Diagnostic Accuracy of a Custom Large Language Model on Rare Pediatric Disease Case Reports.

Accurately diagnosing rare pediatric diseases frequently represent a clinical challenge due to their complex and unusual clinical presentations. Here, we explore the capabilities of three large language models (LLMs), GPT-4, Gemini Pro, and a custom-built LLM (GPT-4 integrated with the Human Phenotype Ontology [GPT-4 HPO]), by evaluating their diagnostic performance on 61 rare pediatric disease case reports. The performance of the LLMs were assessed for accuracy in identifying specific diagnoses, listing the correct diagnosis among a differential list, and broad disease categories. In addition, GPT-4 HPO was tested on 100 general pediatrics case reports previously assessed on other LLMs to further validate its performance. The results indicated that GPT-4 was able to predict the correct diagnosis with a diagnostic accuracy of 13.1%, whereas both GPT-4 HPO and Gemini Pro had diagnostic accuracies of 8.2%. Further, GPT-4 HPO showed an improved performance compared with the other two LLMs in identifying the correct diagnosis among its differential list and the broad disease category. Although these findings underscore the potential of LLMs for diagnostic support, particularly when enhanced with domain-specific ontologies, they also stress the need for further improvement prior to integration into clinical practice.

Orthopedic Surgery Volume Trends During the COVID-19 Pandemic and Postvaccination Era: Implications for Healthcare Planning.

BACKGROUND: The Coronavirus Disease 2019 (COVID-19) pandemic decreased surgical volumes, but prior studies have not investigated recovery through 2022, or analyzed specific procedures or cases of urgency within orthopedic surgery. The aims of this study were to (1) quantify the declines in orthopedic surgery volume during and after the pandemic peak, (2) characterize surgical volume recovery during the postvaccination period, and (3) characterize recovery in the 1-year postvaccine release period. METHODS: We conducted a retrospective cohort study of 27,476 orthopedic surgeries from January 2019 to December 2022 at one urban academic quaternary referral center. We reported trends over the following periods: baseline pre-COVID-19 period (1/6/2019 to 1/4/2020), COVID-19 peak (3/15/2020 to 5/16/2020), post-COVID-19 peak (5/17/2020 to 1/2/2021), postvaccine release (1/3/2021 to 1/1/2022), and 1-year postvaccine release (1/2/2022 to 12/30/2022). Comparisons were performed with 2 sample t-tests. RESULTS: Pre-COVID-19 surgical volume fell by 72% at the COVID-19 peak, especially impacting elective procedures (P < .001) and both hip and knee joint arthroplasty (P < .001) procedures. Nonurgent (P = .024) and urgent or emergency (P = .002) cases also significantly decreased. Postpeak recovery before the vaccine saw volumes rise to 92% of baseline, which further rose to 96% and 94% in 2021 and 2022, respectively. While elective procedures surpassed the baseline in 2022, nonurgent and urgent or emergency surgeries remained low. CONCLUSIONS: The COVID-19 pandemic substantially reduced orthopedic surgical volumes, which have still not fully recovered through 2022, particularly nonelective procedures. The differential recovery within an orthopedic surgery program may result in increased morbidity and can serve to inform department-level operational recovery.

Temporary Declines and Demand Resurgence: Gender-Affirming Surgery Volume and Complication Trends During and After the COVID-19 Pandemic.

