Search

Background Anesthesiologists integrate numerous variables to determine an opioid dose that manages patient nociception and pain while minimizing adverse effects. Clinical dashboards that enable physicians to compare themselves to their peers can reduce unnecessary variation in patient care and improve outcomes. However, due to the complexity of anesthetic dosing decisions, comparative visualizations of opioid-use patterns are complicated by case-mix differences between providers. Objectives This single-institution case study describes the development of a pediatric anesthesia dashboard and demonstrates how advanced computational techniques can facilitate nuanced normalization techniques, enabling meaningful comparisons of complex clinical data. Methods We engaged perioperative-care stakeholders at a tertiary care pediatric hospital to determine patient and surgical variables relevant to anesthesia decision-making and to identify end-user requirements for an opioid-use visualization tool. Case data were extracted, aggregated, and standardized. We performed multivariable machine learning to identify and understand key variables. We integrated interview findings and computational algorithms into an interactive dashboard with normalized comparisons, followed by an iterative process of improvement and implementation. Results The dashboard design process identified two mechanisms—interactive data filtration and machine-learning-based normalization—that enable rigorous monitoring of opioid utilization with meaningful case-mix adjustment. When deployed with real data encompassing 24,332 surgical cases, our dashboard identified both high and low opioid-use outliers with associated clinical outcomes data. Conclusion A tool that gives anesthesiologists timely data on their practice patterns while adjusting for case-mix differences empowers physicians to track changes and variation in opioid administration over time. Such a tool can successfully trigger conversation amongst stakeholders in support of continuous improvement efforts. Clinical analytics dashboards can enable physicians to better understand their practice and provide motivation to change behavior, ultimately addressing unnecessary variation in high impact medication use and minimizing adverse effects.

Background Chat Generative Pre-trained Transformer (ChatGPT) is a 175-billion-parameter natural language processing model that can generate conversation-style responses to user input. Objective This study aimed to evaluate the performance of ChatGPT on questions within the scope of the United States Medical Licensing Examination Step 1 and Step 2 exams, as well as to analyze responses for user interpretability. Methods We used 2 sets of multiple-choice questions to evaluate ChatGPT’s performance, each with questions pertaining to Step 1 and Step 2. The first set was derived from AMBOSS, a commonly used question bank for medical students, which also provides statistics on question difficulty and the performance on an exam relative to the user base. The second set was the National Board of Medical Examiners (NBME) free 120 questions. ChatGPT’s performance was compared to 2 other large language models, GPT-3 and InstructGPT. The text output of each ChatGPT response was evaluated across 3 qualitative metrics: logical justification of the answer selected, presence of information internal to the question, and presence of information external to the question. Results Of the 4 data sets, AMBOSS-Step1, AMBOSS-Step2, NBME-Free-Step1, and NBME-Free-Step2, ChatGPT achieved accuracies of 44% (44/100), 42% (42/100), 64.4% (56/87), and 57.8% (59/102), respectively. ChatGPT outperformed InstructGPT by 8.15% on average across all data sets, and GPT-3 performed similarly to random chance. The model demonstrated a significant decrease in performance as question difficulty increased (P=.01) within the AMBOSS-Step1 data set. We found that logical justification for ChatGPT’s answer selection was present in 100% of outputs of the NBME data sets. Internal information to the question was present in 96.8% (183/189) of all questions. The presence of information external to the question was 44.5% and 27% lower for incorrect answers relative to correct answers on the NBME-Free-Step1 (P Conclusions ChatGPT marks a significant improvement in natural language processing models on the tasks of medical question answering. By performing at a greater than 60% threshold on the NBME-Free-Step-1 data set, we show that the model achieves the equivalent of a passing score for a third-year medical student. Additionally, we highlight ChatGPT’s capacity to provide logic and informational context across the majority of answers. These facts taken together make a compelling case for the potential applications of ChatGPT as an interactive medical education tool to support learning.

