Leveraging artificial intelligence to reduce diagnostic errors in emergency medicine: Challenges, opportunities, and future directions.

Diagnostic errors in health care pose significant risks to patient safety and are disturbingly common. In the emergency department (ED), the chaotic and high-pressure environment increases the likelihood of these errors, as emergency clinicians must make rapid decisions with limited information, often under cognitive overload. Artificial intelligence (AI) offers promising solutions to improve diagnostic errors in three key areas: information gathering, clinical decision support (CDS), and feedback through quality improvement.

Identifying signs and symptoms of urinary tract infection from emergency department clinical notes using large language models.

Background: Natural language processing (NLP) tools including recently developed large language models (LLMs) have myriad potential applications in medical care and research, including the efficient labeling and classification of unstructured text such as electronic health record (EHR) notes. This opens the door to large-scale projects that rely on variables that are not typically recorded in a structured form, such as patient signs and symptoms.

Objectives: This study is designed to acquaint the emergency medicine research community with the foundational elements of NLP, highlighting essential terminology, annotation methodologies, and the intricacies involved in training and evaluating NLP models.

Automated HEART score determination via ChatGPT: Honing a framework for iterative prompt development.

Objectives: This study presents a design framework to enhance the accuracy by which large language models (LLMs), like ChatGPT can extract insights from clinical notes. We highlight this framework via prompt refinement for the automated determination of HEART (History, ECG, Age, Risk factors, Troponin risk algorithm) scores in chest pain evaluation.

Methods: We developed a pipeline for LLM prompt testing, employing stochastic repeat testing and quantifying response errors relative to physician assessment.

PROSER: A Web-Based Peripheral Blood Smear Interpretation Support Tool Utilizing Electronic Health Record Data.

Objectives: Peripheral blood smear (PBS) interpretation represents a cornerstone of pathology practice and resident training but has remained largely static for decades. Here, we describe a novel PBS interpretation support tool.

Methods: In a mixed-methods quality improvement study, a web-based clinical decision support (CDS) tool to assist pathologists in PBS interpretation, PROSER, was deployed in an academic hospital over a 2-month period in 2022.

Development and Implementation of a Standard Format for Clinical Laboratory Test Results.

Objectives: Surprisingly, laboratory results, the principal output of clinical laboratories, are not standardized. Thus, laboratories frequently report results with identical meaning in different formats. For example, laboratories report a positive pregnancy test as "+," "P," or "Positive.

Cluster Analysis of Primary Care Physician Phenotypes for Electronic Health Record Use: Retrospective Cohort Study.

Background: Electronic health records (EHRs) have become ubiquitous in US office-based physician practices. However, the different ways in which users engage with EHRs remain poorly characterized.

Objective: The aim of this study is to explore EHR use phenotypes among ambulatory care physicians.

Teaching medical students about cost-effectiveness.

Background: Rising and burdensome health care costs have driven interest in the practice of high-value care (HVC) and have inspired calls for increased HVC training across all levels of medical education, including among undergraduate medical students.

Context: Classroom-based HVC curricula targeted to medical students have not been previously described in the medical literature.

Innovation: We developed and evaluated a workshop comprising a lecture, a small-group exercise and a group discussion to instruct medical students on interpreting cost-effectiveness analyses (CEA), applying CEA to patient care and discussing the cost of care with patients.

The Student Curriculum Review Team: How we catalyze curricular changes through a student-centered approach.

Student feedback is a valuable asset in curriculum evaluation and improvement, but many institutions have faced challenges implementing it in a meaningful way. In this article, we report the rationale, process and impact of the Student Curriculum Review Team (SCRT), a student-led and faculty-supported organization at the Johns Hopkins University School of Medicine. SCRT's evaluation of each pre-clinical course is composed of a comprehensive three-step process: a review of course evaluation data, a Town Hall Meeting and online survey to generate and assess potential solutions, and a thoughtful discussion with course directors.

HCV and HBV testing acceptability and knowledge among urban emergency department patients and pharmacy clients.

Objective: Hepatitis C and hepatitis B are public health problems in the United States and remain largely undiagnosed. In response to the availability of rapid, point of care hepatitis tests, we assessed hepatitis knowledge and acceptability of hepatitis testing during an emergency department (ED) or pharmacy visit.

Methods: From June 2010 to May 2011, an anonymous prospective survey was administered to a convenience sample of New York City ED patients and pharmacy clients.