BACKGROUND: The COVID-19 pandemic prompted surgical volume reductions due to lockdown measures. This study evaluates COVID-19's impact on gender-affirming surgery (GAS) volume and complications from the pandemic onset through the recovery period. METHODS: The 2019-2021 National Surgical Quality Improvement Program databases were queried for transgender or gender-diverse patients using ICD-10 codes. Five time periods were analyzed: Pre-pandemic, Immediate pre-pandemic and COVID-19 outbreak, Initial COVID-19 peak, Pre-COVID-19 vaccine, and Post-vaccine release. Complications included reoperation, urinary tract infections, and wound complications. Multivariate logistic regressions assessed factors associated with undergoing surgery during the initial COVID-19 peak and experiencing surgical complications. RESULTS: Out of 2,963,230 patients, 4637 underwent GAS between 2019 and 2021. Chest feminizing and masculinizing procedures comprised 60.1% of all GAS. During the initial COVID-19 peak, all GAS surgeries nearly halved, with breast augmentations dropping to 15.3% of pre-pandemic volumes. White patients constituted a significantly higher proportion of GAS patients during the initial COVID-19 peak than in 2019 (74.7% vs. 61.0%, p = 0.014). Post-vaccine, GAS levels surged, exceeding pre-pandemic volumes by 45.5% and initial peak levels by 188.5%. The overall complication rate was 4.9%, and was significantly associated with older age, increased operative time, feminizing and masculinizing genital surgeries, and hysterectomies. The initial COVID-19 peak showed no significant correlations with surgical complications. CONCLUSIONS: GAS volume temporarily decreased during the initial COVID-19 outbreak and has since rebounded and surpassed pre-pandemic levels, corresponding with past-decade trends. Complication risks remained consistent despite the pandemic, though the results highlight potentially significant race-based disparities in GAS access during COVID-19. IMPORTANT POINTS: During the COVID-19 pandemic, public health measures led to severe volume reductions in gender-affirming surgical (GAS) procedures. Since the initial COVID-19 peak, GAS volumes have fully recovered and surpassed pre-pandemic volumes. Surgical complication rates for various GAS procedures were within expected ranges, emphasizing the overall safety of these surgeries. The study's results highlight racial disparities in undergoing GAS during the COVID-19 pandemic, with White patients disproportionately represented among those who had surgery during the COVID-19 lockdown. LEVEL OF EVIDENCE IV: This journal requires that authors assign a level of evidence to each article. For a full description of these evidence-based medicine ratings, please refer to the Table of contents or the online Instructions to Authors www.springer.com/00266 .

Proactive Polypharmacy Management Using Large Language Models: Opportunities to Enhance Geriatric Care.

Polypharmacy remains an important challenge for patients with extensive medical complexity. Given the primary care shortage and the increasing aging population, effective polypharmacy management is crucial to manage the increasing burden of care. The capacity of large language model (LLM)-based artificial intelligence to aid in polypharmacy management has yet to be evaluated. Here, we evaluate ChatGPT's performance in polypharmacy management via its deprescribing decisions in standardized clinical vignettes. We inputted several clinical vignettes originally from a study of general practicioners' deprescribing decisions into ChatGPT 3.5, a publicly available LLM, and evaluated its capacity for yes/no binary deprescribing decisions as well as list-based prompts in which the model was prompted to choose which of several medications to deprescribe. We recorded ChatGPT responses to yes/no binary deprescribing prompts and the number and types of medications deprescribed. In yes/no binary deprescribing decisions, ChatGPT universally recommended deprescribing medications regardless of ADL status in patients with no overlying CVD history; in patients with CVD history, ChatGPT's answers varied by technical replicate. Total number of medications deprescribed ranged from 2.67 to 3.67 (out of 7) and did not vary with CVD status, but increased linearly with severity of ADL impairment. Among medication types, ChatGPT preferentially deprescribed pain medications. ChatGPT's deprescribing decisions vary along the axes of ADL status, CVD history, and medication type, indicating some concordance of internal logic between general practitioners and the model. These results indicate that specifically trained LLMs may provide useful clinical support in polypharmacy management for primary care physicians.

CT utilization in evaluation of skin and soft tissue extremity infections in the ED: Retrospective cohort study.

