Virtual Sprint Outpatient Electronic Health Record Training and Optimization Effect on Provider Burnout

Objectives This study aimed to develop a virtual electronic health record (EHR) training and optimization program and evaluate the impact of the virtual model on provider and staff burnout and electronic health record (EHR) experience. Methods UCHealth created and supported a multidisciplinary EHR optimization and training program, known as the Epic Sprint Program. The Sprint Team conducted dozens of onsite Sprint events over the course of several years prior to the pandemic but transitioned to a fully virtual program and successfully “sprinted” 21 outpatient clinics from May to December 2020. Core program components of group and 1:1 training, workflow analysis, and new or adjusted EHR build were unchanged from the onsite model. Pre- and post-Sprint surveys provided detailed, objective data about EHR usability, EHR proficiency, job satisfaction, and burnout. Results The EHR Net Promoter Score (NPS), a likelihood to recommend metric, increased by 39 points (−3 pre and 36 post; p < 0.001) for providers and 29 points (8 pre and 37 post; p = 0.001) for staff post-Sprint. Positive provider (NPS = +53) and staff (NPS = +47) NPS scores indicated a high likelihood to recommend the Sprint Program. Post-Sprint surveys also reflect an increase in providers (10%; p = 0.04) and staff (9%; 0.13) who indicated “no burnout” or “did not feel burned out.” Discussion The UCHealth Sprint Team transitioned this comprehensive, enterprise level initiative from an onsite model to a fully virtual EHR training and optimization program during the first few months of the novel coronavirus disease (COVID-19) pandemic. Despite this change in program delivery, survey data clearly demonstrated improved EHR satisfaction, a high likelihood to recommend a sprint to a friend or colleague, and a trend toward burnout reduction in providers and staff. Conclusion Changing an existing on-site EHR optimization program to a purely virtual format can be successful, and this study showed improved provider and staff EHR satisfaction with reduced burnout.

Simulation of Selected Traveling Wave Directional Multiplexers

Simulation of selected traveling wave multiplexers have been done in this paper in order to obtain the desired results based on the existing theoretical analysis on this field. The even and odd mode analysis have been added in the beginning to showcase the expected desired conclusions made by the simulation software. The equivalent circuits of the even and odd modes are given. An optimization program has been utilized in order to optimize the multiplexer's findings. Four multiplexers with different bandwidths have been simulated and tested by via ADS program to highlight the returns and validity of the modified traveling wave multiplexers.

Optimization of reinforcing steel with genetic algorithms for the design of concrete columns

Is presented hereby the creation of an optimization program for the design of reinforcing steel for any type of structural elements which may be considered as columns, pillar, pilots or dies, subjected to flexo-compression mechanic stresses for rectangular cross sections elements with the support of numeric methods and meta-heuristic algorithms for the optimization of constructions costs, particularly the Genetic algorithm, adapting such algorithms to the optimization problem so that it makes the resultant designs practical and convenient for its application in the construction industry, based on certain criteria from NTC-17 and questioning other. It will be shown how such optimization algorithm was adapted to the problem, as well as other general numeric methods for engineering and others developed specifically for structural engineering for the optimal design of this sort of concrete elements. Moreover, sensibility cost analysis of unitary construction prices are presented as well, with which the objective function for the optimization algorithm was created. At the end, results of various experiments with and without the optimization program are shown, making comparisons between different structural models regarding cost and geometry.

Travel Demand Estimation in Urban Road Networks as Inverse Traffic Assignment Problem

Nowadays, traffic engineers employ a variety of intelligent tools for decision support in the field of transportation planning and management. However, not a one available tool is useful without precise travel demand information which is actually the key input data in simulation models used for traffic prediction in urban road areas. Thus, it is no wonder that the problem of estimation of travel demand values between intersections in a road network is a challenge of high urgency. The present paper is devoted to this urgent problem and investigates its properties from computational and mathematical perspectives. We rigorously define the travel demand estimation problem as directly inverse to traffic assignment in a form of a bi-level optimization program avoiding usage of any pre-given (a priori) information on trips. The computational study of the obtained optimization program demonstrates that generally it has no clear descent direction, while the mathematical study advances our understanding on rigor existence and uniqueness conditions of its solution. We prove that once a traffic engineer recognizes the travel demand locations, then their values in the road network can be found uniquely. On the contrary, we discover a non-continuous dependence between the travel demand locations and absolute difference of observed and modeled traffic values. Therefore, the results of the present paper reveal that the actual problem to be solved when dealing with travel demand estimation is the problem of recognition of travel demand locations. The obtained findings contribute in the theory of travel demand estimation and give fresh managerial insights for traffic engineers.

2-aminobenzimidazoles for leishmaniasis: From initial hit discovery to in vivo profiling

Leishmaniasis is a major infectious disease with hundreds of thousands of new cases and over 20,000 deaths each year. The current drugs to treat this life-threatening infection have several drawbacks such as toxicity and long treatment regimens. A library of 1.8 million compounds, from which the hits reported here are publicly available, was screened against Leishmania infantum as part of an optimization program; a compound was found with a 2-aminobenzimidazole functionality presenting moderate potency, low metabolic stability and high lipophilicity. Several rounds of synthesis were performed to incorporate chemical groups capable of reducing lipophilicity and clearance, leading to the identification of compounds that are active against different parasite strains and have improved in vitro properties. As a result of this optimization program, a group of compounds was further tested in anticipation of in vivo evaluation. In vivo tests were carried out with compounds 29 (L. infantum IC50: 4.1 μM) and 39 (L. infantum IC50: 0.5 μM) in an acute L. infantum VL mouse model, which showed problems of poor exposure and lack of efficacy, despite the good in vitro potency.

Optimization of Powertrain in EV

The method for preliminary powertrain design is presented in the paper. Performance of the EV is realized by motor torque–speed curve and gear ratio optimization. The typical two-zone mechanical characteristic of a PMSM traction motor is included in the optimization program. The longitudinal vehicle model is considered in the paper. Some examples try to show the calculation possibilities in application to existing vehicles: Tesla Model S and Mini Cooper SE.

Defining quantum divergences via convex optimization

We introduce a new quantum Rényi divergence Dα# for α∈(1,∞) defined in terms of a convex optimization program. This divergence has several desirable computational and operational properties such as an efficient semidefinite programming representation for states and channels, and a chain rule property. An important property of this new divergence is that its regularization is equal to the sandwiched (also known as the minimal) quantum Rényi divergence. This allows us to prove several results. First, we use it to get a converging hierarchy of upper bounds on the regularized sandwiched α-Rényi divergence between quantum channels for α>1. Second it allows us to prove a chain rule property for the sandwiched α-Rényi divergence for α>1 which we use to characterize the strong converse exponent for channel discrimination. Finally it allows us to get improved bounds on quantum channel capacities.