Large-scale phenotyping of patients with long COVID post-hospitalization reveals mechanistic subtypes of disease.

One in ten severe acute respiratory syndrome coronavirus 2 infections result in prolonged symptoms termed long coronavirus disease (COVID), yet disease phenotypes and mechanisms are poorly understood1. Here we profiled 368 plasma proteins in 657 participants ≥3 months following hospitalization. Of these, 426 had at least one long COVID symptom and 233 had fully recovered. Elevated markers of myeloid inflammation and complement activation were associated with long COVID. IL-1R2, MATN2 and COLEC12 were associated with cardiorespiratory symptoms, fatigue and anxiety/depression; MATN2, CSF3 and C1QA were elevated in gastrointestinal symptoms and C1QA was elevated in cognitive impairment. Additional markers of alterations in nerve tissue repair (SPON-1 and NFASC) were elevated in those with cognitive impairment and SCG3, suggestive of brain-gut axis disturbance, was elevated in gastrointestinal symptoms. Severe acute respiratory syndrome coronavirus 2-specific immunoglobulin G (IgG) was persistently elevated in some individuals with long COVID, but virus was not detected in sputum. Analysis of inflammatory markers in nasal fluids showed no association with symptoms. Our study aimed to understand inflammatory processes that underlie long COVID and was not designed for biomarker discovery. Our findings suggest that specific inflammatory pathways related to tissue damage are implicated in subtypes of long COVID, which might be targeted in future therapeutic trials.

Frequency and Characteristics of Nodal and Deltoid FDG and 11C-Choline Uptake on PET Performed After COVID-19 Vaccination.

BACKGROUND. COVID-19 vaccination may trigger reactive lymphadenopathy, confounding imaging interpretation. There has been limited systematic analysis of PET findings after COVID-19 vaccination. OBJECTIVE. The purpose of this study was to evaluate the frequency and characteristics of abnormal FDG and 11C-choline uptake on PET performed after COVID-19 vaccination. METHODS. This retrospective study included 67 patients (43 men and 24 women; mean [± SD] age, 75.6 ± 9.2 years) who underwent PET examination between December 14, 2020, and March 10, 2021, after COVID-19 vaccination and who had undergone prevaccination PET examination without visible axillary node uptake. A total of 52 patients received the BNT162b2 mRNA COVID-19 vaccine (Pfizer-BioNTech; hereafter referred to as the Pfizer-BioNTech vaccine), and 15 received the SARS-CoV-2 mRNA-1273 vaccine (Moderna; hereafter referred to as the Moderna vaccine). Sixty-six of the patients underwent PET/CT, and one underwent PET/MRI. Fifty-four PET examinations used FDG, and 13 used 11C-choline. PET was performed a median of 13 and 10 days after vaccination for patients who had received one (n = 44) and two (n = 23) vaccine doses, respectively. Two nuclear medicine physicians independently reviewed images and were blinded to injection laterality and the number of days since vaccination. Lymph node or deltoid SUVmax greater than the blood pool SUVmax was considered positive. Interreader agreement was assessed, and the measurements made by the more experienced physician were used for subsequent analysis. RESULTS. Positive axillary lymph node uptake was observed in 10.4% (7/67) of patients (7.4% [4/54] of FDG examinations and 23.1% [3/13] of 11C-choline examinations); of the patients with positive axillary lymph nodes, four had received the Pfizer vaccine, and three had received the Moderna vaccine. Injection laterality was documented for five of seven patients with positive axillary lymph nodes and was ipsilateral to the positive node in all five patients. PET was performed within 24 days of vaccination for all patients with a positive node. One patient showed extraaxillary lymph node uptake (ipsilateral supraclavicular uptake on FDG PET). Ipsilateral deltoid uptake was present in 14.5% (8/55) of patients with documented injection laterality, including 42.9% (3/7) of patients with positive axillary lymph nodes. Interreader agreement for SUV measurements (expressed as intraclass correlation coefficients) ranged from 0.600 to 0.988. CONCLUSION. Increased axillary lymph node or ipsilateral deltoid uptake is occasionally observed on FDG or 11C-choline PET performed after COVID-19 vaccination with the Pfizer-BioNTech or Moderna vaccine. CLINICAL IMPACT. Interpreting physicians should recognize characteristics of abnormal uptake on PET after COVID-19 vaccination to guide optimal follow-up management and reduce unnecessary biopsies.