OBJECTIVE: Skin and soft tissue infections (SSTI) are commonly diagnosed in the emergency department (ED). While most SSTI are diagnosed with patient history and physical exam alone, ED clinicians may order CT imaging when they suspect more serious or complicated infections. Patients who inject drugs are thought to be at higher risk for complications from SSTI and may undergo CT imaging more frequently. The objective of this study is to characterize CT utilization when evaluating for SSTI in ED patients particularly in patients with intravenous drug use (IVDU), the frequency of significant and actionable findings from CT imaging, and its impact on subsequent management and ED operations. METHODS: We performed a retrospective analysis of encounters involving a diagnosis of SSTI in seven EDs across an integrated health system between October 2019 and October 2021. Descriptive statistics were used to assess overall trends, compare CT utilization frequencies, actionable imaging findings, and surgical intervention between patients who inject drugs and those who do not. Multivariable logistic regression was used to analyze patient factors associated with higher likelihood of CT imaging. RESULTS: There were 4833 ED encounters with an ICD-10 diagnosis of SSTI during the study period, of which 6% involved a documented history of IVDU and 30% resulted in admission. 7% (315/4833) of patients received CT imaging, and 22% (70/315) of CTs demonstrated evidence of possible deep space or necrotizing infections. Patients with history of IVDU were more likely than patients without IVDU to receive a CT scan (18% vs 6%), have a CT scan with findings suspicious for deep-space or necrotizing infection (4% vs 1%), and undergo surgical drainage in the operating room within 48 h of arrival (5% vs 2%). Male sex, abnormal vital signs, and history of IVDU were each associated with higher likelihood of CT utilization. Encounters involving CT scans had longer median times to ED disposition than those without CT scans, regardless of whether these encounters resulted in admission (9.0 vs 5.5 h), ED observation (5.5 vs 4.1 h), or discharge (6.8 vs 2.9 h). DISCUSSION: ED clinicians ordered CT scans in 7% of encounters when evaluating for SSTI, most frequently in patients with abnormal vital signs or a history of IV drug use. Patients with a history of IVDU had higher rates of CT findings suspicious for deep space infections or necrotizing infections and higher rates of incision and drainage procedures in the OR. While CT scans significantly extended time spent in the ED for patients, this appeared justified by the high rate of actionable findings found on imaging, particularly for patients with a history of IVDU.

Assessing the Utility of ChatGPT Throughout the Entire Clinical Workflow: Development and Usability Study.

BACKGROUND: Large language model (LLM)-based artificial intelligence chatbots direct the power of large training data sets toward successive, related tasks as opposed to single-ask tasks, for which artificial intelligence already achieves impressive performance. The capacity of LLMs to assist in the full scope of iterative clinical reasoning via successive prompting, in effect acting as artificial physicians, has not yet been evaluated. OBJECTIVE: This study aimed to evaluate ChatGPT's capacity for ongoing clinical decision support via its performance on standardized clinical vignettes. METHODS: We inputted all 36 published clinical vignettes from the Merck Sharpe & Dohme (MSD) Clinical Manual into ChatGPT and compared its accuracy on differential diagnoses, diagnostic testing, final diagnosis, and management based on patient age, gender, and case acuity. Accuracy was measured by the proportion of correct responses to the questions posed within the clinical vignettes tested, as calculated by human scorers. We further conducted linear regression to assess the contributing factors toward ChatGPT's performance on clinical tasks. RESULTS: ChatGPT achieved an overall accuracy of 71.7% (95% CI 69.3%-74.1%) across all 36 clinical vignettes. The LLM demonstrated the highest performance in making a final diagnosis with an accuracy of 76.9% (95% CI 67.8%-86.1%) and the lowest performance in generating an initial differential diagnosis with an accuracy of 60.3% (95% CI 54.2%-66.6%). Compared to answering questions about general medical knowledge, ChatGPT demonstrated inferior performance on differential diagnosis (ß=-15.8%; P<.001) and clinical management (ß=-7.4%; P=.02) question types. CONCLUSIONS: ChatGPT achieves impressive accuracy in clinical decision-making, with increasing strength as it gains more clinical information at its disposal. In particular, ChatGPT demonstrates the greatest accuracy in tasks of final diagnosis as compared to initial diagnosis. Limitations include possible model hallucinations and the unclear composition of ChatGPT's training data set.

