Integrated Hands-Free Electronic Patient Care Report (ePCR) Charting (IHeC): Designing the Architecture
Bibliographic record
Abstract
The nature of paramedic workloads typically results in incomplete or lack of patient care reports on patient handover to emergency department staff. Patient information gaps can increase emergency department staff's workload, cause care delays, and increase risks of adverse events. An integrated hands-free electronic patient care report (ePCR) could eliminate this gap. We conducted an environmental scan of the available literature on technologies to improve paramedic documentation and current advanced paramedic charting systems. Two technologies, speech recognition documentation and live telemetry sharing systems, were identified as potential improvements. A theoretical architecture for an integrated hands-free ePCR charting (IHeC) system was developed by combining these technologies. The ePCR could be completed and available upon patient arrival to the hospital using speech recognition and vital sign sharing technology. The IHeC system could solve the problem of patient information gaps and provide a platform for more advanced integration of paramedic services.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".