preoperative antimicrobial prophylaxis from the perspective of a major teaching hospital. DESIGN: A pre/post study design in which a random sample of 60 procedures from a 12-month period in the preintervention phase were reviewed. A comparative sample of 60 procedures during a seven-month postintervention phase was selected. For each prophylactic course, preoperative dose administration details were classified as early (>2 h prior to incision), on time (0-2 h prior), delayed (0-3 h after), or late (>3 after). To determine the economic impact of this intervention, we used a predictive decision analytic model using institutional costs and the published probabilities of inpatient surgical wound infections (SWIs) following administration of antimicrobials timed according to the above criteria. Two conditions were analyzed: (1) an interdisciplinary two-stage therapeutic interchange program involving staff education and modification of preoperative antimicrobial orders to ensure timely administration and (2) no intervention. SETTING: An 1100-bed tertiary care, university-affiliated institution. PATIENTS: 120 randomly selected procedures involving inpatients who received a preoperative antibiotic. OUTCOME MEASURES: Differences in preoperative antimicrobial timing and cost avoidance associated with the intervention. RESULTS: In the preintervention phase, 68% of prophylactic courses were on time, 22% were early, and the balance were delayed or late. The incidence of on-time prophylaxis increased to 97% during the postintervention phase (p = 0.001). Operating room staff involvement in antimicrobial administration increased from 57% to 92% (p = 0.001). Based on a setup and annual intervention cost of $9100 CAN ($1 CAN = $0.68 US), an annual inpatient SWI avoidance of 51 cases, an average infection-associated extended hospital stay of four days, and an average treatment cost of $1957 CAN per inpatient SWI, we estimated that 153 hospital days were avoided and there was an annual cost avoidance of $90 707 CAN ($1779 CAN saved per inpatient infection avoided) due to this intervention. Using sensitivity analyses, no plausible changes in the base case estimates altered the results of the economic model. CONCLUSIONS: An interdisciplinary approach to optimizing the timing of preoperative antimicrobial doses can impact positively on practice patterns and result in substantial cost avoidance. Costs incurred to implement such an intervention are small when compared with the annual cost avoidance to the institution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.000 |
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 teacher head, 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".