Awareness of implicit bias mitigates discrimination in radiology resident selection.

OBJECTIVES: Implicit bias is common and is thought to drive discriminatory behaviour. Having previously demonstrated discrimination against specific applicant demographics by academic radiology departments in a simulated resident selection process, the authors sought to better understand the relationship between implicit bias and discrimination, as well as the potential and mechanisms for their mitigation. METHODS: A total of 51 faculty reviewers at three academic radiology departments, who had participated in a 2017 audit study in which they were shown to treat applicants differently based on race or ethnicity and physical appearance, were invited to complete testing for implicit racial and weight bias using the Implicit Association Test in 2019. Respondents were also surveyed regarding awareness of their own personal racial and weight biases, as well as any prior participation in formal diversity training. Comparisons were made between implicit bias scores and applicant ratings, as well as between diversity training and self-awareness of bias. RESULTS: A total of 31 out of 51 faculty reviewers (61%) completed and submitted results of race and weight Implicit Association Tests. A total of 74% (23/31) reported implicit anti-obese bias, concordant with discrimination demonstrated in the resident selection simulation, in which obese applicants were rated 0.40 standard deviations (SDs) lower than non-obese applicants (P < .001). A total of 71% (22/31) reported implicit anti-Black bias, discordant with application ratings, which were 0.47 SDs higher for Black than for White applicants (P < .001). A total of 84% (26/31) of participants reported feeling self-aware of potential racial bias at the time of application review, significantly higher than the 23% (7/31) reporting self-awareness of potential anti-obese bias (P < .001). Participation in formal diversity training was not associated with implicit anti-Black or anti-fat bias, nor with self-reported awareness of potential racial or weight-based bias (all P > .2). CONCLUSIONS: These findings suggest that implicit bias, as measured by the Implicit Association Test, does not inevitably lead to discrimination, and that personal awareness of implicit biases may allow their mitigation.

Optimal Mass Transport: Signal processing and machine-learning applications.

Transport-based techniques for signal and data analysis have received increased attention recently. Given their ability to provide accurate generative models for signal intensities and other data distributions, they have been used in a variety of applications including content-based retrieval, cancer detection, image super-resolution, and statistical machine learning, to name a few, and shown to produce state of the art results in several applications. Moreover, the geometric characteristics of transport-related metrics have inspired new kinds of algorithms for interpreting the meaning of data distributions. Here we provide a practical overview of the mathematical underpinnings of mass transport-related methods, including numerical implementation, as well as a review, with demonstrations, of several applications. Software accompanying this tutorial is available at [43].

Bone Metastases.

ABSTRACT: Bone metastases occur frequently in common malignancies such as breast and prostate cancer. They are responsible for considerable morbidity and skeletal-related events. Fortunately, there are now several systemic, focal, and targeted therapies that can improve quality and length of life, including radionuclide therapies. It is therefore important that bone metastases can be detected as early as possible and that treatment can be accurately and sensitively monitored. Several bone-specific and tumor-specific single-photon emission computed tomography and positron emission tomography molecular imaging agents are available, for detection and monitoring response to systemic therapeutics, as well as theranostic agents to confirm target expression and predict response to radionuclide therapies.

Performance of ChatGPT-4 and Bard chatbots in responding to common patient questions on prostate cancer 177Lu-PSMA-617 therapy.

