Resource utilization and cost assessment of a proactive penicillin allergy de-labeling program for low-risk inpatients
Bibliographic record
Abstract
Abstract Background Resource utilization and costs can impede proactive assessment and de-labeling of penicillin allergy among inpatients. Methods Our pilot intervention was a proactive penicillin allergy de-labeling program for new inpatients with penicillin allergy. Patients deemed appropriate for a challenge with a low-risk penicillin allergy history were administered 250 mg amoxicillin and monitored for 1 h. We performed an explorative economic evaluation using various healthcare professional wages. Results Over two separate 2-week periods between April 2021 and March 2022, we screened 126 new inpatients with a penicillin allergy. After exclusions, 55 were appropriate for formal assessment. 19 completed the oral challenge, and 12 were directly de-labeled, resulting in a number needed to screen of 4 and a number needed to assess of 1.8 to effectively de-label one patient. The assessor’s median time in the hospital per day de-labeling was 4h08 with a range of (0h05, 6h45). A single-site annual implementation would result in 715 penicillin allergy assessments with 403 patients de-labeled assuming 20,234 annual weekday admissions and an 8.9% penicillin allergy rate. Depending on the assessor used, the annual cost of administration would be between $21,476 ($53.29 per effectively de-labeled patient) for a pharmacy technician and $61,121 ($151.67 per effectively de-labeled patient) for a Nurse Practitioner or Physician Assistant. Conclusion A proactive approach, including a direct oral challenge for low-risk in-patients with penicillin allergy, appears safe and feasible. Similar programs could be implemented at other institutions across Canada to increase access to allergy assessment.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".