Current Use, Training, and Barriers to Point-of-Care Ultrasound Use in ICUs in the Department of Veterans Affairs
Bibliographic record
Abstract
Background Point-of-care ultrasound (POCUS) has become an integral part of critical care medicine for procedural guidance, bedside diagnostics, and assessing response to treatment. Multiple critical care societies recommend POCUS use, and POCUS training has been a requirement for critical care fellowship since 2012. Yet, current practice patterns of POCUS use in ICUs are not well known. Research Question This study aimed to characterize current POCUS use, training needs, and barriers to use among intensivists. Study Design and Methods A prospective observational study of all Veterans Affairs (VA) medical centers was conducted between June 2019 and March 2020 using a web-based survey of all chiefs of staff and ICU chiefs. These data were compared with those from a similar survey conducted in 2015. Results Chiefs of staff and ICU chiefs from 130 VA medical centers were surveyed with 100% and 94% response rates, respectively. At least one physician currently uses POCUS in 93% of ICUs, and 62% of individual physicians were estimated to be using POCUS. The most common POCUS applications were procedural guidance (59%), cardiac ultrasound (55%), and thoracic ultrasound (56%) . Most chiefs (80%) reported teaching POCUS to trainees in their ICU. The most frequently reported barriers to POCUS use were lack of trained providers (48%), lack of funding for training (45%), lack of training opportunities (37%), and lack of image archiving (34%). From 2015 through 2020, POCUS use increased across most applications and an increase in desire for training was seen. Interpretation POCUS use increased across VA ICUs between 2015 and 2020, but significant gaps remain. Without a deliberate investment in POCUS training and infrastructure for physicians in practice, institutions are unlikely to benefit fully from standardized POCUS use in ICUs.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".