Nonaccidental Injury in the Elderly: What Radiologists Need to Know.

Elder abuse may result in serious physical injuries and long-term psychological consequences and can be life threatening. Over the past decade, attention to elder abuse has increased owing to its high prevalence, with one in six people aged 60 years and older experiencing some form of abuse worldwide. Despite this, the detection and reporting rates remain relatively low. While diagnostic imaging is considered critical in detection of child abuse, it is relatively underused in elder abuse. The authors discuss barriers to use of imaging for investigation and diagnosis of elder abuse, including lack of training, comorbidities present in this vulnerable population, and lack of communication among the intra- and interdisciplinary care providers. Moreover, imaging features that should raise clinical concern for elder abuse are reviewed, including certain types of fractures (eg, posterior rib), characteristic soft-tissue and organ injuries (eg, shoulder dislocation), and cases in which the reported mechanism of injury is inconsistent with the imaging findings. As most findings suggesting elder abuse are initially discovered at radiography and CT, the authors focus mainly on use of those modalities. This review also compares and contrasts elder abuse with child abuse. Empowered with knowledge of elderly victims' risk factors, classic perpetrator characteristics, and correlative imaging findings, radiologists should be able to identify potential abuse in elderly patients presenting for medical attention. Future recommendations for research studies and clinical workflow to increase radiologists' awareness of and participation in elder abuse detection are also presented. An invited commentary by Jubanyik and Gettel is available online. Online supplemental material is available for this article. ©RSNA, 2022.

Impact of iodinated contrast allergies on emergency department operations.

OBJECTIVES: Adverse reactions to intravenous (IV) iodinated contrast media are classified by the American College of Radiology (ACR) Manual on Contrast Media as either allergic-like (ALR) or physiologic (PR). Premedication may be beneficial for patients who have prior documented mild or moderate ALR. We sought to perform a retrospective analysis of patients who received computed tomography (CT) imaging in our emergency department (ED) to establish whether listing of an iodinated contrast media allergy results in a delay in care, increases the use of non-contrast studies, and to quantify the incidence of listing iodinated contrast allergies which do not necessitate premedication. METHODS: We performed a retrospective analysis of CT scans performed in our academic medical center ED during a 6-month period. There were 12,737 unique patients of whom 454 patients had a listed iodinated contrast allergy. Of these, 106 received IV contrast and were categorized as to whether premedication was necessary. Descriptive statistics were used to evaluate patient demographics, clinical characteristics, and operational outcomes. A multivariate linear regression model was used to predict time from order to start (OTS time) of CT imaging while controlling for co-variates. RESULTS: Non-allergic patients underwent contrast-enhanced CT imaging at a significantly higher rate than allergic patients (45.9% vs. 23.3%, p < 0.01). The OTS time for allergic patients who underwent contrast-enhanced CT imaging was 360 min and significantly longer than the OTS time for non-allergic patients who underwent contrast-enhanced CT imaging (118 min, p < 0.001). Of the 106 allergic patients who underwent contrast-enhanced CT imaging, 27 (25.5%) did not meet ACR criteria for necessitating premedication. The average OTS time for these 27 patients was 296 min, significantly longer than the OTS for non-allergic patients (118 min, p < 0.01) and did not differ from the OTS time for the 79 patients who did meet premedication criteria (382 min, p = 0.23). A multivariate linear regression showed that OTS time was significantly longer if a contrast allergy was present (p < 0.001). CONCLUSION: A chart-documented iodinated contrast allergy resulted in a significant increase in time to obtain a contrast-enhanced CT study. This delay persisted among patients who did not meet ACR criteria for premedication. Appropriately deferring premedication could potentially reduce the ED length-of-stay by over 4 h for these patients.

"All-in-one" window/level whole-body computed tomography scan - A faster way to evaluate trauma cases.