Background: Many patients use artificial intelligence (AI) chatbots as a rapid source of health information. This raises important questions about the reliability and effectiveness of AI chatbots in delivering accurate and understandable information. Purpose: To evaluate and compare the accuracy, conciseness, and readability of responses from OpenAI ChatGPT-4 and Google Bard to patient inquiries concerning the novel 177Lu-PSMA-617 therapy for prostate cancer. Materials and methods: Two experts listed the 12 most commonly asked questions by patients on 177Lu-PSMA-617 therapy. These twelve questions were prompted to OpenAI ChatGPT-4 and Google Bard. AI-generated responses were distributed using an online survey platform (Qualtrics) and blindly rated by eight experts. The performances of the AI chatbots were evaluated and compared across three domains: accuracy, conciseness, and readability. Additionally, potential safety concerns associated with AI-generated answers were also examined. The Mann-Whitney U and chi-square tests were utilized to compare the performances of AI chatbots. Results: Eight experts participated in the survey, evaluating 12 AI-generated responses across the three domains of accuracy, conciseness, and readability, resulting in 96 assessments (12 responses x 8 experts) for each domain per chatbot. ChatGPT-4 provided more accurate answers than Bard (2.95 ± 0.671 vs 2.73 ± 0.732, p=0.027). Bard's responses had better readability than ChatGPT-4 (2.79 ± 0.408 vs 2.94 ± 0.243, p=0.003). Both ChatGPT-4 and Bard achieved comparable conciseness scores (3.14 ± 0.659 vs 3.11 ± 0.679, p=0.798). Experts categorized the AI-generated responses as incorrect or partially correct at a rate of 16.6% for ChatGPT-4 and 29.1% for Bard. Bard's answers contained significantly more misleading information than those of ChatGPT-4 (p = 0.039). Conclusion: AI chatbots have gained significant attention, and their performance is continuously improving. Nonetheless, these technologies still need further improvements to be considered reliable and credible sources for patients seeking medical information on 177Lu-PSMA-617 therapy.

Theranostics and artificial intelligence: new frontiers in personalized medicine.

The field of theranostics is rapidly advancing, driven by the goals of enhancing patient care. Recent breakthroughs in artificial intelligence (AI) and its innovative theranostic applications have marked a critical step forward in nuclear medicine, leading to a significant paradigm shift in precision oncology. For instance, AI-assisted tumor characterization, including automated image interpretation, tumor segmentation, feature identification, and prediction of high-risk lesions, improves diagnostic processes, offering a precise and detailed evaluation. With a comprehensive assessment tailored to an individual's unique clinical profile, AI algorithms promise to enhance patient risk classification, thereby benefiting the alignment of patient needs with the most appropriate treatment plans. By uncovering potential factors unseeable to the human eye, such as intrinsic variations in tumor radiosensitivity or molecular profile, AI software has the potential to revolutionize the prediction of response heterogeneity. For accurate and efficient dosimetry calculations, AI technology offers significant advantages by providing customized phantoms and streamlining complex mathematical algorithms, making personalized dosimetry feasible and accessible in busy clinical settings. AI tools have the potential to be leveraged to predict and mitigate treatment-related adverse events, allowing early interventions. Additionally, generative AI can be utilized to find new targets for developing novel radiopharmaceuticals and facilitate drug discovery. However, while there is immense potential and notable interest in the role of AI in theranostics, these technologies do not lack limitations and challenges. There remains still much to be explored and understood. In this study, we investigate the current applications of AI in theranostics and seek to broaden the horizons for future research and innovation.

Low PSA radiographic disease progression on C11-choline PET.

