Improving safety and communication for healthcare providers caring for SARS-COV-2 patients
Bibliographic record
Abstract
BACKGROUND: Decreasing healthcare provider (HCP) exposure to the severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) virus in emergency departments (EDs) is crucial. Approaches include limiting the HCP presence and ensuring sealed isolation rooms, which can result in communication difficulties. This quality improvement (QI) initiative aimed to decrease by 50% duration of isolation room door opening and increasing HCP-perceived communication clarity by one point on a five-point Likert scale. METHODS: This was a prospective, multi-stage project with three Plan-Do-Study-Act (PDSA) cycles between May and July 2020: (1) an educational intervention, (2) the introduction of a novel transceiver communication device, and (3) utilizing a clinical champion. Statistical Process Control XbarR charts were used to assess for special cause variation, and two-tailed Mann-Whitney U tests were used for statistical significance between Likert survey means. Qualitative responses underwent thematic analysis. RESULTS: Observation of 174 patient encounters was completed over 33 days, with 95 meeting the inclusion criteria. Door opening decreased from baseline (n=40; mean 72.97%) to PDSA 3 (n=21; mean 1.58%; p<0.0001). HCP-perceived communication clarity improved from baseline (n=36; mean 3.36) to PDSA-3 (n=49; mean 4.21; p<0.001). Survey themes included positive effects on communication and workflow, with some challenges on the integration of the new device into the clinical workflow. HCP-perceived errors, workarounds, and workflow pauses showed significant improvements. CONCLUSION: This QI initiative with a novel transceiver showed significant decreases in isolation room door opening and increases in communication clarity. Future work will expand to operating rooms and intensive care units.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".