PURPOSE: To investigate the accuracy and total assessment time (TAT) of the "All-in-one" (AIO)-window/level setting for whole-body computed tomography (CT) image compared to multiple tissue-specific window/level settings conventionally used for detection of traumatic injuries. METHOD: Contrast-enhanced chest, abdomen, and pelvic CT scans of 50 patients who presented to our emergency department (ED) for major trauma were retrospectively selected. In a simulation of a "wet read" performed at the CT scanner console, 6 readers with different levels of experience had up to 3 min to describe any traumatic finding identified on the CTs. The readers reviewed each patient in two different sessions separated by a washout period to suppress any recall bias from one session to the next. Each scan was reviewed once using the AIO-window/level setting and another time using the conventional bone, lung, and soft tissue window/level display settings, in a randomized order. The CT reports were used as reference standard. RESULTS: Overall, there was no statistically significant difference in the assessment accuracy of the review based on the AIO or the conventional window/level settings (0.89 ± 0.09 vs 0.90 ± 0.08). Using the AIO-window/level settings, TAT was 14.3 s faster when compared with the conventional window/level settings (2.33 ± 0.63 vs 2.57 ± 0.51 min; p < 0.001). CONCLUSIONS: In a time-delimited image review, similar diagnostic accuracy was reached faster using the AIO vs the conventional window/level settings. When providing a "wet read" at the CT console, the ability to identify traumatic injury using a single AIO-window/level may help expedite patient management.

Automated tracking of emergency department abdominal CT findings during the COVID-19 pandemic using natural language processing.

PURPOSE: During the COVID-19 pandemic, emergency department (ED) volumes have fluctuated. We hypothesized that natural language processing (NLP) models could quantify changes in detection of acute abdominal pathology (acute appendicitis (AA), acute diverticulitis (AD), or bowel obstruction (BO)) on CT reports. METHODS: This retrospective study included 22,182 radiology reports from CT abdomen/pelvis studies performed at an urban ED between January 1, 2018 to August 14, 2020. Using a subset of 2448 manually annotated reports, we trained random forest NLP models to classify the presence of AA, AD, and BO in report impressions. Performance was assessed using 5-fold cross validation. The NLP classifiers were then applied to all reports. RESULTS: The NLP classifiers for AA, AD, and BO demonstrated cross-validation classification accuracies between 0.97 and 0.99 and F1-scores between 0.86 and 0.91. When applied to all CT reports, the estimated numbers of AA, AD, and BO cases decreased 43-57% in April 2020 (first regional peak of COVID-19 cases) compared to 2018-2019. However, the number of abdominal pathologies detected rebounded in May-July 2020, with increases above historical averages for AD. The proportions of CT studies with these pathologies did not significantly increase during the pandemic period. CONCLUSION: Dramatic decreases in numbers of acute abdominal pathologies detected by ED CT studies were observed early on during the COVID-19 pandemic, though these numbers rapidly rebounded. The proportions of CT cases with these pathologies did not increase, which suggests patients deferred care during the first pandemic peak. NLP can help automatically track findings in ED radiology reporting.

Characterization of an Open-Access Medical News Platform's Readership During the COVID-19 Pandemic: Retrospective Observational Study.

