Improving ambulance dispatch triage to trauma: A scoping review using the framework of development and evaluation of clinical prediction rules

Use of a large language model (LLM) for ambulance dispatch and triage

INTRODUCTION

Large language models (LLMs) have grown in popularity in recent months and have demonstrated advanced clinical reasoning ability. Given the need to prioritize the sickest patients requesting emergency medical services (EMS), we attempted to identify if an LLM could accurately triage ambulance requests using real-world data from a major metropolitan area.

METHODS

An LLM (ChatGPT 4o Mini, Open AI, San Francisco, CA, USA) with no prior task-specific training was given real ambulance requests from a major metropolitan city in the United States. Requests were batched into groups of four, and the LLM was prompted to identify which of the four patients should be prioritized. The same groupings of four requests were then shown to a panel of experienced critical care paramedics who voted on which patient should be prioritized.

RESULTS

Across 98 groupings of four ambulance requests (392 total requests), the LLM agreed with the paramedic panel in most cases (76.5 %, n = 75). In groupings where the paramedic panel was unanimous in their decision (n = 48), the LLM agreed with the unanimous panel in 93.8 % of groupings (n = 45).

CONCLUSIONS

Our preliminary analysis indicates LLMs may have the potential to become a useful tool for triage and resource allocation in emergency care settings, especially in cases where there is consensus among subject matter experts. Further research is needed to better understand and clarify how they may best be of service.

Pre-Hospital Stroke Care beyond the MSU

PURPOSE OF REVIEW

Mobile stroke units (MSU) have established a new, evidence-based treatment in prehospital stroke care, endorsed by current international guidelines and can facilitate pre-hospital research efforts. In addition, other novel pre-hospital modalities beyond the MSU are emerging. In this review, we will summarize existing evidence and outline future trajectories of prehospital stroke care & research on and off MSUs.

RECENT FINDINGS

The proof of MSUs' positive effect on patient outcomes is leading to their increased adoption in emergency medical services of many countries. Nevertheless, prehospital stroke care worldwide largely consists of regular ambulances. Advancements in portable technology for detecting neurocardiovascular diseases, telemedicine, AI and large-scale ultra-early biobanking have the potential to transform prehospital stroke care also beyond the MSU concept. The increasing implementation of telemedicine in emergency medical services is demonstrating beneficial effects in the pre-hospital setting. In synergy with telemedicine the exponential growth of AI-technology is already changing and will likely further transform pre-hospital stroke care in the future. Other promising areas include the development and validation of miniaturized portable devices for the pre-hospital detection of acute stroke. MSUs are enabling large-scale screening for ultra-early blood-based biomarkers, facilitating the differentiation between ischemia, hemorrhage, and stroke mimics. The development of suitable point-of-care tests for such biomarkers holds the potential to advance pre-hospital stroke care outside the MSU-concept. A multimodal approach of AI-supported telemedicine, portable devices and blood-based biomarkers appears to be an increasingly realistic scenario for improving prehospital stroke care in regular ambulances in the future.

Spanish vs USA cohort comparison of prehospital trauma scores to predict short-term mortality

BACKGROUND

This study aimed to evaluate three prehospital early warning scores (EWSs): RTS, MGAP and MREMS, to predict short-term mortality in acute life-threatening trauma and injury/illness by comparing United States (US) and Spanish cohorts.

METHODS

A total of 8,854 patients, 8,598/256 survivors/nonsurvivors, comprised the unified cohort. Datasets were randomly divided into training and test sets. Training sets were used to analyse the discriminative power of the scores in terms of the area under the curve (AUC), and the score performance was assessed in the test set in terms of sensitivity (SE), specificity (SP), accuracy (ACC) and balanced accuracy (BAC).

RESULTS

The three scores showed great discriminative power with AUCs>0.90, and no significant differences between cohorts were found. In the test set, RTS/MREMS/MGAP showed SE/SP/ACC/BAC values of 86.0/89.9/89.6/87.1%, 91.0/86.9/87.5/88.5%, and 87.7/82.9/83.4/85.2%, respectively.

CONCLUSIONS

All EWSs showed excellent ability to predict the risk of short-term mortality, independent of the country.

Redefining trauma deserts: novel technique to accurately map prehospital transport time

Background

Prehospital transport time has been directly related to mortality for hemorrhaging trauma patients. 'Trauma deserts' were previously defined as being outside of a 5-mile radial distance of an urban trauma center. We postulated that the true 'desert' should be based on transport time rather than transport distance.

Methods

Using the Chicagoland area that was used to describe 'trauma deserts,' a sequential process to query a commercial travel optimization product to map transport times over coordinates that covered the entire urban area at a particular time of day. This produces a heat map representing prehospital transport times. Travel times were then limited to 15 minutes to represent a temporally based map of transport capabilities. This was repeated during high and low traffic times and for centers across the city.

Results

We demonstrated that the temporally based map for transport to a trauma center in an urban center differs significantly from the radial distance to the trauma center. Primary effects were proximity to highways and the downtown area. Transportation to centers were significantly different when time was considered instead of distance (p<0.001). We were further able to map variations in traffic patterns and thus transport times by time of day. The truly 'closest' trauma center by time changed based on time of day and was not always the closest hospital by distance.

Discussion

As the crow flies is not how the ambulance drives. This novel technique of dynamically mapping transport times can be used to create accurate trauma deserts in an urban setting with multiple trauma centers. Further, this technique can be used to quantify the potential benefit or detriment of adding or removing firehouses or trauma centers.