The Skeptics Guide to Emergency Medicine

Reference: Muldowney et al. A Comparison of Ketamine to Midazolam for the Management of Acute Behavioral Disturbance in the Out-of-Hospital Setting. Ann Emerg Med. 2025  Date: April 24, 2025 Guest Skeptic: Dr. Howie Mell received his Medical Doctorate (MD) from the University of Illinois at Chicago, College of Medicine at Rockford. Prior to that, he received a Master of Public Health (MPH) degree emphasizing Environmental and Occupational Health from the University of Illinois at Chicago, School of Public Health, while serving as a firefighter/paramedic in the Chicago suburbs. He completed his residency in emergency medicine at the Mayo Graduate School of Medicine, Rochester, Minnesota. Dr. Mell is board-certified by the American Board of Emergency Medicine in both Emergency Medicine (EM) and Emergency Medical Services (EMS) Medicine. He is a Fellow of the American College of Emergency Physicians (FACEP). Dr. Mell serves as an Ambassador Emergency Physician for Vituity (formerly CEP-America), and he is currently assigned to Schneck Medical Center in Seymour, Indiana (John Cougar Mellencamp’s “Small Town”). Case: You’re an experienced paramedic working a busy night shift in an urban EMS system. Dispatch sends you to a call for a 35-year-old male found acting erratically in a public park. Upon arrival, you find him disoriented, agitated, and combative. Bystanders report that he has been using methamphetamine and alcohol. The patient is uncooperative, making verbal de-escalation ineffective. Physical restraint is needed for transport. Your EMS protocol allows for pharmacologic sedation with either midazolam (1 to 5 mg IV/IM, repeat every 2 to 5 minutes as needed) or ketamine (5 mg/kg IM, max 500 mg). The patient is tachycardic (HR 122 bpm), hypertensive (BP 156/96 mmHg), and has a Glasgow Coma Scale (GCS) score of 12. You need to act quickly for scene safety and the patient's well-being. Background: Acutely agitated patients in the pre-hospital setting present a unique challenge for emergency medical services (EMS). Agitation can stem from various underlying conditions, including psychiatric disorders, substance intoxication, metabolic disturbances, traumatic brain injury, or postictal states​. If not managed appropriately, severe agitation can escalate, leading to self-harm, harm to others, or interference with necessary medical care. Initial management emphasizes verbal de-escalation techniques, which should always be attempted first. However, when these strategies fail, pharmacologic sedation may be necessary to ensure the safety of both the patient and pre-hospital providers. The choice of sedative agent is a critical decision. The paramedic must balance the need for rapid sedation with the risk of adverse effects, including respiratory depression and cardiovascular instability​. Benzodiazepines, such as midazolam, have historically been used for pre-hospital sedation due to their anxiolytic and muscle-relaxant properties. However, their use is associated with risks such as respiratory depression and paradoxical agitation. In recent years, ketamine has gained popularity due to its rapid onset, potent dissociative properties, and preservation of airway reflexes​. Despite its advantages, ketamine is not without concerns, including the potential for emergence reactions, increased blood pressure, and the need for airway management in some cases. Current guidelines lack consensus on the optimal pharmacologic approach, leading to significant variation in practice across EMS systems. The ongoing debate surrounding the best sedation strategy highlights the need for robust clinical research to guide evidence-based practice. A newly published study aims to address this knowledge gap by comparing ketamine and midazolam in the out-of-hospital setting, shedding light on their relative efficacy and safety. Clinical Question: In prehospital patients requiring pharmacologic sedation for acute behavioural disturbance,...

