Cardiothoracic operating room blood gas workflow performance improvement initiative
Bibliographic record
Abstract
OBJECTIVES: To evaluate the current workflow of blood gas ordering and testing in a cardiothoracic operating room to identify opportunities to streamline the process, using performance improvement methodologies. METHODS: Issues with specimen relabeling were identified that lead to delayed results and potential patient safety concerns. Blood gas specimen relabeling was evaluated for operating room cases from August 2018 to December 2022. An OpTime Epic Sidebar button for arterial blood gas and venous blood gas orders was created in January 2019 to streamline the ordering process so that laboratory barcode labels were then printed in the operating room and attached to the specimen, eliminating the need for relabeling by the technologists. RESULTS: This Epic Sidebar intervention led to a drastic improvement of appropriate labeling, which has been sustained. From March 2019 to January 2023, with our new workflow, over 95% of blood gas specimens arrived barcode labeled compared to less than 1% in the preintervention era. CONCLUSIONS: A multidisciplinary team with key stakeholders is important to address complex care issues. Performance improvement methodology is critical to develop interventions that hardwire the process. This intervention led to a sustained reduction in secondary relabeling of patient samples and improved timeliness of reporting of blood gas results.
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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 | 0.035 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".