ABSTRACT
Stress cardiomyopathy (SCM) is a clinical phenomenon presenting symptoms suggestive of acute coronary syndrome and defined by acute, but transient, electrocardiogram (ECG) changes and left ventricular wall motion abnormalities. However, no obstructive coronary lesion is identified on catheterization, and pathognomic echocardiogram findings are typically encountered. Multiple causes have been posited in the literature (e.g., severe stress, anxiety, pain, comorbid illness, trauma). We present the case of a 46-year-old female who presented to the emergency department (ED) for delayed left-sided hemothorax (six weeks following a high-speed motor vehicle collision) and developed an acute SCM following large-bore chest tube placement. To our knowledge, no prior cases have been reported immediately following thoracostomy tube placement and hemothorax drainage in the ED setting. We explore possible mechanistic explanations related to our case, which adds to the existing literature on the subject.
ABSTRACT
Background Breaking bad news is an essential skill for practicing physicians, particularly in the field of emergency medicine (EM). Patient-physician communication teaching has previously relied on standardized patient scenarios and objective structured clinical examination formats. The novel use of artificial intelligence (AI) chatbot technology, such as Chat Generative Pre-trained Transformer (ChatGPT), may provide an alternative role in graduate medical education in this area. As a proof of concept, the author demonstrates how providing detailed prompts to the AI chatbot can facilitate the design of a realistic clinical scenario, enable active roleplay, and deliver effective feedback to physician trainees. Methods ChatGPT-3.5 language model was utilized to assist in the roleplay of breaking bad news. A detailed input prompt was designed to outline rules of play and grading assessment via a standardized scale. User inputs (physician role), chatbot outputs (patient role) and ChatGPT-generated feedback were recorded. Results ChatGPT set up a realistic training scenario on breaking bad news based on the initial prompt. Active roleplay as a patient in an emergency department setting was accomplished, and clear feedback was provided to the user through the application of the Setting up, Perception, Invitation, Knowledge, Emotions with Empathy, and Strategy or Summary (SPIKES) framework for breaking bad news. Conclusion The novel use of AI chatbot technology to assist educators is abundant with potential. ChatGPT was able to design an appropriate scenario, provide a means for simulated patient-physician roleplay, and deliver real-time feedback to the physician user. Future studies are required to expand use to a targeted group of EM physician trainees and provide best practice guidelines for AI use in graduate medical education.