Reference: Kareemi et al Artificial intelligence-based clinical decision support in the emergency department: a scoping review. AEM April 2025. Date: April 15, 2025 Guest Skeptic: Dr. Kirsty Challen is a Consultant in Emergency Medicine at Lancashire Teaching Hospitals. Case: It may be April, but as you sit in your departmental meeting with your emergency physician colleagues, you all note that the winter “surge” of patients hasn’t stopped. The decision fatigue at the end of shifts is as present as ever. “Surely AI will be making some of these decisions better than us soon?” says one of your colleagues, only half joking. Another colleague chips in that the medical students at the nearby university have been warned against using ChatGPT to create differential diagnoses and you are left wondering whether AI might be “working” in the ED soon. Background: Emergency departments can be a high-pressure environment. Clinical decisions must be made quickly and accurately, often with incomplete information. Clinical decision support (CDS) tools aim to address this challenge by offering real-time, evidence-informed recommendations that help clinicians make better diagnostic, prognostic, and therapeutic decisions. CDS spans a wide spectrum from traditional paper-based clinical decision rules to smartphone apps (MDCalc) to more integrated systems into electronic health records (EHRs). These tools function by combining patient data with expert-driven algorithms or guidelines to inform care pathways. They can help determine disease likelihood, risk stratify patients and even guide resource utilization such as imaging or admission decisions​. Recent years have seen a growing interest in applying artificial intelligence (AI), particularly machine learning (ML), to CDS. Unlike traditional "knowledge-based" CDS that relies on literature-based thresholds, AI-driven tools derive patterns from large datasets ("big data") to identify associations and make predictions. These "non–knowledge-based" systems promise to augment human decision-making by uncovering insights that might be overlooked by clinicians or static rules​. However, the majority of AI-based CDS (AI-CDS) tools remain in early development. Few have been rigorously tested in the ED, and even fewer have demonstrated improvements in patient outcomes or clinician workflow. Despite FDA clearance for some tools, evidence for real-world impact remains limited​. Emergency physicians are right to approach this technology with skeptical optimism. We will need to balance the transformative potential of AI with a critical eye toward evidence, safety, and usability. Clinical Question: (1) What is the current landscape of AI-CDS tools for prognostic, diagnostic, and treatment decisions for individual patients in the ED? and (2) What phase of development have these AI-CDS tools achieved? Reference: Kareemi et al Artificial intelligence-based clinical decision support in the emergency department: a scoping review. AEM April 2025. Population: Studies involving AI or ML-based clinical decision support tools applied to individual patient care in the ED, published 2010 - 2023. Excluded: Models that assessed a specific test (e.g. imaging) without clinical context, administrative or operational outcomes (e.g. patient census), models involving irrelevant data (e.g. vignettes or data not available following the emergency assessment), length of stay as a primary outcome, studies without full text or abstract in English. Intervention: AI- or ML-based clinical decision support tools used during patient care in the ED. Comparison: Not applicable for a scoping review. However, the review identified whether studies involved any comparison with usual care, clinician judgment, or non-AI tools. Outcomes: The review didn’t focus on a single outcome but instead categorized studies by their targeted clinical decision task—diagnosis, prognosis, disposition, treatment,