BACKGROUND: There are many alternatives to direct journal access, such as podcasts, blogs, and news sites, that allow physicians and the general public to stay up to date with medical literature. However, there is a scarcity of literature that investigates the readership characteristics of open-access medical news sites and how these characteristics may have shifted during the COVID-19 pandemic. OBJECTIVE: This study aimed to assess readership and survey data to characterize open-access medical news readership trends related to the COVID-19 pandemic and overall readership trends regarding pandemic-related information delivery. METHODS: Anonymous, aggregate readership data were obtained from 2 Minute Medicine, an open-access, physician-run medical news organization that has published over 8000 original, physician-written texts and visual summaries of new medical research since 2013. In this retrospective observational study, the average number of article views, number of actions (defined as the sum of the number of views, shares, and outbound link clicks), read times, and bounce rates (probability of leaving a page in <30 s) were compared between COVID-19 articles published from January 1 to May 31, 2020 (n=40) and non-COVID-19 articles (n=145) published in the same time period. A voluntary survey was also sent to subscribed 2 Minute Medicine readers to further characterize readership demographics and preferences, which were scored on a Likert scale. RESULTS: COVID-19 articles had a significantly higher median number of views than non-COVID-19 articles (296 vs 110; U=748.5; P<.001). There were no significant differences in average read times (P=.12) or bounce rates (P=.12). Non-COVID-19 articles had a higher median number of actions than COVID-19 articles (2.9 vs 2.5; U=2070.5; P=.02). On a Likert scale of 1 (strongly disagree) to 5 (strongly agree), our survey data revealed that 65.5% (78/119) of readers agreed or strongly agreed that they preferred staying up to date with emerging literature about COVID-19 by using sources such as 2 Minute Medicine instead of journals. A greater proportion of survey respondents also indicated that open-access news sources were one of their primary sources for staying informed (86/120, 71.7%) compared to the proportion who preferred direct journal article access (61/120, 50.8%). The proportion of readers indicating they were reading one or less full-length medical studies a month were lower following introduction to 2 Minute Medicine compared to prior (21/120, 17.5% vs 38/120, 31.6%; P=.005). CONCLUSIONS: The readership significantly increased for one open-access medical literature platform during the pandemic. This reinforces the idea that open-access, physician-written sources of medical news represent an important alternative to direct journal access for readers who want to stay up to date with medical literature.

Turning around cancer: Oncology imaging and implications for emergency department radiology workflow.

PURPOSE: Oncologic imaging in the emergency department (ED) is frequently encountered, including non-acute scans known as "metastatic workups" or "staging" (referred to as "cancer staging computed tomography (CT) exams"). This study examines the impact of oncologic staging CT exams on ED imaging turnaround time (TAT), defined as the time from the end of the CT exam to a final signed radiologist report, as well as order to scan completion time. METHODS: A retrospective review was conducted of all adult patients presenting to an urban, quaternary academic medical center ED from February 2016 to September 2017, who had CT imaging ordered, performed, and interpreted in the ED imaging department. CT exams containing institution-specific cancer descriptors were included. After excluding all acute exams, cancer staging CT exams were compared to a matched cohort of non-oncologic ED CT exams to evaluate median TAT and order to scan completion time using a log transformed multivariable linear regression. RESULTS: Adjusting for age and CT body part, cancer staging CT exams were associated with an independently statistically significant increased median log TAT compared to non-oncologic ED CT exams (114.5 min [IQR 112] versus 69 min [IQR 67], respectively, p < .0001) and an independently statistically significant increased median log initial order to scan completion time (166 min [IQR: 89] vs 119 min [IQR: 93], p < .0001). CONCLUSION: Oncology patients receiving non-acute metastatic workup scans in the ED have a significantly longer TAT compared to non-oncologic ED CT exams as well as longer order to scan completion times.

Racial, ethnic, and sex bias in large language model opioid recommendations for pain management.

