Barriers to penicillin allergy de-labeling in the inpatient and outpatient settings: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Penicillin allergy is the most commonly reported drug allergy in the US. Despite evidence demonstrating that up to 90% of labels are incorrect, scalable interventions are not well established. As part of a larger mixed methods investigation, we conducted a qualitative study to describe the barriers to implementing a risk-based penicillin de-labeling protocol within a single site Veteran's hospital. METHODS: We conducted individual and group interviews with multidisciplinary inpatient and outpatient healthcare teams. The interview guides were developed using the Theoretical Domains Framework (TDF) to explore workflows and contextual factors influencing identification and evaluation of patients with penicillin allergy. Three researchers iteratively developed the codebook based on TDF domains and coded the data using thematic analysis. RESULTS: We interviewed 20 clinicians. Participants included three hospitalists, five inpatient pharmacists, one infectious disease physician, two anti-microbial stewardship pharmacists, four primary care providers, two outpatient pharmacists, two resident physicians, and a nurse case manager for the allergy service. The factors that contributed to barriers to penicillin allergy evaluation and de-labeling were classified under six TDF domains; knowledge, skills, beliefs about capabilities, beliefs about consequences, professional role and identity, and environmental context and resources. Participants from all groups acknowledged the importance of penicillin de-labeling. However, they lacked confidence in their skills to perform the necessary evaluations, such as test dose challenges. The fear of inducing an allergic reaction and adding further complexity to patient care exacerbated their reluctance to de-label patients. The lack of ownership of de-labeling initiative was another significant obstacle in establishing consistent clinical workflows. Additionally, heavy workloads, competing priorities, and ease of access to alternative antibiotics prevented the prioritization of tasks related to de-labeling. Space limitations and nursing staff shortages added to challenges in outpatient settings. CONCLUSION: Our findings demonstrated that barriers to penicillin allergy de-labeling fall under multiple behavioral domains. Better role clarification, opportunities to develop necessary skills, and dedicated resources are needed to overcome these barriers. Future interventions will need to employ a systemic approach that addresses each of the behavioral domains influencing penicillin allergy de-labeling with stakeholder engagement of the inpatient and outpatient health care teams.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".