Reference: Sax DR, et al. Emergency Severity Index Version 4 and Triage of Pediatric Emergency Department Patients. JAMA Pediatrics, October 2024 Date: February 12, 2025 Dr. Brandon Ho Guest Skeptic: Dr. Brandon Ho is a graduating pediatric emergency medicine fellow at Children’s National Hospital in Washington DC and soon to be attending physician at Seattle Children's. His research interests include AI in healthcare, medical education, and social determinants of health. Case: You are approached by the medical director of your emergency department (ED). She has noticed that recently, there has been an increasing number of pediatric cases presenting to your facility. In some of these cases, the children ended up being more sick than initially triaged. As the institution’s evidence-based medicine enthusiast, she asks you, “What do you think of the triage system we’re using now? How accurate is it for children?” Background: Pediatric triage is a fundamental component of emergency medicine, serving as the first critical step in managing acutely ill or injured children in the emergency department (ED). Unlike adult triage, pediatric triage presents unique challenges due to variations in physiology, developmental differences, and communication barriers in younger patients. Accurately assessing the severity of a child’s condition is essential for ensuring timely intervention while avoiding unnecessary resource utilization.  The Emergency Severity Index (ESI) is the most widely used triage system in the United States. It classifies patients based on acuity and predicted resource utilization, ranging from ESI Level 1 (requiring immediate, life-saving intervention) to ESI Level 5 (requiring no resources beyond physician evaluation). However, pediatric triage remains particularly challenging due to factors such as age-based vital sign variations, difficulty in obtaining an accurate history, and non-specific presentations of critical illness​. Typically, ESI levels 1 and 2 are used to assess acuity and risk of instability. ESI levels 3, 4, and 5) are determined by expected resource needs. Those resources can be labs, imaging, medications, consultations, etc. Despite its widespread use, it’s imperfect with previous studies reporting mistriage rates as high as 50%. Pediatric patients can either be undertriage (assigning a lower acuity level than warranted) or overtriage (assigning a higher acuity level than necessary). This can have significant consequences when EDs are experiencing prolonged wait times, the boarding of patients, and are chronically short-staffed. Undertriage may lead to delayed care for critically ill children, whereas overtriage can result in unnecessary resource use, increased healthcare costs, and prolonged ED crowding. Studies have shown that pediatric patients are frequently subject to both types of errors, with younger children and those presenting with atypical symptoms being at risk​. Clinical Question: How accurate is ESI version 4 in predicting acuity and resource needs among pediatric ED patients? Reference: Sax DR, et al. Emergency Severity Index Version 4 and Triage of Pediatric Emergency Department Patients. JAMA Pediatrics, October 2024 Population: Pediatric patients (aged 0-18 years) presenting to 21 Kaiser Permanente Northern California ED’s from January 1, 2016, to December 31, 2020. Excluded: Missing ESI, incomplete ED time variables, transferred patients, patients who left against medical advice (AMA) or left without being seen (LWBS). Exposure: Assigned ESI level compared to actual resource utilization and critical interventions. Comparison: Correct triage rates were compared against undertriaged and overtriaged cases to identify patterns of mistriage​. Outcome: Primary Outcome: The rate of mistriage (undertriage or overtriage) of pediatric patients using ESI v4​. Secondary Outcomes: Patient and visit characteristics assoc...

The discussion dives into the myth that non-urgent patients are the primary cause of emergency department crowding. Misconceptions surrounding this issue are debunked, illustrating the risks of diverting patients who might have serious conditions. The conversation critiques traditional approaches, labeling them as ineffective solutions and calling for evidence-based strategies. It emphasizes the need for comprehensive solutions that address deeper healthcare system flaws, rather than just treating the symptoms of overcrowding.

Dive into the latest findings in emergency medicine as key research papers are dissected. Discover innovative pre-oxygenation techniques and the reliability of trials that can reshape clinical practices. Explore pivotal insights into pediatric injuries and the nuances of decision-making in critical situations. The discussion highlights biases in research and safe sedation methods for agitated patients, while also questioning the safety of anticoagulant reversal trials. A must-listen for anyone in the medical field!

Nicholas Peoples, a standout medical student from Baylor College of Medicine with a rich background in global health, dives into the pressing issue of citation errors in biomedical literature. He reveals that up to 40% of citations may reference non-existent studies, undermining clinical practice. The conversation highlights the role of AI in enhancing citation accuracy and the urgent need for accountability among researchers. They also discuss the cultural shift necessary in academia to ensure integrity and trust in scientific research.

