IntroductionResident remediation is a pressing topic in emergency medicine (EM) training programs. Simulation has become a prominent educational tool in EM training and been recommended for identification of learning gaps and resident remediation. Despite the ubiquitous need for formalized remediation, there is a dearth of literature regarding best practices for simulation-based remediation (SBR).MethodsWe conducted a literature search on SBR practices using the terms “simulation,” “remediation,” and “simulation based remediation.” We identified relevant themes and used them to develop an open-ended questionnaire that was distributed to EM programs with experience in SBR. Thematic analysis was performed on all subsequent responses and used to develop survey instruments, which were then used in a modified two-round Delphi panel to derive a set of consensus statements on the use of SBR from an aggregate of 41 experts in simulation and remediation in EM.ResultsFaculty representing 30 programs across North America composed the consensus group with 66% of participants identifying themselves as simulation faculty, 32% as program directors, and 2% as core faculty. The results from our study highlight a strong agreement across many areas of SBR in EM training. SBR is appropriate for a range of deficits, including procedural, medical knowledge application, clinical reasoning/decision-making, communication, teamwork, and crisis resource management. Simulation can be used both diagnostically and therapeutically in remediation, although SBR should be part of a larger remediation plan constructed by the residency leadership team or a faculty expert in remediation, and not the only component. Although summative assessment can have a role in SBR, it needs to be very clearly delineated and transparent to everyone involved.ConclusionSimulation may be used for remediation purposes for certain specific kinds of competencies as long as it is carried out in a transparent manner to all those involved.
Introduction: Traditional simulation debriefing is both time-and resource-intensive. Shifting the degree of primary learning responsibility from the faculty to the learner through self-guided learning has received greater attention as a means of reducing this resource intensity. The aim of the study was to determine if video-assisted self-debriefing, as a form of self-guided learning, would have equivalent learning outcomes compared to standard debriefing.Methods: This randomized cohort study consisting of 49 PGY-1 to -3 emergency medicine residents compared performance after video self-assessment utilizing an observer checklist versus standard debriefing for simulated emergency department procedural sedation (EDPS). The primary outcome measure was performance on the second EDPS scenario.Results: Independent-samples t-test found that both control (standard debrief) and intervention (video selfassessment) groups demonstrated significantly increased scores on Scenario 2 (standard-t(40) = 2.20, p < 0.05; video-t(45) = 3.88, p < 0.05). There was a large and significant positive correlation between faculty and resident self-evaluation (r = 0.70, p < 0.05). There was no significant difference between faculty and residents selfassessment mean scores (t(24) = 1.90, p = 0.07). Conclusions:Residents receiving feedback on their performance via video-assisted self-debriefing improved their performance in simulated EDPS to the same degree as with standard faculty debriefing. Video-assisted selfdebriefing is a promising avenue for leveraging the benefits of simulation-based training with reduced resource requirements. P rocedural sedation is a core competency for the practice of emergency medicine comprising a specific competency milestone in the Accreditation for Graduate Medical Education Next Accreditation System. 1 Despite advances in technology such as end-tidal CO 2 monitoring, the safety profiles of commonly used From the
Sepsis is a major cause of mortality among hospitalized patients worldwide. Shorter time to administration of broad-spectrum antibiotics is associated with improved outcomes, but early recognition of sepsis remains a major challenge. In a two-center cohort study with prospective sample collection from 1400 adult patients in emergency departments suspected of sepsis, we sought to determine the diagnostic and prognostic capabilities of a machine-learning algorithm based on clinical data and a set of uncommonly measured biomarkers. Specifically, we demonstrate that a machine-learning model developed using this dataset outputs a score with not only diagnostic capability but also prognostic power with respect to hospital length of stay (LOS), thirty-day mortality, and thirty-day inpatient readmission both in our entire testing cohort and various subpopulations. The area under the Receiver Operating Curve (AUROC) for diagnosis of sepsis was 0.83. Predicted risk scores for patients with septic shock were higher compared to patients with sepsis but without shock (p < 0.0001). Scores for patients with infection and organ dysfunction were higher compared to those without either condition (p < 0.0001). Stratification based on predicted scores of the patients into low, medium and high-risk groups showed significant differences in length of stay (p < 0.0001), thirty-day mortality (p < 0.0001), and thirty-day inpatient readmission (p < 0.0001). In conclusion, a machine-learning algorithm based on EMR data and three non-routinely measured biomarkers demonstrated good diagnostic and prognostic capability at the time of initial blood culture.
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