Reconciliation of health records following penicillin allergy testing of hospitalized patients
Bibliographic record
Abstract
Medication errors are common and can lead to substantial morbidity. Similarly, inaccurate medication allergy lists can result in increased costs to the system, unnecessary allergy testing, prescription of inappropriate antibiotics, and allergic reactions. We hypothesized that most inpatients tested for penicillin allergy were not allergic and this information was not documented in the EMR or communicated to general practitioners. We retrospectively reviewed charts of all inpatients seen at a teaching hospital by a consultant allergist in 2012. Data collected included basic demographics, penicillin allergy test results, current allergy status in the EMR, readmission rates, prescribed antibiotics, and discharge summary contents. 146 patients were tested for penicillin allergy and 144 (98.6%) were not allergic. Although orders were written in 145 (99.3%) charts to update the allergy status after testing, 32 (22.23%) patients with negative tests were still listed as allergic to penicillin in the EMR. Only 19 (15.2%) discharge summaries notified family physicians of the allergy testing results and discharge summaries were missing for 25 (20%) patients. Further assessment of half the charts revealed that in 41% of cases the negative allergy test resulted in a change of antibiotic to penicillin or its derivative. Of the 60 readmitted patients, 20 (33%) were still listed as allergic to penicillin in the EMR (only one patient tested positive) and 14 (70%) of the 20 patients required antibiotics. 12 of these 14 patients (86%) were prescribed antibiotics in the penicillin family despite their positive allergy status. A significant proportion of health records were not amended following antibiotic allergy testing and the new allergy status was not communicated to most general practitioners in the discharge summary. A more efficient and reliable system needs to be implemented to ensure allergy status changes are communicated to all members of the healthcare team.
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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 | 0.019 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".