Tan, Nelly

We examined outcomes of a quality improvement (QI) culture implemented through Mayo Clinic Quality Academy by retrospectively reviewing data from QI projects credited from 2019 to 2024. Data analyzed included roles, member count, region, project details, priorities, financial outcomes, methodologies, and lessons learned. During the study period, Mayo Clinic Quality Academy credited 1106 projects involving 10 063 members (median, 8 members per project). Physicians participated in 52.4% of projects, allied health professionals in 83.6%, and nurses in 55.2%. Among all projects, teamwork (54.0%) and efficiency (52.8%) were the most common organizational priorities; among National Academy of Medicine priorities, efficiency was the most common (32.2%), followed by safety (20.7%) and patient-centeredness (18.9%). Model for improvement was the most prevalent QI methodology (29.2%). Most participants (93%) believed the QI project changed the way they work. Cultivating a QI culture influences health care delivery by emphasizing efficiency, teamwork, operational performance, patient outcomes, satisfaction, and financial benefits.

Abstract Background Radiologist burnout affects approximately 40% of US radiologists. Large language models (LLMs) may improve workflow efficiency, but real-world implementation data are limited. Objective To evaluate LLM-assisted workflow impact on radiologist efficiency using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. Design, Setting, and Participants: HIPAA-compliant, IRB-approved retrospective cohort study of a single fellowship-trained abdominal radiologist at Mayo Clinic Arizona. We compared baseline (January–April 2024) and post-implementation (December 2025–February 2026) periods. A custom generative pre-trained transformer was developed using ChatGPT Enterprise with disease-specific templates. Main Outcome Measures: Inter-study interval time (proxy for interpretation time) compared using Wilcoxon rank-sum tests with Bonferroni correction (α = 0.0125). UTAUT constructs assessed: performance expectancy (efficiency), effort expectancy (training burden), facilitating conditions (infrastructure), and behavioral intention (satisfaction). Results We analyzed 609 studies (495 CT, 114 MRI). LLM assistance significantly reduced inter-study intervals for outpatient CT with contrast (23.0 vs 13.0 minutes; difference 10 minutes; p = 0.0021) and without contrast (18.5 vs 7.0 minutes; difference 11.5 minutes; p = 0.0017). No improvement occurred for MRI with contrast (14.0 vs 16.0 minutes; p = 0.2808) or without contrast (14.0 vs 7.0 minutes; p = 0.0889). The radiologist reported improved work-life balance for CT but neutral satisfaction for complex MRI templates. Training required 10 hours over 5 days. Conclusions LLM-assisted workflow reduced interpretation times for standardized CT studies but not heterogeneous MRI examinations, supporting UTAUT's emphasis on performance expectancy and task–technology fit as adoption drivers. Efficiency gains may reduce documentation burden when tools align with task complexity.

An understanding of regions of endemicity and the spectrum of organ system involvement can improve recognition of the imaging features of coccidioidomycosis, which can overlap with those of other diseases.

This cohort study examines trends in patient access of radiology imaging results after implementation of the 21st Century Cures Act in different clinical settings.

This review examines the applications and challenges of large language models (LLMs), like OpenAI’s ChatGPT, in radiology research. ChatGPT can assist radiology researchers in generating new ideas, finding and summarizing research papers, designing studies, analyzing data, and facilitating manuscript writing. LLMs are powerful tools with numerous applications in radiology research. However, users should be mindful of potential pitfalls, such as producing incorrect or biased outputs and inconsistent responses, along with ethical and privacy concerns. We discuss approaches to optimize models and address these issues, including prompting techniques like chain-of-thought prompting, retrieval-augmented generation, and fine-tuning. For researchers, prompt engineering can be particularly effective. This review seeks to demonstrate how researchers can utilize ChatGPT for radiology research while offering strategies to mitigate associated risks. We aim to help researchers harness these potent tools to safely boost their productivity.

Frequently overlooked yet clinically significant, the urachus can exhibit a diverse range of pathologic conditions, which can pose a diagnostic challenge at imaging for the radiologist and which require a thorough understanding of urachal embryogenesis.

Education-level or socioeconomic background of patients may dictate their ability to understand medical jargon. Inability to understand primary findings from a radiology report may lead to unnecessary anxiety among patients or missed follow up. We aim to meet this challenge by developing a patient-sensitive summarization model for radiology reports. We selected computed tomography (CT) exams of chest as a use-case and collected 7,000 studies from Mayo Clinic. Summarization model was built on top of the T5 large language model (LLM) as our experiments indicated that its text-to-text transfer architecture was suited for abstractive text summarization, resulting in a model with 0.77B trainable parameters. Noisy ground truth for model training was collected by prompting LLaMA-13B model. We recruited experts (board-certified radiologists) and laymen to manually evaluate model-generated summaries generated by model. Our model rarely missed information as marked by majority opinion of radiologists. Laymen indicated 63% improvement in their understanding by reading model-generated layman summaries. Comparison with zero-shot performance of ChatGPT indicated that the proposed model reduced the rate of hallucination by half and rate of missing important information by fivefold. The proposed model can generate reliable summaries for radiology reports understandable by patients with vastly different levels of medical knowledge.