ABSTRACT: Understanding how large language model (LLM) recommendations vary with patient race/ethnicity provides insight into how LLMs may counter or compound bias in opioid prescription. Forty real-world patient cases were sourced from the MIMIC-IV Note dataset with chief complaints of abdominal pain, back pain, headache, or musculoskeletal pain and amended to include all combinations of race/ethnicity and sex. Large language models were instructed to provide a subjective pain rating and comprehensive pain management recommendation. Univariate analyses were performed to evaluate the association between racial/ethnic group or sex and the specified outcome measures-subjective pain rating, opioid name, order, and dosage recommendations-suggested by 2 LLMs (GPT-4 and Gemini). Four hundred eighty real-world patient cases were provided to each LLM, and responses included pharmacologic and nonpharmacologic interventions. Tramadol was the most recommended weak opioid in 55.4% of cases, while oxycodone was the most frequently recommended strong opioid in 33.2% of cases. Relative to GPT-4, Gemini was more likely to rate a patient's pain as "severe" (OR: 0.57 95% CI: [0.54, 0.60]; P < 0.001), recommend strong opioids (OR: 2.05 95% CI: [1.59, 2.66]; P < 0.001), and recommend opioids later (OR: 1.41 95% CI: [1.22, 1.62]; P < 0.001). Race/ethnicity and sex did not influence LLM recommendations. This study suggests that LLMs do not preferentially recommend opioid treatment for one group over another. Given that prior research shows race-based disparities in pain perception and treatment by healthcare providers, LLMs may offer physicians a helpful tool to guide their pain management and ensure equitable treatment across patient groups.

Pilot Study of Large Language Models as an Age-Appropriate Explanatory Tool for Chronic Pediatric Conditions.

There exists a gap in existing patient education resources for children with chronic conditions. This pilot study assesses large language models' (LLMs) capacity to deliver developmentally appropriate explanations of chronic conditions to pediatric patients. Two commonly used LLMs generated responses that accurately, appropriately, and effectively communicate complex medical information, making them a potentially valuable tool for enhancing patient understanding and engagement in clinical settings.

Transforming Health Care Landscapes: The Lever of Radiology Research and Innovation on Emerging Markets Poised for Aggressive Growth.

Advances in radiology are crucial not only to the future of the field but to medicine as a whole. Here, we present three emerging areas of medicine that are poised to change how health care is delivered-hospital at home, artificial intelligence, and precision medicine-and illustrate how advances in radiological tools and technologies are helping to fuel the growth of these markets in the United States and across the globe.

Trends in non-focal neurological chief complaints and CT angiography utilization among adults in the emergency department.

Prudent imaging use is essential for cost reduction and efficient patient triage. Recent efforts have focused on head and neck CTA in patients with emergent concerns for non-focal neurological complaints, but have failed to demonstrate whether increases in utilization have resulted in better care. The objective of this study was to examine trends in head and neck CTA ordering and determine whether a correlation exists between imaging utilization and positivity rates. This is a single-center retrospective observational study at a quaternary referral center. This study includes patients presenting with headache and/or dizziness to the emergency department between January 2017 and December 2021. Patients who received a head and neck CTA were compared to those who did not. The main outcomes included annual head and neck CTA utilization and positivity rates, defined as the percent of scans with attributable acute pathologies. Among 24,892 emergency department visits, 2264 (9.1%) underwent head and neck CTA imaging. The percentage of patients who received a scan over the study period increased from 7.89% (422/5351) in 2017 to 13.24% (662/5001) in 2021, representing a 67.4% increase from baseline (OR, 1.14; 95% CI 1.11-1.18; P < .001). The positivity rate, or the percentage of scans ordered that revealed attributable acute pathology, dropped from 16.8% (71/422) in 2017 to 10.4% (69/662) in 2021 (OR, 0.86; 95% CI 0.79-0.94; P = .001), a 38% reduction in positive examinations. Throughout the study period, there was a 67.4% increase in head and neck CTA ordering with a concomitant 38.1% decrease in positivity rate.

Implementation of a Survey Spine MR Imaging Protocol for Cord Compression in the Emergency Department: Experience at a Level 1 Trauma Center.

