Combining store-and-forward pictures and videoconferencing for outpatient burn follow-up care
Bibliographic record
Abstract
• BurnPics is a patient-centered program for follow-up burn care in a large regional catchment area. • Follow-up care by videoconferencing saves significant times for patients and clinic staff. • Direct telehealth care to the patient’s home replaces the traditional hub-and-spoke care model. Regional burn centers face challenges in follow-up care for large catchment areas. In 2006, we implemented a store-and-forward outpatient protocol (“BurnPics”) for established burn patients in our region to assess healing and range of motion. Starting August 2014, we added videoconferencing visits for select patients. The purpose of this quality improvement project was to evaluate BurnPics and videoconferencing utilization. We reviewed records of patients enrolled in the BurnPics program from August 2014 through July 2015. We evaluated videoconferencing enrollment, provider time, and revenue generation. Data was analyzed using descriptive statistics. There were 398 BurnPics patients. Most patients resided in-state (82%). Median distance from residence to burn center was 67 miles (IQR 29–228). Fifty-three patients underwent 85 videoconferencing visits; 30 visits (35%) were billable, 27 visits (32%) were post-operative follow-ups, and 28 visits (33%) were not billable due to out-of-state residence. Median time per video-visit was 46 min (IQR 35–60); median burn care provider time was 11 min (IQR 7–14). The BurnPics program has potential to mitigate challenges of follow-up burn care. Videoconferencing has improved our follow-up care and created minimal time burden on providers. Future legislative efforts will help minimize financial and legal barriers to successful regional telehealth programs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".