Reducing unnecessary outpatient group and screen testing at a regional cancer center
Bibliographic record
Abstract
BACKGROUND: Unnecessary group and screens (G&S) can lead to unnecessary antibody investigations, use of technologist time, and laboratory resources. LOCAL PROBLEM: A baseline audit at our institution identified that 25% of G&S from the cancer center were unnecessary. We aimed to reduce the ratio of monthly G&S to CBC samples processed from the cancer center by 10% (from 0.034 to 0.031) by January 2024. METHODS: This represents an interrupted time series design from November 2022 to January 2024. Using Plan Do Study Act (PDSA) cycles, we aimed to increase the use of an existing reflex testing system, termed "do not test." When this option is selected, the blood bank will only process the G&S sample if specific CBC criteria are met (e.g., hemoglobin <9.0 g/dL). Educational sessions increased awareness of this feature and sought feedback from end-users on its usability. With feedback, the design was updated to include a modifiable hemoglobin threshold for G&S testing, automatic re-selection of the "do not test" feature for future G&S orders, and aesthetic changes to make the feature more visible. RESULTS: The percentage of samples with "do not test" selected increased from 7.2% to 63.0% (p < .0001) and the ratio of G&S to CBC specimens improved from 0.034 to 0.028, exceeding the target of 0.031. We noted an improvement in the appropriateness of G&S orders from 75% at baseline (n = 20) to 97.5% (n = 80) post intervention (p = .003). CONCLUSIONS: We describe an effective strategy to improve G&S utilization at our institution's cancer center using a reflex testing system.
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".