Multi-Institutional Multidisciplinary Injury Mortality Investigation in the Civilian Pre-Hospital Environment (MIMIC): a methodology for reliably measuring prehospital time and distance to definitive care

Activation and On-Scene Intervals for Severe Trauma EMS Interventions: An Analysis of the NEMSIS Database

Objective: Time to care is a determinant of trauma patient outcomes, and timely delivery of trauma care to severely injured patients is critical in reducing mortality. Numerous studies have analyzed access to care using prehospital intervals from a Carr et al. meta-analysis of studies from 1975 to 2005. Carr et al.'s research sought to determine national mean activation and on-scene intervals for trauma patients using contemporary emergency medical services (EMS) records. Since the Carr et al. meta-analysis was published, the National Highway Traffic Safety Administration (NHTSA) created and refined the National Emergency Medical Services Information System (NEMSIS) database. We sought to perform a modern analysis of prehospital intervals to establish current standards and temporal patterns.Methods: We utilized NEMSIS to analyze EMS data of trauma patients from 2016 to 2019. The dataset comprises more than 94 million EMS records, which we filtered to select for severe trauma and stratified by type of transport and rurality to calculate mean activation and on-scene intervals. Furthermore, we explored the impact of basic life support (BLS) and advanced life support (ALS) of ground units on activation and on-scene time intervals.Results: Mean activation and on-scene intervals for ground transport were statistically different when stratified by rurality. Urban, suburban, and rural ground activation intervals were 2.60 ± 3.94, 2.88 ± 3.89, and 3.33 ± 4.58 minutes, respectively. On-scene intervals were 15.50 ± 10.46, 17.56 ± 11.27, and 18.07 ± 16.13 minutes, respectively. Mean helicopter transport activation time was 13.75 ± 7.44 minutes and on-scene time was 19.42 ± 16.09 minutes. This analysis provides an empirically defined mean for activation and on-scene times for trauma patients based on transport type and rurality. Results from this analysis proved to be significantly longer than the previous analysis, except for helicopter transport on-scene time. Shorter mean intervals were seen in ALS compared to BLS for activation intervals, however ALS on-scene intervals were marginally longer than BLS.Conclusions: With the increasing sophistication of geospatial technologies employed to analyze access to care, these intervals are the most accurate and up-to-date and should be included in access to care models.

Hispanic Farmers Experience Shorter EMS Response Times but Longer Emergency Department Length of Stay Following Occupational Injuries

BACKGROUND

Agriculture has the highest rate of fatal injuries by sector. Hispanic workers also experience more fatal work injuries than every other minority group combined. Pre-hospital and initial trauma evaluation represent an important marker to understand the impact of a trauma system. We sought to investigate whether Hispanic agricultural workers in the United States (US) experience disparities following traumatic occupational injuries in terms of pre-hospital and emergency department care.

METHODS

We retrospectively analyzed the National Trauma Data Bank from 2012-2016 to understand differences between Hispanic and non-Hispanic farmers in Emergency Medical Services (EMS) response and transport times (minutes), transport mode, transfer rates, presentation to University or Level I trauma hospitals, Injury Severity Scores (ISS), length of stay (LOS) in the emergency department (ED, minutes) or hospital (days), need for the operating room (OR), admittance to the Intensive Care Unit (ICU), and mortality.

RESULTS

A total of 6,161 farmers were included in our analyses (median age 47 years, females 7.0%). Multivariable analyses indicate differences regarding EMS response, EMS transport, and LOS in the ED. Rates of admission to the ICU, surgical operations, days on a ventilator, discharge from the hospital with supportive care, and mortality did not differ between groups.

CONCLUSIONS

Non-Hispanic patients have longer median EMS response and total transport times. Hispanic patients have longer median LOS in the ED. However, the lack of significant differences in management variables other than EMS times and ED LOS indicate an equitable delivery of trauma care once patients were transferred from the ED.

Do EMS times associate with injury severity?

In this study, emergency medical services times, along with other crash-related explanatory variables, have been used to investigate influential factors on injury severity. To overcome the complexity of emergency medical services times impact on crash outcome, the interaction effects of EMS times and injury location on the body were also investigated in a separate model. This study utilized the linked data of police-reported crash data and emergency medical services runs, including 2192 crash injuries that transferred to hospital. A random-effects ordered probit approach was implemented to identify effective factors on crash injury severity. Three models of (1) crash-related variables, (2) crash-related and emergency medical services times and (3) crash-related, emergency medical services times and interaction effects of EMS times and injury location on the body were developed. Although the outcome could not find the impact of faster emergency medical services times on injury severity in the second model, in the third model, faster response time and slower on-scene time were associated with decreasing the severity of entire-body injuries. We discuss why this may be the case.

Natural language processing of prehospital emergency medical services trauma records allows for automated characterization of treatment appropriateness

BACKGROUND

Incomplete prehospital trauma care is a significant contributor to preventable deaths. Current databases lack timelines easily constructible of clinical events. Temporal associations and procedural indications are critical to characterize treatment appropriateness. Natural language processing (NLP) methods present a novel approach to bridge this gap. We sought to evaluate the efficacy of a novel and automated NLP pipeline to determine treatment appropriateness from a sample of prehospital EMS motor vehicle crash records.

METHODS

A total of 142 records were used to extract airway procedures, intraosseous/intravenous access, packed red blood cell transfusion, crystalloid bolus, chest compression system, tranexamic acid bolus, and needle decompression. Reports were processed using four clinical NLP systems and augmented via a word2phrase method leveraging a large integrated health system clinical note repository to identify terms semantically similar with treatment indications. Indications were matched with treatments and categorized as indicated, missed (indicated but not performed), or nonindicated. Automated results were then compared with manual review, and precision and recall were calculated for each treatment determination.

RESULTS

Natural language processing identified 184 treatments. Automated timeline summarization was completed for all patients. Treatments were characterized as indicated in a subset of cases including the following: 69% (18 of 26 patients) for airway, 54.5% (6 of 11 patients) for intraosseous access, 11.1% (1 of 9 patients) for needle decompression, 55.6% (10 of 18 patients) for tranexamic acid, 60% (9 of 15 patients) for packed red blood cell, 12.9% (4 of 31 patients) for crystalloid bolus, and 60% (3 of 5 patients) for chest compression system. The most commonly nonindicated treatment was crystalloid bolus (22 of 142 patients). Overall, the automated NLP system performed with high precision and recall with over 70% of comparisons achieving precision and recall of greater than 80%.

CONCLUSION

Natural language processing methodologies show promise for enabling automated extraction of procedural indication data and timeline summarization. Future directions should focus on optimizing and expanding these techniques to scale and facilitate broader trauma care performance monitoring.

LEVEL OF EVIDENCE

Diagnostic tests or criteria, level III.