Resource utilization and cost assessment of a proactive penicillin allergy de-labeling program for low-risk inpatients
Bibliographic record
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.000 | 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".