Seeing Ghosts: A Quality Improvement Intervention to Decrease Phantom Scanning Through Increased Image Archiving of POCUS by Internal Medicine Residents
Bibliographic record
Abstract
Point of care ultrasound (POCUS) is used in internal medicine (IM) to augment clinical decision making and improve procedural safety. Institutionally-supported archiving software can help learners track scan numbers and receive feedback on image acquisition and interpretation. At the University of Saskatchewan, IM residents use POCUS for procedures and assessments but rarely save images, limiting feedback opportunities. Our quality improvement project aimed to increase the number of POCUS images saved by Postgraduate Year One (PGY-1) IM residents, targeting over 75% of non-procedural scans and ensuring over 50% of residents save at least one scan. This quality improvement project was conducted on a clinical teaching unit at an academic hospital over two years. We used four Plan-Do-Study-Act (PDSA) cycles each year to measure the percentage of non-procedural scans saved by PGY-1 IM residents. As a balance measure, we compared the number of scans performed historically and during the study period to monitor for changes in usage. Data was collected using an ultrasound sign-out sheet. At baseline, no diagnostic scans were saved by PGY-1 IM residents. Post-intervention, 56% of scans were archived in cohort one and 76% in cohort two. Additionally, 79% of residents in cohort one and 94% in cohort two archived at least one scan. The balance measure improved from 1.13 in the first year to 2.25 in the second, suggesting image archiving is not a deterrent to performing scans. Through this intervention, we significantly increased the archiving of non-procedural scans by PGY-1 IM residents. We advocate for implementing a formal POCUS archiving system to promote quality assurance in residency programs.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".