BackgroundThe emergence of novel, potentially vaccine-resistant strains of SARS-CoV-2 poses a serious risk to public health. The interactions between passengers and drivers facilitated by rideshare platforms such as Uber are, essentially, a series of partially standardized, random experiments of SARS-CoV-2 transmission. Rideshare companies share data with government health agencies, but no statistical method is available to aggregate these data for the systematic study of the transmission dynamics of COVID-19.MethodsWe develop a proof-of-concept model for the analysis of data from rideshare interactions merged with COVID-19 diagnosis records. Using simulated data with rideshare volumes, disease prevalence, and diagnosis rates based on a large US city, we use the model to test hypotheses about the emergence of viral strains and their transmission characteristics in the presence of non-pharmaceutical interventions and superspreaders.FindingsData from 10 simulated trials of SARS-CoV-2 propagation within the Los Angeles rideshare network resulted in an average of 190,387.1 potentially infectious rideshare interactions. Assuming access to data on 25% of the total estimated infections (Partial Reporting), these interactions resulted in an average of 409.0 diagnosed rideshare infections given our transmission model assumptions. For each of the 10 simulated trials, analysis given Partial Reporting could consistently differentiate between a baseline strain and an emergent, more infectious viral strain, enabling hypothesis testing about transmission characteristics.InterpretationSimulated evaluation of a novel statistical model suggests that rideshare data combined with COVID-19 diagnosis data have the potential to automate continued surveillance of emergent novel strains of SARS-CoV-2 and their transmission characteristics.

Background: The emergence of novel, partially vaccine-resistant strains of SARS-CoV-2 poses a serious risk to public health. The interactions between passengers and drivers facilitated by rideshare platforms, such as Uber and Lyft, are essentially a series of partially standardized, pseudo-random experiments of SARS-CoV-2 transmission. Rideshare companies share data with government health agencies, but no statistical method is available to aggregate these data for the systematic study of the transmission dynamics of COVID-19. Methods: We develop a proof-of-concept method for the analysis of data from rideshare interactions merged with COVID-19 diagnosis records. Using simulated data with rideshare volumes, disease prevalence, and diagnosis rates based on a large US city, we test hypotheses about the emergence of viral strains and their transmission characteristics in the presence of non-pharmaceutical interventions and superspreaders. Findings: Data from ten simulated trials of SARS-CoV-2 propagation within the Los Angeles rideshare network resulted in an average of 190,387·1 potentially infectious rideshare interactions. Assuming access to data on 25% of the total estimated infections (Partial Reporting), these interactions resulted in an average of 409·0 diagnosed rideshare infections given our transmission model assumptions. For each of the ten simulated trials, analysis given Partial Reporting could consistently differentiate between a baseline strain and an emergent, more infectious viral strain, enabling hypothesis testing about transmission characteristics. Interpretation: Simulated evaluation of a novel statistical method suggests that rideshare data combined with COVID-19 diagnosis data have the potential to automate continued surveillance of emergent novel strains of SARS-CoV-2 and their transmission characteristics. Funding Information: Stanford University, Stanford. Declaration of Interests: The authors have no relationships or disclosures relevant to the contents of this paper. All authors have no conflicts of interest, financial or otherwise.

Repairing cardiac tissue after myocardial infarction (MI) is one of the most challenging goals in tissue engineering. Following ischemic injury, significant matrix remodeling and the formation of avascular scar tissue significantly impairs cell engraftment and survival in the damaged myocardium. This limits the efficacy of cell replacement therapies, demanding strategies that reduce pathological scarring to create a suitable microenvironment for healthy tissue regeneration. Here, we demonstrate the successful fabrication of discrete hyaluronic acid (HA)-based microrods to provide local biochemical and biomechanical signals to reprogram cells and attenuate cardiac fibrosis. HA microrods were produced in a range of physiological stiffness and shown to degrade in the presence of hyaluronidase. Additionally, we show that fibroblasts interact with these microrods invitro, leading to significant changes in proliferation, collagen expression and other markers of a myofibroblast phenotype. When injected into the myocardium of an adult rat MI model, HA microrods prevented left ventricular wall thinning and improved cardiac function at 6 weeks post infarct.