BACKGROUND AND PURPOSE: Imaging stewardship in the emergency department (ED) is vital in ensuring patients receive optimized care. While suspected cord compression (CC) is a frequent indication for total spine MR imaging in the ED, the incidence of CC is low. Recently, our level 1 trauma center introduced a survey spine MR imaging protocol to evaluate for suspected CC while reducing examination time to avoid imaging overutilization. This study aims to evaluate the time savings, frequency of ordering patterns of the survey, and the symptoms and outcomes of patients undergoing the survey. MATERIALS AND METHODS: This retrospective study examined patients who received a survey spine MR imaging in the ED at our institution between 2018 and 2022. All examinations were performed on a 1.5T GE Healthcare scanner by using our institutional CC survey protocol, which includes sagittal T2WI and STIR sequences through the cervical, thoracic, and lumbar spine. Examinations were read by a blinded, board-certified neuroradiologist. RESULTS: A total of 2002 patients received a survey spine MR imaging protocol during the study period. Of these patients, 845 (42.2%, mean age 57 ± 19 years, 45% women) received survey spine MR imaging examinations for the suspicion of CC, and 120 patients (14.2% positivity rate) had radiographic CC. The survey spine MR imaging averaged 5 minutes and 50 seconds (79% faster than routine MR imaging). On multivariate analysis, trauma, back pain, lower extremity weakness, urinary or bowel incontinence, numbness, ataxia, and hyperreflexia were each independently associated with CC. Of the 120 patients with CC, 71 underwent emergent surgery, 20 underwent nonemergent surgery, and 29 were managed medically. CONCLUSIONS: The survey spine protocol was positive for CC in 14% of patients in our cohort and acquired at a 79% faster rate compared with routine total spine. Understanding the positivity rate of CC, the clinical symptoms that are most associated with CC, and the subsequent care management for patients presenting with suspected cord compression who received the survey spine MR imaging may better inform the broad adoption and subsequent utilization of survey imaging protocols in emergency settings to increase throughput, improve allocation of resources, and provide efficient care for patients with suspected CC.

A Novel Design-Thinking, Hospital Innovation Core Certificate Curriculum for Radiologists and Trainees: Creation, Implementation, and Multiyear Results.

RATIONALE AND OBJECTIVES: Innovation is a crucial skill for physicians and researchers, yet traditional medical education does not provide instruction or experience to cultivate an innovative mindset. This study evaluates the effectiveness of a novel course implemented in an academic radiology department training program over a 5-year period designed to educate future radiologists on the fundamentals of medical innovation. MATERIALS AND METHODS: A pre- and post-course survey and examination were administered to residents who participated in the innovation course (MESH Core) from 2018 to 2022. Respondents were first evaluated on their subjective comfort level, understanding, and beliefs on innovation-related topics using a 5-point Likert-scale survey. Respondents were also administered a 21-question multiple-choice exam to test their objective knowledge of innovation-related topics. RESULTS: Thirty-eight residents participated in the survey (response rate 95%). Resident understanding, comfort and belief regarding innovation-related topics improved significantly (P < .0001) on all nine Likert-scale questions after the course. After the course, a significant majority of residents either agreed or strongly agreed that technological innovation should be a core competency for the residency curriculum, and that a workshop to prototype their ideas would be beneficial. Performance on the course exam showed significant improvement (48% vs 86%, P < .0001). The overall course experience was rated 5 out of 5 by all participants. CONCLUSION: MESH Core demonstrates long-term success in educating future radiologists on the basic concepts of medical technological innovation. Years later, residents used the knowledge and experience gained from MESH Core to successfully pursue their own inventions and innovative projects. This innovation model may serve as an approach for other institutions to implement training in this domain.

Drug delivery interfaces in the 21st century: from science fiction ideas to viable technologies.

Early science fiction envisioned the future of drug delivery as targeted micrometer-scale submarines and "cyborg" body parts. Here we describe the progression of the field toward technologies that are now beginning to capture aspects of this early vision. Specifically, we focus on the two most prominent types of systems in drug delivery: the intravascular micro/nano drug carriers for delivery to the site of pathology and drug-loaded implantable devices that facilitate release with the predefined kinetics or in response to a specific cue. We discuss the unmet clinical needs that inspire these designs, the physiological factors that pose difficult challenges for their realization, and viable technologies that promise robust solutions. We also offer a perspective on where drug delivery may be in the next 50 years based on expected advances in material engineering and in the context of future diagnostics.