Minding the Gap(s): Hospitalists Experience Aspirational, Safety, and Knowledge Deficits That Prevent Them From Practicing POCUS
Bibliographic record
Abstract
Point-of-care ultrasound (POCUS) has been a mainstay of clinical decision-making in the intensive care unit and emergency department for more than a decade, but adoption into hospital medicine has lagged behind. Recently, internal medicine residency programs have started to develop POCUS curricula for trainees, though concurrent hospitalist training programs have been limited to date, with little consensus on what hospitalist-oriented curricula should entail. As such, there is wide variability amongst hospitalists with respect to utilization of, training in, and proficiency in POCUS. We conducted a two-part survey of internal medicine hospitalists at our institution: (1) needs assessment that focused on prior training, attitudes and perspectives, current practices, desired use, and barriers to clinical integration; and (2) knowledge test of exam indications, image interpretation, medical decision-making, and understanding of limitations. Our results demonstrate that a majority of hospitalists felt that POCUS was important for diagnostic purposes and that they would benefit from POCUS-specific education. Inadequate training was the most cited barrier to POCUS use. Hospitalist knowledge was lacking in all domains, particularly image interpretation and clinical integration. As a result, we created a three-tiered training program meant to engage: (1) All hospitalists in basic knowledge and appropriate use of POCUS, (2) Some hospitalists in hands-on skill acquisition and image interpretation, and (3) Few hospitalists in mastery of POCUS with resultant formal credentialing. A tiered approach to POCUS training for hospitalists ensures a fundamental cognitive understanding of POCUS for all, but also facilitates hands-on training for those who are committed to further skillset development.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".