Abstract T P277: Time Out: Safe Patient Transition Pre-Hospital to Hospital
Bibliographic record
Abstract
Background and Purpose: Historically, Emergency Medical Services (EMS) and Emergency Department (ED) nursing staff have had no standardized method of hand-off reporting, and documentation of the communication has been difficult to locate in the medical record. Comprehensive Stroke Centers (CSC) are called to collaborate and communicate with all care delivery partners, including providers from transferring facilities. A standardized method of communication and documentation sets the stage for safe patient care transitions. Methods: Regional community discussion began in 2011. Proposals were made and no early adopters came forward. Internal discussion was readdressed. A team was assembled consisting of researchers and frontline staff from ED, EMS and the CSC. A hand-off communication tool was developed using Situation, Background, Assessment, and Treatments format. The tool includes stroke specific data points important to care and pilot data will be collected during fourth quarter of 2013. Metrics include compliance of tool use and an accurate medical history with home medications. Three EMS providers representing urban, suburban and rural services have been recruited. Each Paramedic-RN team involved in a hand-off will receive an electronic survey measuring engagement and satisfaction. Results: A standardized method for communicating important hand-off information was developed and will be demonstrated. Final analysis of the data at the end of the quarter will provide direction to further improve the tool with the goal of permanent implementation within the region. Ongoing data analysis will be communicated to all providers monthly via electronic communication. Sharing the story with the region on a regular basis presents a road-map for successful safe patient transitions. Conclusions: The development of consistent standardized hand-off communication between pre-hospital and hospital staff is essential to patient safety. Collaboration empowers best practice solutions. Accurate medical history assists in rapid emergent evaluation. Engaged pre-hospital and hospital personnel using standardized tools are likely to provide consistent safe patient transitions. Safe hand-off communication is paramount for all CSC partners.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.070 | 0.024 |
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".