Governance Considerations for Point-of-Care Ultrasound: a HIMSS-SIIM Enterprise Imaging Community Whitepaper in Collaboration with AIUM
Bibliographic record
Abstract
Point-of-care ultrasound (POCUS) has emerged as a standard of care across a variety of healthcare settings due to its ability to provide critical clinical information and as well as procedural guidance to clinicians directly at the bedside. Implementation of enterprise imaging (EI) strategies is needed such that POCUS images can be appropriately captured, indexed, managed, stored, distributed, viewed, and analyzed. Because of its unique workflow and educational requirements, reliance on traditional order-based workflow solutions may be insufficient. To improve patient care outcomes and operational efficiency, a robust governance committee for POCUS within healthcare systems that addresses pertinent institutional policies to ensure effective and sustainable implementation of enterprise imaging, appropriate to the specific clinical encounter-based workflow needs of POCUS, is critical. This white paper explores several key governance considerations in the formulation and structure of a POCUS enterprise imaging strategy, focusing on program governance, clinical governance, technology governance, information governance, and financial governance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".