ABSTRACT
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 .
ABSTRACT
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.
ABSTRACT
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.
Subject(s)
Cardiovascular Diseases , Deprescriptions , General Practitioners , Humans , Aged , Polypharmacy , Artificial IntelligenceABSTRACT
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.
ABSTRACT
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.
Subject(s)
COVID-19 , Orthopedic Procedures , Humans , COVID-19/epidemiology , COVID-19/prevention & control , Retrospective Studies , Orthopedic Procedures/statistics & numerical data , Orthopedic Procedures/trends , Male , Female , Health Planning , COVID-19 Vaccines/administration & dosage , Pandemics , Middle Aged , Elective Surgical Procedures/statistics & numerical data , Elective Surgical Procedures/trends , SARS-CoV-2 , Aged , Arthroplasty, Replacement, Knee/statistics & numerical data , Arthroplasty, Replacement, Knee/trendsSubject(s)
Fever , Headache , Travel-Related Illness , Adult , Humans , Male , Fever/etiology , Headache/etiology , Travel , InternationalityABSTRACT
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.
Subject(s)
Education, Medical, Graduate , Internship and Residency , Humans , Education, Medical, Graduate/methods , Clinical Competence , Curriculum , Radiologists , HospitalsABSTRACT
Percutaneous drains have provided a minimally invasive way to treat a wide range of disorders from abscess drainage to enteral feeding solutions to treating hydronephrosis. These drains suffer from a high rate of dislodgement of up to 30% resulting in emergency room visits, repeat hospitalizations, and catheter repositioning/replacement procedures, which incur significant morbidity and mortality. Using ex vivo and in vivo models, a force body diagram was utilized to determine the forces experienced by a drainage catheter during dislodgement events, and the individual components which contribute to drainage catheter securement were empirically collected. Prototypes of a skin level catheter securement and valved quick release system were then developed. The system was inspired by capstans used in boating for increasing friction of a line around a central spool and quick release mechanisms used in electronics such as the Apple MagSafe computer charger. The device was tested in a porcine suprapubic model, which demonstrated the effectiveness of the device to prevent drain dislodgement. The prototype demonstrated that the miniaturized versions of technologies used in boating and electronics industries were able to meet the needs of preventing dislodgement of patient drainage catheters.
Subject(s)
Catheters , Device Removal , Humans , Animals , Swine , Drainage , Electric Power Supplies , ElectronicsABSTRACT
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.
Subject(s)
Artificial Intelligence , Humans , Clinical Decision-Making , Organizations , Workflow , User-Centered DesignABSTRACT
Imaging is a central determinant of health outcomes, and radiologic disparities can cascade throughout a patient's illness course. Innovative efforts in radiology are constant, but innovation that is driven by short-term profit-making incentives without explicit regard for principles of justice can lead to exclusion of the vulnerable from potential benefits and widening of inequities. Accordingly, we must consider the ways in which the field of radiology can shape innovative efforts to ensure that innovation ameliorates injustice instead of exacerbating it. The authors propose a distinction between approaches to innovation that prioritize justice and those that do not. The authors argue that the field's institutional incentives should be adjusted to prioritize forms of innovation that are likely to ameliorate imaging inequities, and they provide examples of initial steps that can be taken to make these adjustments. The authors propose the term justice-oriented innovation as a way of describing forms of innovation that are motivated by reducing injustice and can reasonably be expected to do so.
Subject(s)
Radiology , Social Justice , HumansABSTRACT
OBJECTIVE: Despite rising popularity and performance, studies evaluating the use of large language models for clinical decision support are lacking. Here, we evaluate ChatGPT (Generative Pre-trained Transformer)-3.5 and GPT-4's (OpenAI, San Francisco, California) capacity for clinical decision support in radiology via the identification of appropriate imaging services for two important clinical presentations: breast cancer screening and breast pain. METHODS: We compared ChatGPT's responses to the ACR Appropriateness Criteria for breast pain and breast cancer screening. Our prompt formats included an open-ended (OE) and a select all that apply (SATA) format. Scoring criteria evaluated whether proposed imaging modalities were in accordance with ACR guidelines. Three replicate entries were conducted for each prompt, and the average of these was used to determine final scores. RESULTS: Both ChatGPT-3.5 and ChatGPT-4 achieved an average OE score of 1.830 (out of 2) for breast cancer screening prompts. ChatGPT-3.5 achieved a SATA average percentage correct of 88.9%, compared with ChatGPT-4's average percentage correct of 98.4% for breast cancer screening prompts. For breast pain, ChatGPT-3.5 achieved an average OE score of 1.125 (out of 2) and a SATA average percentage correct of 58.3%, as compared with an average OE score of 1.666 (out of 2) and a SATA average percentage correct of 77.7%. DISCUSSION: Our results demonstrate the eventual feasibility of using large language models like ChatGPT for radiologic decision making, with the potential to improve clinical workflow and responsible use of radiology services. More use cases and greater accuracy are necessary to evaluate and implement such tools.
Subject(s)
Breast Neoplasms , Mastodynia , Radiology , Humans , Female , Breast Neoplasms/diagnostic imaging , Decision MakingABSTRACT
OBJECTIVES: Current state-of-the-art natural language processing (NLP) techniques use transformer deep-learning architectures, which depend on large training datasets. We hypothesized that traditional NLP techniques may outperform transformers for smaller radiology report datasets. METHODS: We compared the performance of BioBERT, a deep-learning-based transformer model pre-trained on biomedical text, and three traditional machine-learning models (gradient boosted tree, random forest, and logistic regression) on seven classification tasks given free-text radiology reports. Tasks included detection of appendicitis, diverticulitis, bowel obstruction, and enteritis/colitis on abdomen/pelvis CT reports, ischemic infarct on brain CT/MRI reports, and medial and lateral meniscus tears on knee MRI reports (7,204 total annotated reports). The performance of NLP models on held-out test sets was compared after training using the full training set, and 2.5%, 10%, 25%, 50%, and 75% random subsets of the training data. RESULTS: In all tested classification tasks, BioBERT performed poorly at smaller training sample sizes compared to non-deep-learning NLP models. Specifically, BioBERT required training on approximately 1,000 reports to perform similarly or better than non-deep-learning models. At around 1,250 to 1,500 training samples, the testing performance for all models began to plateau, where additional training data yielded minimal performance gain. CONCLUSIONS: With larger sample sizes, transformer NLP models achieved superior performance in radiology report binary classification tasks. However, with smaller sizes (<1000) and more imbalanced training data, traditional NLP techniques performed better. ADVANCES IN KNOWLEDGE: Our benchmarks can help guide clinical NLP researchers in selecting machine-learning models according to their dataset characteristics.