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When emergency medical service (EMS) arrives on the scene, it is estimated that patients end up declining treatment and/or transport around 5% to 10% of the time. When a patient is suspected of having a life-threatening emergency and declines care, these cases are deemed “high-risk refusals,” as the subsequent delay in treatment drastically increases the risk of morbidity and mortality, as well as the legal risk for all those involved. These cases warrant careful consideration and deliberate training, not only among EMS professionals and EMS medical directors but also among any in-hospital physicians providing online medical oversight. To mitigate the risk associated with high-risk refusals, EMS systems and emergency departments should have standardized policies and guidelines in place for managing these cases, just as with any other complex, high-risk procedure. State statutes should be referenced and ideally legal counsel should be consulted when developing these guidelines. Checklists and quick references are recommended, and calling online medical supervision should be strongly encouraged. EMS medical directors should also routinely review high-risk refusal charts and include these cases in their ongoing quality improvement and quality assessment efforts.

Artificial intelligence (AI) is increasingly being utilized to augment the practice of emergency medicine due to rapid technological advances and breakthroughs. AI applications have been used to enhance triage systems, predict disease-specific risk, estimate staffing needs, forecast patient decompensation, and interpret imaging findings in the emergency department setting. This article aims to help readers without formal training become informed end-users of AI in emergency medicine. The authors will briefly discuss the principles and key terminology of AI, the reasons for its rising popularity, its potential applications in the emergency department setting, and its limitations. Additionally, resources for further self-studying will also be provided.

9-1-1 call centers are a critical component of prehospital care: they accept emergency calls, dispatch field responders such as emergency medical services, and provide callers with emergency medical instructions before their arrival. The aim of this study was to describe the technical structure of the 9-1-1 call-taking system and to describe its vulnerabilities that could lead to compromised patient care. 9-1-1 calls answered from mobile phones and landlines use a variety of technologies to provide information about caller location and other information. These interconnected technologies create potential cyber vulnerabilities. A variety of attacks could be carried out on 9-1-1 infrastructure to various ends. Attackers could target individuals, groups, or entire municipalities. These attacks could result in anything from a nuisance to increased loss of life in a physical attack to worse overall outcomes owing to delays in care for time-sensitive conditions. Evolving 9-1-1 systems are increasingly connected and dependent on network technology. As implications of cybersecurity vulnerabilities loom large, future research should examine methods of hardening the 9-1-1 system against attack.

ST-segment elevation myocardial infarction (STEMI) is a time-sensitive entity that has been shown to benefit from prehospital diagnosis by electrocardiogram (ECG). Current computer algorithms with binary decision making are not accurate enough to be relied on for cardiac catheterization lab (CCL) activation. An algorithmic approach is proposed to stratify binary STEMI computerized ECG interpretations into low, intermediate, and high STEMI probability tiers. Based on previous literature, a four-criteria algorithm was developed to rule out/in common causes of prehospital STEMI false-positive computer interpretations: heart rate, QRS width, ST elevation criteria, and artifact. Prehospital STEMI cases were prospectively collected at a single academic center in Salt Lake City, Utah (USA) from May 2012 through October 2013. The prehospital ECGs were applied to the algorithm and compared against activation of the CCL by an emergency department (ED) physician as the outcome of interest. In addition to calculating test characteristics, linear regression was used to look for an association between number of criteria used and accuracy, and logistic regression was used to test if any single criterion performed better than another. There were 63 ECGs available for review, 39 high probability and 24 intermediate probability. The high probability STEMI tier had excellent test characteristics for ruling in STEMI when all four criteria were used, specificity 1.00 (95% CI, 0.59-1.00), positive predictive value 1.00 (0.91-1.00). Linear regression showed a strong correlation demonstrating that false-positives increased as fewer criteria were used (adjusted r-square 0.51; P <.01). Logistic regression showed no significant predictive value for any one criterion over another (P = .80). Limiting physician overread to the intermediate tier only would reduce the number of ECGs requiring physician overread by a factor of 0.62 (95% CI, 0.48-0.75; P <.01). Prehospital STEMI ECGs can be accurately stratified to high, intermediate, and low probabilities for STEMI using the four criteria. While additional study is required, using this tiered algorithmic approach in prehospital ECGs could lead to changes in CCL activation and decreased requirements for physician overread. This may have significant clinical and quality implications.

Early detection of ST-segment elevation myocardial infarction (STEMI) on the prehospital electrocardiogram (ECG) improves patient outcomes. Current software algorithms optimize sensitivity but have a high false-positive rate. The authors propose an algorithm to improve the specificity of STEMI diagnosis in the prehospital setting. A dataset of prehospital ECGs with verified outcomes was used to validate an algorithm to identify true and false-positive software interpretations of STEMI. Four criteria implicated in prior research to differentiate STEMI true positives were applied: heart rate <130, QRS <100, verification of ST-segment elevation, and absence of artifact. The test characteristics were calculated and regression analysis was used to examine the association between the number of criteria included and test characteristics. There were 44,611 cases available. Of these, 1,193 were identified as STEMI by the software interpretation. Applying all four criteria had the highest positive likelihood ratio of 353 (95% CI, 201-595) and specificity of 99.96% (95% CI, 99.93-99.98), but the lowest sensitivity (14%; 95% CI, 11-17) and worst negative likelihood ratio (0.86; 95% CI, 0.84-0.89). There was a strong correlation between increased positive likelihood ratio (r = 0.90) and specificity (r = 0.85) with increasing number of criteria. Prehospital ECGs with a high probability of true STEMI can be accurately identified using these four criteria: heart rate <130, QRS <100, verification of ST-segment elevation, and absence of artifact. Applying these criteria to prehospital ECGs with software interpretations of STEMI could decrease false-positive field activations, while also reducing the need to rely on transmission for physician over-read. This can have significant clinical and quality implications for Emergency Medical Services (EMS) systems.

Continuous-flow left ventricular assist devices (LVADs) are increasingly implanted to support patients with end-stage heart failure. These patients are at high risk for complications, many of which necessitate emergency care. While rehospitalization rates have been described, there is little data regarding emergency department (ED) visits. We hypothesize that ED visits are frequent and often require admission after LVAD implantation. We performed a retrospective review of patients in our health-care system followed by the advanced heart failure service for LVAD management after implantation between January 2011 and July 2015. We accounted for all ED visits in our system through February 2016, 7 months after the last implantation included. Clinically relevant demographic variables and ED visit details were recorded and analyzed to describe this population. We identified 81 patients with complete data, among whom there were 283 visits (3.49 visits/patient), occurring at a rate of approximately 7.3 ED visits per patient per year alive with LVAD. The most common reason for an ED visit is a complication related to bleeding (18% of visits), followed by chest pain (14%) and dizziness or syncope (13%). Thirty-six percent of patients were discharged from the ED without hospital admission. A growing populace with implanted LVADs represents an important population within emergency medicine. They are at risk for significant complications and frequently present to the ED. While many of these visits may be managed without hospital admission, this specialized patient group represents a potential area for improvement in provider education.