Point‐of‐Care Ultrasound Work Flow Innovation: Impact on Documentation and Billing
Bibliographic record
Abstract
OBJECTIVES: To evaluate the impact that an innovative automated ultrasound (US) work flow, which allows for bedside performance of examination documentation and order placement, has on point-of-care US billing compared to ordering US examinations through an electronic medical record. METHODS: We conducted a retrospective review of point-of-care US billing data (March 2014-February 2016) for adult and pediatric emergency departments with an emergency medicine residency and a US fellowship. An innovative work flow with the ability to automate US billing and selectively transfer the images and reports for patient care examinations to an electronic medical record and picture archiving and communication system using the QPath US work flow solution (Telexy Healthcare, Maple Ridge, British Columbia, Canada) was implemented. The total number of examinations billed and percent increase in technical and professional revenue, excluding examinations performed by US fellows, before and after implementation of the automated work flow innovation were determined. RESULTS: After implementation of our automated US work flow process, the number of patient care US examinations billed increased significantly due to completing documentation and immediate billing determination at the bedside. The increase in percent billing relative to total examinations was noted in both technical (32% to 61%; P < .0001) and professional (37% to 65%; P < .0001) billing components. In addition, there was a net increase in technical and professional fee revenue to 96% and 78%, respectively. CONCLUSIONS: The implementation of an innovative automated work flow to include bedside point-of-care US documentation, order placement, and the automated transfer of images and reports led to a significant increase in US billing revenue, documentation, and compliance.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".