Predictors of Prehospital On-Scene Time in an Australian Emergency Retrieval Service
Bibliographic record
Abstract
INTRODUCTION: Prehospital physicians balance the need to stabilize patients prior to transport, minimizing the delay to transport patients to the appropriate level of care. Literature has focused on which interventions should be performed in the prehospital environment, with airway management, specifically prehospital intubation (PHI), being a commonly discussed topic. However, few studies have sought additional factors which influence scene time or quantify the impact of mission characteristics or therapeutic interventions on scene time.Hypothesis/Problem:The goal of this study was to identify specific interventions, patient demographics, or mission characteristics that increase scene time and quantify their impact on scene time. METHODS: A retrospective, database model-building study was performed using the prehospital mission database of South Australian Ambulance Service (SAAS; Adelaide, South Australia) MedSTAR retrieval service from January 1, 2015 through August 31, 2016. Mission variables, including patient age, weight, gender, retrieval platform, physician type, PHI, arterial line placement, central line placement, and finger thoracostomy, were assessed for predictors of scene time. RESULTS: A total of 506 missions were included in this study. Average prehospital scene time was 34 (SD = 21) minutes. Four mission variables significantly increased scene time: patient age, rotary wing transport, PHI, and arterial line placement increased scene time by 0.09 (SD = 0.08) minutes, 13.6 (SD = 3.2) minutes, 11.6 (SD = 3.8) minutes, and 34.4 (SD = 8.4) minutes, respectively. CONCLUSION: This study identifies two mission characteristics, patient age and rotary wing transport, and two interventions, PHI and arterial line placement, which significantly increase scene time. Elderly patients are medically complex and more severely injured than younger patients, thus, may require more time to stabilize on-scene. Inherent in rotary wing operations is the time to prepare for the flight, which is shorter during ground transport. The time required to safely execute a PHI is similar to that in the literature and has remained constant over the past two years; arterial line placement took longer than envisioned. The SAAS MedSTAR has changed its clinical practice guidelines for prehospital interventions based on this study's results. Retrieval services should similarly assess the necessity and efficiency of interventions to optimize scene time, knowing that the time required to safely execute an intervention may reach a minimum duration. Defining the scene time enables mission planning, team training, and audit review with the aim of improved patient care.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".