Journal of the American College of Radiology : JACR最新文献

Objective: The Physician Sunshine Act of 2010 aimed to increase public awareness of physician-industry relationships. Our objective was to evaluate whether there is an association between scholarly impact and industry funding among academic interventional radiologists.

Methods: A database from a prior study with our group was used in which we had investigated H-indices among US interventional radiologists; academic rank, gender, institution, and geographic location were obtained. The Scopus database was queried to determine all physicians' H-index. The CMS Open Payments database was used to determine industry payments from 2015 to 2021 for each interventional radiologist.

Results: H-index and professor rank positively and significantly correlated with industrial funding (H-index coefficient = $6,977, P < .001 and professor rank coefficient = $183,902, P = .003). Industry funding was found to be significantly different between all ranks. Among 830 academic interventional radiologists, the mean industrial funding of male physicians was $130,034, which was significantly higher than female physicians' $28,166 (P = .00013). By academic rank, male primary investigators of associate professor and unranked position had higher industrial funding than female primary investigators (Wilcoxon test, P = .029 and P= .039, respectively). Professor and assistant professor ranks had no significant difference in industrial funding between male and female physicians (Wilcoxon's test, P = .080 and P = .053, respectively).

Conclusion: Scholarly activity as defined by the H-index and academic rank seem to have a positive association with industry funding of academic interventional radiologists.

Objective: Develop structured, quality improvement interventions to achieve a 15%-point reduction in MRIs performed under sedation or general anesthesia (GA) delayed more than 15 min within a 6-month period.

Methods: A prospective audit of MRIs under sedation or GA from January 2022 to June 2023 was conducted. A multidisciplinary team performed process mapping and root cause analysis for delays. Interventions were developed and implemented over four Plan, Do, Study, Act (PDSA) cycles, targeting workflow standardization, preadmission patient counseling, reinforcing adherence to scheduled scan times and written consent respectively. Delay times (compared with Kruskal-Wallis and Dunn's tests), delays more than 15 min and delays of 60 min or more at baseline and after each PDSA cycle were recorded.

Results: In all, 627 MRIs under sedation or GA were analyzed, comprising 443 at baseline and 184 postimplementation. Of the 627, 556 (88.7%) scans were performed under sedation, 22 (3.5%) under monitored anesthesia care, and 49 (7.8%) under GA. At baseline, 71.6% (317 of 443) scans were delayed over 15 min and 28.2% (125 of 443) scans by 60 min or more, with a median delay of 30 min. Postimplementation, there was a 34.7%-point reduction in scans delayed more than 15 min, a 17.5%-point reduction in scans delayed by 60 min or more, and a reduction in median delay time by 15 min (P < .001).

Discussion: Structured interventions significantly reduced delays in MRIs under sedation and GA, potentially improving outcomes for both patients and providers. Key factors included a diversity of perspectives in the study team, continued stakeholder engagement and structured quality improvement tools including PDSA cycles.

Objective: To explore the capabilities of Chat Generative Pre-trained Transformer (ChatGPT) for the purpose of simplifying and translating radiology reports into Spanish, Hindi, and Russian languages, with comparisons to its performance in simplifying to the English language.

Methods: Fifty deidentified abdomen-pelvis CT reports were fed to ChatGPT (4.0), instructing it to simplify and translate the report. The processed reports were rated on factual correctness (category 1), potential harmful errors (category 2), completeness (category 3), and explanation of medical terms (category 4). The translated versions were also rated on the quality of translation (category 5). The scores in each category were compared between the translated versions and each translated version was compared with the English version in the first four categories. The original reports and the simplified English reports were rated on the Flesch Reading Ease Score and the Flesch Kincaid Grade Level.

Results: The Spanish translation outperformed the Hindi and Russian version significantly in categories 1 and 3 (P < .05). All translated versions performed significantly worse compared with the English version in category 4 (P < .001). Notably, the Hindi translated version performed significantly worse in all four categories (P < .05). The Russian translated version was also significantly worse in category 3 (P < .05). In the first three categories, the Spanish translation, and in the first two categories, the Russian translation demonstrated no statistically significant difference from the English version. No statistically significant difference was observed in the Flesch Reading Ease Score and Flesch Kincaid Grade Level of the simplified English reports. Typographical errors in the original reports negatively affected the translation.

Conclusion: ChatGPT demonstrates potential ability in translating reports and communicating pertinent clinical information with limited errors. More training and tailoring are required for languages that are not as commonly used in medical literature. Large language models can be used for translating and simplifying radiology reports, potentially improving access to health care and helping reduce health care costs.