Background: For men with prostate cancer, radiographic progression may occur without a concordant rise in prostate-specific antigen (PSA). Our study aimed to assess the prevalence of radiographic progression using C-11 choline positron emission tomography (PET) imaging in patients achieving ultra-low PSA values and to evaluate clinical outcomes in this patient population. Methods: In a single institution study, we reviewed the prospectively maintained Mayo Clinic C-11 Choline PET metastatic prostate cancer registry to identify patients experiencing radiographic disease progression (rDP) on C-11 choline PET scan while the PSA value was less than 0.5 ng/mL. Disease progression was confirmed by tissue biopsy or response to subsequent therapy. Clinicopathologic variables were abstracted by trained research personnel. Overall survival was estimated using the Kaplan-Meier method. Intergroup differences were assessed using the log-rank test. A univariate and multivariate Cox regression model was performed to investigate variables associated with poor survival after rDP. Results: A total of 1323 patients within the registry experienced rDP between 2011 and 2021, including 220 (16.6%) men with rDP occurring at low PSA level. A median (interquartile range [IQR]) of 54.7 (19.7-106.9) months elapsed between the time of prostate cancer diagnosis and low PSA rDP, during which 173 patients (78%) developed castration-resistant prostate cancer (CRPC). Sites of low PSA rDP included local recurrence (n = 17, 8%), lymph node (n = 90, 41%), bone (n = 94, 43%) and visceral metastases (n = 19, 9%). Biopsy at the time of rDP demonstrated small-cell or neuroendocrine features in 21% of patients with available tissue. Over a median (IQR) follow-up of 49.4 (21.3-95.1) months from the time of low PSA rDP, 46% (n = 102) of patients died. Factors associated with poorer survival outcomes include advanced age at rDP, CRPC status, bone and visceral metastasis (p value <0.05). Visceral metastases were associated with decreased overall survival (p = 0.009 by log-rank) as compared with other sites of rDP. Conclusions: Men with prostate cancer commonly experience metastatic progression at very low or even undetectable PSA levels. Periodic imaging, even at low absolute PSA values, may result in more timely identification of disease progression.

Salivary toxicity from PSMA-targeted radiopharmaceuticals: What we have learned and where we are going.

Clinical trials of prostate-specific membrane antigen (PSMA) targeted radiopharmaceuticals have shown encouraging results. Some agents, like lutetium-177 [177Lu]Lu-PSMA-617 ([177Lu]Lu-PSMA-617), are already approved for late line treatment of metastatic castration-resistant prostate cancer (mCRPC). Projections are for continued growth of this treatment modality; [177Lu]Lu-PSMA-617 is being studied both in earlier stages of disease and in combination with other anti-cancer therapies. Further, the drug development pipeline is deep with variations of PSMA-targeting radionuclides, including higher energy alpha particles conjugated to PSMA-honing vectors. It is safe to assume that an increasing number of patients will be exposed to PSMA-targeted radiopharmaceuticals during the course of their cancer treatment. In this setting, it is important to better understand and mitigate the most commonly encountered toxicities. One particularly vexing side effect is xerostomia. In this review, we discuss the scope of the problem, inventories to better characterize and monitor this troublesome side effect, and approaches to preserve salivary function and effectively palliate symptoms. This article aims to serve as a useful reference for prescribers of PSMA-targeted radiopharmaceuticals, while also commenting on areas of missing data and opportunities for future research.

Rates of convergence for regression with the graph poly-Laplacian.

In the (special) smoothing spline problem one considers a variational problem with a quadratic data fidelity penalty and Laplacian regularization. Higher order regularity can be obtained via replacing the Laplacian regulariser with a poly-Laplacian regulariser. The methodology is readily adapted to graphs and here we consider graph poly-Laplacian regularization in a fully supervised, non-parametric, noise corrupted, regression problem. In particular, given a dataset {xi}i=1n and a set of noisy labels {yi}i=1n⊂R we let un:{xi}i=1n→R be the minimizer of an energy which consists of a data fidelity term and an appropriately scaled graph poly-Laplacian term. When yi=g(xi)+ξi, for iid noise ξi, and using the geometric random graph, we identify (with high probability) the rate of convergence of un to g in the large data limit n→∞. Furthermore, our rate is close to the known rate of convergence in the usual smoothing spline model.

Technologist-Based Implementation of Total Metabolic Tumor Volume into Clinical Practice.

