Ill and injured children have unique needs that can be magnified when the child' s ailment is serious or life-threatening. This is especially true in the outof-hospital environment. Providing high-quality out-of-hospital care to children requires an emergency medical services (EMS) system infrastructure designed to support the care of pediatric patients. As in the emergency department setting, it is important that all EMS agencies have the appropriate resources, including physician oversight, trained and competent staff, education, policies, medications, equipment, and supplies, to provide effective emergency care for children. Resource availability across EMS agencies is variable, making it essential that EMS medical directors, administrators, and personnel collaborate with outpatient and hospital-based pediatric experts, especially those in emergency departments, to optimize prehospital emergency care for children. The principles in the policy statement "Pediatric Readiness in Emergency Medical Services Systems" and this accompanying technical report establish a foundation on which to build optimal pediatric care within EMS systems and serve as a resource for clinical and administrative EMS leaders. DEFINITIONS • Emergency medical services (EMS): An intricate and comprehensive system, which in a coordinated response, provides the arrangements of personnel, facilities, and equipment for the effective, coordinated, and timely delivery of health and safety services to provide emergency care. 1,2 • Out of hospital: A term used in emergency medicine to mean "in the field," "in the community," "at the patient's home or workplace," or "prehospital." Assessments performed and treatments given out of
The opioid crisis is a growing concern for Americans, and it has become the leading cause of injury-related death in the United States. An adjunct to respiratory support that can reduce this high mortality rate is the administration of naloxone by Emergency Medical Services (EMS) practitioners for patients with suspected opioid overdose. However, clear evidence-based guidelines to direct EMS use of naloxone for opioid overdose have not been developed. Leveraging the recent Agency for Healthcare Research and Quality (AHRQ) systematic review on the EMS administration of naloxone for opioid poisonings, federal partners determined the need for a clinical practice guideline for EMS practitioners faced with suspected opioid poisoning. Project funding was provided by the National Highway Traffic Safety Administration, Office of EMS, (NHTSA OEMS), and the Health Resources and Services Administration, Maternal and Child Health Bureau's EMS for Children Program (EMSC). The objectives of this project were to develop and disseminate an evidence-based guideline and model protocol for administration of naloxone by EMS practitioners to persons with suspected opioid overdose.We have four
BackgroundWe evaluated the ability of experienced trauma surgeons to accurately predict specific blunt injuries, as well as patient disposition from the emergency department (ED), based only on the initial clinical evaluation and prior to any imaging studies. It would be hypothesized that experienced trauma surgeons’ initial clinical evaluation is accurate for excluding life-threatening blunt injuries and for appropriate admission triage decisions.MethodsUsing only their history and physical exam, and prior to any imaging studies, three (3) experienced trauma surgeons, with a combined Level 1 trauma experience of over 50 years, predicted injuries in patients with an initial GCS (Glasgow Coma Score) of 14–15. Additionally, ED disposition (ICU, floor, discharge to home) was also predicted. These predictions were compared to actual patient dispositions and to blunt injuries documented at discharge.ResultsA total of 101 patients with 92 blunt injuries were studied. 43/92 (46.7 %) injuries would have been missed by only performing an initial history and physical exam (“Missed injury”). A change in treatment, though often minor, was required in 19/43 (44.2 %) of the missed injuries. Only 1/43 (2.3 %) of these “missed injuries” (blunt aortic injury) required surgery. Sensitivity, specificity, and accuracy for injury prediction were 53.2, 95.9, and 92.3 % respectively. Positive and negative predictive values were 53.8 and 95.8 % respectively. Prediction of disposition from the ED was 77.8 % accurate. In 7/34 (20.6 %) patients, missed injuries led to changes in disposition. “Undertriage” occurred in 9/99 (9.1 %) patients (Predicted for floor but admitted to ICU). Additionally, 8/84 (9.5 %) patients predicted for floor admission were sent home from the ED; and 5/13 (38.5 %) patients predicted for ICU admission were actually sent to the floor after complete evaluations, giving an “overtriage” rate of 13/99 (13.1 %) patients.ConclusionsIn a neurologically-intact group of trauma patients, experienced trauma surgeons would have missed 46.7 % of the actual injuries, based only on their history and physical exam. Once accurate diagnoses of injuries were completed, usually with the help of CT scans, admission dispositions changed in 20.6 % of patients. Treatment changes occurred in 44.2 % of the missed injuries, though usually minimal. Broad elimination of early imaging studies in alert, blunt trauma patients cannot be advocated.
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.
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