Reference: Pessano S, et al. Positioning for lumbar puncture in newborn infants. Cochrane Database Syst Rev. December 2023 Date: February 7, 2025 Dr. Lauren Rosenfeld Guest Skeptic: Dr. Lauren Rosenfeld is a PGY-3 emergency medicine resident at George Washington University. She is also a new podcast host for Emergency Medicine Residents’ Association (EMRA) Cast Series. Case: A five-day-old girl is brought to the emergency department (ED) for fever by her parents. She was born full-term and seemed to be doing very well after the family returned home. Her mother had an uneventful pregnancy and delivery. Today, the parents thought she was feeling warm and took the girl’s temperature, which was 101°F (38.3°C). They called the pediatrician, who told them to go to the ED for more testing and warned them of the likelihood that their baby may need a lumbar puncture. The worried father asks you, “What is a lumbar puncture? Will it hurt?” Her mother asks you, “Is it like when I got an epidural before delivering? Will you sit her up for it? She can’t sit yet.”  Background:  We have covered the topic of febrile infants and lumbar punctures (LP) before on the SGEM. However, we typically focused on the febrile infant part. Today we’re going to talk more about performing the procedure of a lumbar puncture on babies.  In the ED, lumbar punctures are typically performed in infants with fever in the evaluation for invasive bacterial infections including meningitis. There are many thoughts and bits of advice around how to perform an LP including the proper position, when to remove the stylet from the needle, what kind of analgesia to use, etc. There are multiple positions to set up the lumbar puncture. Commonly, patients can be placed on their side in the lateral decubitus, bend the neck so the chin is close to the chest, hunch the back, and bring the knees toward the chest to approximate the fetal position. Alternatively, patients may also sit upright and then bend their head and shoulders forward. When it comes to infants, most of the time, we are relying on someone else to help hold the baby in those positions as we’re performing the LP. Sometimes, these babies can have episodes of oxygen desaturation when they get held in that position for too long. Clinical Question: How does the positioning of infants during lumbar puncture (lateral decubitus vs sitting vs prone) affect success rates and adverse events?  Reference: Pessano S, et al. Positioning for lumbar puncture in newborn infants. Cochrane Database Syst Rev. December 2023 Population: preterm and term infants of postmenstrual age up to 46 weeks and 0 days. Age 4.9 hours to 5 weeks Intervention: Infants positioned in a lateral decubitus position. Comparison: Infants positioned in a sitting position or prone position​. Outcome: Primary Outcome(s): Successful lumbar puncture on the first attempt, with < 500 red blood cells/mm3. Total number of lumbar puncture attempts (successful or unsuccessful). Episodes of bradycardia, defined as a decrease in HR of more than 30% below baseline or less than 100bpm for 10 seconds or longer. Secondary Outcomes: Time to perform LP, episodes of desaturation (SpO2 <80%), apnea, need for pain/sedation medication, skin changes at LP site, infection rate related to LP, pain, and parental satisfaction. Type of Study: Systematic Review Meta-analysis Authors’ Conclusions: “When compared to sitting position, lateral decubitus position probably results in little to no difference in successful lumbar puncture procedure at first attempt. None of the included studies reported the total number of lumbar puncture attempts as specified in this review. Furthermore, infants in a sitting position likely experience less episodes of bradycardia and oxygen desaturation than in the lateral decubitus, and there may be little to no difference in episodes of apnea.

In this discussion, Dr. Lauren Westafer, an Assistant Professor and pulmonary embolism expert from the University of Massachusetts Medical School, delves into the nuances of D-dimer testing. She highlights the challenges of ruling out pulmonary embolism (PE) in high-risk patients, spotlighting striking findings from recent research. The conversation critically assesses the reliability of D-dimer levels and biases in existing studies, urging a need for reevaluation of testing strategies for better outcomes in emergency medicine.

Dr. Aaron Skolnik, an Assistant Professor at Mayo Clinic and critical care expert, dives into the complexities of diagnosing sepsis. He highlights a critical case of a 62-year-old man with severe symptoms and discusses the limitations of current biomarkers. The conversation centers on monocyte distribution width (MDW) as a promising but not yet routine diagnostic tool for sepsis in emergency settings. Skolnik also examines biases in sepsis studies, underscoring the importance of clinician judgment in making accurate diagnoses.

Dr. Ross Prager, an Intensivist and adjunct professor, dives into the transformative role of AI in clinical research. He discusses how AI can enhance everything from study design to data analysis, but emphasizes the importance of collaboration for success. Ethical challenges, such as privacy risks and biases in AI models, are explored, along with the necessity of maintaining research integrity. Ultimately, the conversation reveals a future where AI revolutionizes patient-centered care while demanding vigilance from researchers.