Metabolic tumor volume (MTV) is defined as the total metabolically active tumor volume seen on 18F-FDG PET/CT examinations. Calculating MTV is often time-consuming, requiring a high degree of manual input. In this study, the MTV calculations of a board-certified nuclear radiologist were compared with those of 2 nuclear medicine technologists. As part of the technologists' educational program, after their classroom time they were trained by the radiologist for 30 min. The technologists calculated MTV within 7.5% of the radiologist's calculations in a set of patients who had diffuse large B-cell lymphoma and were undergoing initial staging 18F-FDG PET/CT. These findings suggest that nuclear medicine technologists may help accelerate implementation of MTV into clinical practice with favorable accuracy, possibly as an initial step followed by validation by the interpreting physician. The aim of this study was to explore whether efficiency is improved by integrating nuclear medicine technologists into a semiautomated workflow to calculate total MTV.

Potential applications of PET/MRI in non-oncologic conditions within the abdomen and pelvis.

PET/MRI is a relatively new imaging modality with several advantages over PET/CT that promise to improve imaging of the abdomen and pelvis for specific diagnostic tasks by combining the superior soft tissue characterization of MRI with the functional information acquired from PET. PET/MRI has an established role in staging and response assessment of multiple abdominopelvic malignancies, but the modality is not yet established for non-oncologic conditions of the abdomen and pelvis. In this review, potential applications of PET/MRI for non-oncologic conditions of abdomen and pelvis are outlined, and the available literature is reviewed to highlight promising areas for further research and translation into clinical practice.

Metabolic PET/CT analysis of aggressive Non-Hodgkin lymphoma prior to Axicabtagene Ciloleucel CAR-T infusion: predictors of progressive disease, survival, and toxicity.

PET/CT is used to evaluate relapsed/refractory non-Hodgkin lymphoma (NHL) prior to chimeric antigen receptor T-cell (CAR-T) infusion at two time points: pre-leukapheresis (pre-leuk) and pre-lymphodepletion chemotherapy (pre-LD). We hypothesized that changes in PET/CT between these time points predict outcomes after CAR-T. Metabolic tumor volume (MTV), total lesion glycolysis (TLG), and other metrics were calculated from pre-leuk and pre-LD PET/CT scans in patients with NHL who received axicabtagene ciloleucel, and assessed for association with outcomes. Sixty-nine patients were analyzed. While single time point PET/CT characteristics were not associated with risk of PD or death, increases from pre-leuk to pre-LD in parenchymal MTV, nodal MTV, TLG of the largest lesion, and total number of lesions were associated with increased risk of death (p < 0.05 for all). LASSO analysis identified increasing extranodal MTV and increasing TLG of the largest lesion as strong predictors of death (AUC 0.74). Greater pre-LD total MTV was associated with higher risk of grade 3+ immune effector cell-associated neurotoxicity syndrome (ICANS) (p = 0.042). Increasing metabolic disease burden during CAR-T manufacturing is associated with increased risk of progression and death. A two variable risk score stratifies prognosis prior to CAR-T infusion and may inform risk-adapted strategies.

The impact of imputation quality on machine learning classifiers for datasets with missing values.

BACKGROUND: Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missing values are typically imputed using established methods, followed by classification of the now complete samples. The focus of the machine learning researcher is to optimise the classifier's performance. METHODS: We utilise three simulated and three real-world clinical datasets with different feature types and missingness patterns. Initially, we evaluate how the downstream classifier performance depends on the choice of classifier and imputation methods. We employ ANOVA to quantitatively evaluate how the choice of missingness rate, imputation method, and classifier method influences the performance. Additionally, we compare commonly used methods for assessing imputation quality and introduce a class of discrepancy scores based on the sliced Wasserstein distance. We also assess the stability of the imputations and the interpretability of model built on the imputed data. RESULTS: The performance of the classifier is most affected by the percentage of missingness in the test data, with a considerable performance decline observed as the test missingness rate increases. We also show that the commonly used measures for assessing imputation quality tend to lead to imputed data which poorly matches the underlying data distribution, whereas our new class of discrepancy scores performs much better on this measure. Furthermore, we show that the interpretability of classifier models trained using poorly imputed data is compromised. CONCLUSIONS: It is imperative to consider the quality of the imputation when performing downstream classification as the effects on the classifier can be considerable.

