Implementation of a quality improvement initiative to reduce daily chest radiographs in the intensive care unit
Bibliographic record
Abstract
OBJECTIVE: To reduce the number of routine chest radiographs (CXRs) done in a tertiary care intensive care unit (ICU). METHODS: Using a quality improvement approach, we measured the number of CXRs done per patient-day before (15 June 2010-15 June 2011) and after (15 June 2011-15 June 2012) a multipronged intervention in a 15-bed medical-surgical ICU in a 350-bed tertiary care teaching hospital. We studied a total of 1492 patients who were admitted to this ICU-738 patients during the preintervention period and 754 patients during the postintervention period. Interventions were education for the ICU house staff, developing indications for routine CXRs on the computer order-entry system, and visual posters/signage to remind ICU staff that there were no indications for routine, daily CXRs. The primary outcome was the number of CXRs per patient-day, but we also measured CTs of the chest, mechanical ventilator days, length of ICU stay and ICU and hospital mortality. RESULTS: There were 0.73 CXRs per patient-day done during the preintervention period and 0.54 CXRs per patient-day done during the postintervention period, a 26% reduction. There were no differences between the periods in age, sex or severity of illness (Acute Physiology and Chronic Health Evaluation (APACHE) II score) of the patients, number of chest CTs, mechanical ventilator days, length of ICU stay and ICU or hospital mortality. CONCLUSIONS: A quality improvement that includes education, reminders of appropriate indications and computerised decision support can decrease the number of routine CXRs in an ICU.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".