Developing and Evaluating a Remote Quality Assurance System for Point-of-Care Ultrasound for an Internal Medicine Residency Global Health Track
Bibliographic record
Abstract
Background: A quality assurance system is vital when using point-of-care ultrasound (POCUS) to ensure safe and effective ultrasound use. There are many barriers to implementing a quality assurance system including need for costly software, faculty time, and extra work to log images. Methods: With minimal funding or protected faculty time, we successfully developed an effective remote quality assurance system between residents rotating internationally and faculty in the US. Results: 270 total exams were logged using this system (41 per resident over a 7 week period). Over the course of the implementation period, a significant increase was seen in average image quality (p = 0.030) and percent agreement with reviewer (p = 0.021). No significant increase was seen for percent images with quality rating 5/5 (p = 0.068) or for studies per resident per week (p = 0.30). Discussion/Conclusions: A quality assurance system for remote review and feedback of POCUS exams was successfully developed with limited available funding, using consumer-level software and an educational collaboration. Residents used the system regularly and demonstrated improvement in reviewer-rated image acquisition and interpretation skills. A similar system can be applied for physicians in any geographic area looking to learn POCUS, in partnership with local or international POCUS mentors. We detail a step-by-step approach, challenges encountered, and lessons learned, to help guide others seeking to implement similar 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 | 0.073 | 0.082 |
| 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 | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".