Many artificial intelligence (AI) methods aim to classify samples of data into groups, e.g., patients with disease vs. those without. This often requires datasets to be complete, i.e., that all data has been collected for all samples. However, in clinical practice this is often not the case and some data can be missing. One solution is to 'complete' the dataset using a technique called imputation to replace those missing values. However, assessing how well the imputation method performs is challenging. In this work, we demonstrate why people should care about imputation, develop a new method for assessing imputation quality, and demonstrate that if we build AI models on poorly imputed data, the model can give different results to those we would hope for. Our findings may improve the utility and quality of AI models in the clinic.

Can the use of deception be justified in medical education research? A point/counterpoint and case study.

Deception is a common feature of behavioral research design, although not commonly employed in the medical literature. It can promote scientific validity but is ethically controversial because it compromises subject autonomy and incurs additional costs.  In this Point/Counterpoint monograph, we review the nature of deception in research and present arguments for and against its ethical use as a research methodology in behavioral studies.  We describe the necessary guidelines, safeguards, and oversight, when deceptive methodology is considered, and report our experiences and lessons learned from conducting a multi-institutional audit study that relied upon deception of academic radiology faculty.

You're Biased! Deal With It.

Widespread implementation of the Implicit Association Test has revealed that most doctors, and many radiologists, hold implicit racial bias. Because implicit bias is thought to drive discrimination, it has emerged as a primary explanation for radiology's ongoing racial disparity. In this critical review of the literature, which includes empirical studies of radiology resident selection, the authors argue that implicit bias is a universal human instinctual characteristic, but one that humans have the capacity to override through more deliberative thought processes. Overstating the validity of the Implicit Association Test, and the role of implicit bias in causing radiology's racial disparities, is unwarranted, unhelpful, and potentially distracting from addressing actual causes and real solutions.

Amyloid arthropathy in smoldering myeloma: Do not take it lightly.

We report a case of smoldering multiple myeloma patient who developed signs and symptoms consistent with polyarthritis. A PET-CT demonstrated marked FDG activity in multiple joints, concerning for inflammatory arthritis. Arthrocentesis from the glenohumeral joint was consistent with inflammatory synovial fluid with no evidence for infection or crystals. Congo-red stain of the synovial fluid was positive, and mass-spectrometry based amyloid typing was consistent with wild-type transthyretin type. The patient responded instantly to glucocorticoids. This case reports highlights the feasibility of non-tissue diagnosis of amyloidosis using body fluids and underscores the importance of accurate typing to avoid erroneous treatment.

Radium-223 in the Third-Line Setting in Metastatic Castration-Resistant Prostate Cancer: Impact of Concomitant Use of Enzalutamide on Overall Survival (OS) and Predictors of Improved OS.

INTRODUCTION: Radium-223 (Ra-223) has been recommended for bone-dominant metastatic castration-resistant prostate cancer (mCRPC). Second-generation hormone therapy in combination with Ra-223 in mCRPC has been utilized, yet its benefit has not been well elucidated. We investigated the potential survival benefit of concomitant enzalutamide with Ra-223 in the third-line setting and predictors of improved overall survival (OS). PATIENTS AND METHODS: We retrospectively identified 51 patients with bone-dominant mCRPC that were treated with Ra-223 in the postchemotherapy and post-hormone therapy setting, either alone (group A; n = 32) or with concomitant enzalutamide (group B; n = 19). The primary endpoint was to study the OS difference between groups A and B. The secondary endpoint was to identify predictors of improved OS with Ra-223 in the third-line setting. RESULTS: Mean age was 70.9 years, median baseline prostatic-specific antigen (PSA) was 23.1 ng/mL, alkaline phosphatase was 91 IU/L, and hemoglobin was 12.5 g/dL. There was no difference in median OS between groups A and B, at 20.4 versus 17.5 months, respectively (P = .5186). In univariate and multivariate analyses, only pre-Ra-223 PSA < 30 ng/mL and Eastern Cooperative Oncology Group performance status < 2 were associated with improved OS. CONCLUSION: In our study cohort, concomitant use of enzalutamide with Ra-223 in the mCRPC setting was not associated with improved OS. Only pretreatment PSA < 30 ng/mL and pretreatment Eastern Cooperative Oncology Group performance status < 2 were associated with improved OS. Further prospective studies are warranted.

