Transmissibility in Interactive Nanocomposite Diffusion: The Nonlinear Double-Diffusion Model

Model analogies and exchange of ideas between physics or chemistry with biology or epidemiology have often involved inter-sectoral mapping of techniques. Material mechanics has benefitted hugely from such interpolations from mathematical physics where dislocation patterning of platstically deformed metals and mass transport in nanocomposite materials with high diffusivity paths such as dislocation and grain boundaries, have been traditionally analyzed using the paradigmatic Walgraef-Aifantis (W-A) double-diffusivity (D-D) model. A long standing challenge in these studies has been the inherent nonlinear correlation between the diffusivity paths, making it extremely difficult to analyze their interdependence. Here, we present a novel method of approximating a closed form solution of the ensemble averaged density profiles and correlation statistics of coupled dynamical systems, drawing from a technique used in mathematical biology to calculate a quantity called the basic reproduction number R0, which is the average number of secondary infections generated from every infected. We show that the R0 formulation can be used to calculate the correlation between diffusivity paths, agreeing closely with the exact numerical solution of the D-D model. The method can be generically implemented to analyze other reaction-diffusion models

Identifying and addressing barriers to implementing core electronic health record use metrics for ambulatory care: Virtual consensus conference proceedings

Precise, reliable, valid metrics that are cost-effective and require reasonable implementation time and effort are needed to drive electronic health record (EHR) improvements and decrease EHR burden. Differences exist between research and vendor definitions of metrics. PROCESS:  We convened three stakeholder groups (health system informatics leaders, EHR vendor representatives, and researchers) in a virtual workshop series to achieve consensus on barriers, solutions, and next steps to implementing the core EHR use metrics in ambulatory care. CONCLUSION:  Actionable solutions identified to address core categories of EHR metric implementation challenges include: (1) maintaining broad stakeholder engagement, (2) reaching agreement on standardized measure definitions across vendors, (3) integrating clinician perspectives, and (4) addressing cognitive and EHR burden. Building upon the momentum of this workshop\u27s outputs offers promise for overcoming barriers to implementing EHR use metrics

Transmissibility in Interactive Nanocomposite Diffusion: The Nonlinear Double-Diffusion Model

Model analogies and exchange of ideas between physics or chemistry with biology or epidemiology have often involved inter-sectoral mapping of techniques. Material mechanics has benefitted hugely from such interpolations from mathematical physics where dislocation patterning of platstically deformed metals [1,2,3] and mass transport in nanocomposite materials with high diffusivity paths such as dislocation and grain boundaries, have been traditionally analyzed using the paradigmatic Walgraef-Aifantis (W-A) double-diffusivity (D-D) model [4,5,6,7,8,9]. A long standing challenge in these studies has been the inherent nonlinear correlation between the diffusivity paths, making it extremely difficult to analyze their interdependence. Here, we present a novel method of approximating a closed form solution of the ensemble averaged density profiles and correlation statistics of coupled dynamical systems, drawing from a technique used in mathematical biology to calculate a quantity called the {\it basic reproduction number} $R_0$, which is the average number of secondary infections generated from every infected. We show that the $R_0$ formulation can be used to calculate the correlation between diffusivity paths, agreeing closely with the exact numerical solution of the D-D model. The method can be generically implemented to analyze other reaction-diffusion models.Comment: 5 two-pannelled figures, 13 page

Predicting physician departure with machine learning on EHR use patterns: A longitudinal cohort from a large multi-specialty ambulatory practice.

Physician turnover places a heavy burden on the healthcare industry, patients, physicians, and their families. Having a mechanism in place to identify physicians at risk for departure could help target appropriate interventions that prevent departure. We have collected physician characteristics, electronic health record (EHR) use patterns, and clinical productivity data from a large ambulatory based practice of non-teaching physicians to build a predictive model. We use several techniques to identify possible intervenable variables. Specifically, we used gradient boosted trees to predict the probability of a physician departing within an interval of 6 months. Several variables significantly contributed to predicting physician departure including tenure (time since hiring date), panel complexity, physician demand, physician age, inbox, and documentation time. These variables were identified by training, validating, and testing the model followed by computing SHAP (SHapley Additive exPlanation) values to investigate which variables influence the model's prediction the most. We found these top variables to have large interactions with other variables indicating their importance. Since these variables may be predictive of physician departure, they could prove useful to identify at risk physicians such who would benefit from targeted interventions

National trends in emergency conditions through the Omicron COVID‐19 wave in commercial and Medicare Advantage enrollees

Abstract Objective To evaluate trends in emergency care sensitive conditions (ECSCs) from pre‐COVID (March 2018–February 2020) through Omicron (December 2021–February 2022). Methods This cross‐sectional analysis evaluated trends in ECSCs using claims (OptumLabs Data Warehouse) from commercial and Medicare Advantage enrollees. Emergency department (ED) visits for ECSCs (acute appendicitis, aortic aneurysm/dissection, cardiac arrest/severe arrhythmia, cerebral infarction, myocardial infarction, pulmonary embolism, opioid overdose, pre‐eclampsia) were reported per 100,000 person months from March 2018 to February 2022 by pandemic wave. We calculated the percent change for each pandemic wave compared to the pre‐pandemic period. Results There were 10,268,554 ED visits (March 2018−February 2022). The greatest increases in ECSCs were seen for pulmonary embolism, cardiac arrest/severe arrhythmia, myocardial infarction, and pre‐eclampsia. For commercial enrollees, pulmonary embolism visit rates increased 22.7% (95% confidence interval [CI], 18.6%–26.9%) during Waves 2−3, 37.2% (95% CI, 29.1%–45.8%] during Delta, and 27.9% (95% CI, 20.3%–36.1%) during Omicron, relative to pre‐pandemic rates. Cardiac arrest/severe arrhythmia visit rates increased 4.0% (95% CI, 0.2%–8.0%) during Waves 2−3; myocardial infarction rates increased 4.9% (95% CI, 2.1%–7.8%) during Waves 2−3. Similar patterns were seen in Medicare Advantage enrollees. Pre‐eclampsia visit rates among reproductive‐age female enrollees increased 31.1% (95% CI, 20.9%–42.2%), 23.7% (95% CI, 7.5%,–42.3%), and 34.7% (95% CI, 16.8%–55.2%) during Waves 2−3, Delta, and Omicron, respectively. ED visits for other ECSCs declined or exhibited smaller increases. Conclusions ED visit rates for acute cardiovascular conditions, pulmonary embolism and pre‐eclampsia increased despite declines or stable rates for all‐cause ED visits and ED visits for other conditions. Given the changing landscape of ECSCs, studies should identify drivers for these changes and interventions to mitigate them

Shapley Additive Explanations (SHAP) analysis Beeswarm Plot showing the 10 top features contributing to physician departure.

Each dot represents a physician-month. Positive SHAP values (right of 0.0 vertical line) indicate the feature increased the individual physician’s monthly risk of departure. Actual feature values are color-coded with high feature values indicated in red, low values in blue and null values in gray.</p

Spearman correlation coefficients for interaction SHAP plots.

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Physician feature values stratified by departure status.

Physician feature values stratified by departure status.</p

Model comparison.

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