Prehospital emergency care : official journal of the National Association of EMS Physicians and the National Association of State EMS Directors
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In many emergency medical services (EMS) systems, a direct medical oversight physician is available to paramedics for mandatory and/or elective consultations. At the time of this study, a clinical support desk (CSD) was being implemented within the medical communications center of a provincial EMS system in addition to the physician resource. The CSD was initially staffed with a registered nurse or an advanced care paramedic. The objective of the current study was to compare CSD "peer to peer" consults versus physician consults with regards to consultation patterns, transport dispositions, and patient safety measures. ⋯ The introduction of a novel "peer-to-peer" consult program was associated with an increased total number of consults made and reduced call volume for direct medical oversight physicians. There was no change in the patient safety measure studied.
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This review aims to understand the present circumstances on the provision of prehospital trauma care in low- and middle-income countries (LMICs), particularly scoping the challenges experienced by LMICs in this regard. The objective is to systematically evaluate the currently available evidence on this topic. Based on the themes and challenges identified in the provision of prehospital trauma care in LMICs, we provide a series of recommendations and a knowledge base for future research in the field. ⋯ The provision of prehospital trauma care in LMICs faces significant barriers at multiple levels, largely dependent on wider social, geographic, economic, and political factors impeding the development of such higher functioning systems within health care. However, there have been numerous breakthroughs within certain LMICs in different aspects of prehospital trauma care, supported to varying degrees by international initiatives, that serve as case studies for widespread implementation and targets. Such experiential learning is essential due to the heterogenous landscapes that comprise LMICs.
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In many parts of the world, emergency medical services (EMS) clinical care is traditionally delivered by different levels or types of EMS clinicians, such as emergency medical technicians and paramedics. In some areas, physicians are also included among the cadre of professionals administering EMS-based care. ⋯ NAEMSP first published recommendations regarding what some of these competencies should be in 1983 and subsequently updated those recommendations in 2002. This document is an updated work, given the evolution of the field.
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Mobile integrated health care (MIH) leverages emergency medical services (EMS) clinicians to perform local health care functions. Little is known about the individual EMS clinicians working in this role. We sought to describe the prevalence, demographics, and training of EMS clinicians providing MIH in the United States (US). ⋯ Few nationally certified US EMS clinicians perform MIH roles. Only half of MIH roles were performed by paramedics; EMT and AEMT clinicians performed a substantial proportion of MIH roles. The observed variability in certification and training suggest heterogeneity in preparation and performance of MIH roles among US EMS clinicians.
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Background: The objective of this study was to develop and validate machine learning models for data entry error detection in a national out-of-hospital cardiac arrest (OHCA) prehospital patient care report database. Methods: Adult OHCAs of presumed cardiac etiology were included. Data entry errors were defined as discrepancies between the coded data and the free-text note documenting the intervention or event; for example, information that was recorded as "absent" in the coded data but "present" in the free-text note. ⋯ Machine learning models detected errors most efficiently for outcome place and initial rhythm errors; 82.6% of place errors and 93.8% of initial rhythm errors could be detected while checking 11 and 35% of data, respectively, compared to the strategy of checking all data. Conclusion: Machine learning models can detect data entry errors in care reports of emergency medical services (EMS) clinicians with acceptable performance and likely can improve the efficiency of the process of data quality control. EMS organizations that provide more prehospital interventions for OHCA patients could have higher error rates and may benefit from the adoption of error-detection models.