Factors Influential in the Selection of Radiology Residents in the Post-Step 1 World: A Discrete Choice Experiment.

OBJECTIVES: Reporting of United States Medical Licensing Examination Step 1 results will transition from a numerical score to a pass or fail result. We sought an objective analysis to determine changes in the relative importance of resident application attributes when numerical Step 1 results are replaced. METHODS: A discrete choice experiment was designed to model radiology resident selection and determine the relative weights of various application factors when paired with a numerical or pass or fail Step 1 result. Faculty involved in resident selection at 14 US radiology programs chose between hypothetical pairs of applicant profiles between August and November 2020. A conditional logistic regression model assessed the relative weights of the attributes, and odds ratios (ORs) were calculated. RESULTS: There were 212 participants. When a numerical Step 1 score was provided, the most influential attributes were medical school (OR: 2.35, 95% confidence interval [CI]: 2.07-2.67), Black or Hispanic race or ethnicity (OR: 2.04, 95% CI: 1.79-2.38), and Step 1 score (OR: 1.8, 95% CI: 1.69-1.95). When Step 1 was reported as pass, the applicant's medical school grew in influence (OR: 2.78, 95% CI: 2.42-3.18), and there was a significant increase in influence of Step 2 scores (OR: 1.31, 95% CI: 1.23-1.40 versus OR 1.57, 95% CI: 1.46-1.69). There was little change in the relative influence of race or ethnicity, gender, class rank, or clerkship honors. DISCUSSION: When Step 1 reporting transitions to pass or fail, medical school prestige gains outsized influence and Step 2 scores partly fill the gap left by Step 1 examination as a single metric of decisive importance in application decisions.

Moving Radiology Workflow to the Electronic Health Record: Quantitative and Qualitative Experience From a Large Academic Medical Center.

RATIONALE AND OBJECTIVES: To objectively and subjectively evaluate a large, academic radiology department's transition to electronic health record (EHR) centered workflow. MATERIALS AND METHODS: Multiple metrics were compared from before and after the move to EHR-driven workflow. Examination ordering and reading priority data were obtained for 30 days both before and after the transition. Sixteen radiologists were observed opening a computed tomography (CT) examination, and time to open, mouse clicks, and keystrokes were recorded. Information available to the radiologist during interpretation was also compared. Additionally, a 12 question survey was sent out to the residents and faculty both before and after the transition. RESULTS: Implementation of an eight-level reading priority system increased worklist granularity and improved identification of more urgent studies to read. Radiologists opened CT studies in picture archiving and communications system-driven workflow in 52.4 ± 16.9 seconds using 9.5 ± 3.9 clicks and 6.3 ± 2.9 keystrokes, compared to 17.3 ± 9.5 seconds, 4.8 ± 1.5 clicks, and 0.1 ± 0.3 keystrokes in EHR-driven workflow (p < 0.001 for each measure). More information was available to the radiologist during examination interpretation, and 54.7% of radiologists rated the ease of use of the new system as good or very good (compared to 4.2% for the old system, p < 0.001). CONCLUSION: Transitioning to an EHR-driven workflow at a large academic medical center improved efficiency, was favorable to radiologists, and enhanced examination prioritization.