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Effect of Tailored Coaching on Physicians’ Electronic Health Record Proficiency and User Experience: A Randomized Crossover Study

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Mayo Clinic Proceedings: Digital Health 2023年第2期1卷 94-104页

作者： Khairat, Saif Chourasia, Prabal Kwong, Elizabeth Choi, Ji Min Seashore, Carl Carolina Health Informatics Program University of North Carolina at Chapel Hill Chapel Hill NC United States School of Nursing University of North Carolina at Chapel Hill Chapel Hill NC United States Department of Statistics and Operations Research University of North Carolina at Chapel Hill Chapel Hill NC United States Division of General Pediatrics and Adolescent Medicine University of North Carolina at Chapel Hill Chapel Hill NC United States

Objective: To examine the effect of a tailored EHR coaching intervention, informed by audit log data, on physicians’ EHR proficiency, and user experience among pediatricians. Patients and Methods: A 12-month (August 2020 – August 2021) randomized crossover quality improvement study of a tailored EHR coaching was conducted on 34 pediatric physicians at a major medical center. Participants were randomized into Group AB or Group BA. The intervention was a single 1-hour, one-on-one coaching session. An Epic certified pediatrician tailored each coaching session to meet each physician needs using their EHR audit log data. We analyzed EHR audit log data for 3 months pre- and post-intervention. Results: Out of the 34 physicians, 15 (44%) were primary care pediatricians, 19 (56%) were female, 24 (70%) practiced at the medical center. During the initial intervention, the average proficiency score for the Group AB increased by 8.9% (pre-post difference: 0.37, 95% CI: −0.35 to 1.09;P-value=.381). For the crossover intervention, the average proficiency score for Group BA significantly increased by 11.2% (pre-post difference: 0.31, 95% CI: −0.11 to 0.74;P-value=.05). The average perceived EHR workload decreased post coaching sessions compared to pre-session (50.89 vs 46.66, P-value=.06). Post-coaching intervention, the average score for perceived EHR usability improved compared to pre-intervention (4.10 vs 4.34;P-value=.1). Conclusion: Electronic health record audit log data can be used to inform tailored coaching to improve physicians’ EHR proficiency levels and user experience. Tailored EHR coaching can increase awareness about efficiency tools available in the EHR. These findings are relevant to decisionmakers and learning health systems interested in provider well-being by optimizing EHR use. © 2023 The Authors

关键词： CRT classroom training EHR electronic health record NASA-TLX NASA Task Load Index SD standard deviation USE Usability Satisfaction and Ease of Use

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Cost-Effectiveness Analysis for Disease Prevention – A Case Study on Colorectal Cancer Screening

arXiv

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arXiv 2024年

作者： Xiong, Yi Chan, Kwun C.G. Gorfine, Malka Hsu, Li Department of Biostatistics University at Buffalo BuffaloNY United States Department of Biostatistics University of Washington SeattleWA United States Department of Statistics and Operations Research Tel Aviv University Tel Aviv Israel Biostatistics Program Fred Hutchinson Cancer Center SeattleWA United States

Cancer Screening has been widely recognized as an effective strategy for preventing the disease. Despite its effectiveness, determining when to start screening is complicated, because starting too early increases the number of screenings over lifetime and thus costs but starting too late may miss the cancer that could have been prevented. Therefore, to make an informed recommendation on the age to start screening, it is necessary to conduct cost-effectiveness analysis to assess the gain in life years relative to the cost of screenings. As more large-scale observational studies become accessible, there is growing interest in evaluating cost-effectiveness based on empirical evidence. In this paper, we propose a unified measure for evaluating cost-effectiveness and a causal analysis for the continuous intervention of screening initiation age, under the multi-state modeling with semi-competing risks. Extensive simulation results show that the proposed estimators perform well in realistic scenarios. We perform a cost-effectiveness analysis of the colorectal cancer screening, utilizing data from the large-scale Women’s Health Initiative. Our analysis reveals that initiating screening at age 50 years yields the highest quality-adjusted life years with an acceptable incremental cost-effectiveness ratio compared to no screening, providing real-world evidence in support of screening recommendation for colorectal cancer. © 2024, CC BY.

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Evaluation of Marginal and Average Sectorial Ghgs Emissions Abatement Costs Using an Open-Source Energy System Optimization Model: The Italian Case Study

SSRN

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SSRN 2024年

作者： Nicoli, Matteo Colucci, Gianvito Faria, Victor Augusto Duraes Lerede, Daniele de Queiroz, Anderson Rodrigo Savoldi, Laura MAHTEP Group Dipartimento Energia "Galileo Ferraris" Politecnico di Torino Torino Italy Department of Civil Construction & Environmental Engineering NC State University RaleighNC United States Graduate Program on Operations Research NC State University RaleighNC United States Department of Economics and Statistics "Cognetti de Martiis" Università degli Studi di Torino Italy School of Business NC Central University DurhamNC United States

To achieve the goal of transforming Europe into a carbon-neutral continent by 2050, it is essential that emission reduction efforts are supported by well-informed decision-making processes and models. This paper proposes a methodology to evaluate the curves of the marginal GHGs abatement cost through energy system optimization models. The analysis is performed by comparing the marginal cost associated with progressively stricter decarbonization pathways from 2020 to 2050 with the emission reduction benefit. Moreover, average sectorial and sub-sectorial abatement costs are computed to assess the role of different sectors and technologies in the decarbonization process. In the light of its international commitment towards GHGs emission reduction, the selected case study is the Italian energy system, being Italy one of the largest emitting countries in Europe. The maximum marginal cost associated with the -90% emission reduction target in 2050 with respect to the reference scenario is approximately 1600 €/t. From our findings, the power sector turns out to be the cheapest sector to decarbonize (~ 170 €/t), while the industrial sector is the most expensive (~ 620 €/t). The values obtained for the abatement cost suggest that a carbon tax around 160 €/t would be sufficient to strongly reduce the emissions from the Italian energy system. © 2024, The Authors. All rights reserved.

关键词： Carbon

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Neural FIM for learning Fisher information metrics from point cloud data

arXiv

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arXiv 2023年

作者： Fasina, Oluwadamilola Huguet, Guillaume Tong, Alexander Zhang, Yanlei Wolf, Guy Nickel, Maximilian Adelstein, Ian Krishnaswamy, Smita Applied Math Program Yale University New HavenCT United States Mila - Quebec AI Institute MontrealQC Canada Department of Mathematics and Statistics Université de Montréal MontrealQC Canada Department of Computer Science and Operations Research Université de Montréal MontrealQC Canada FAIR Meta AI United States Department of Math Yale University New HavenCT United States Department of Computer Science Yale University New HavenCT United States Department of Genetics Yale University New HavenCT United States

Although data diffusion embeddings are ubiquitous in unsupervised learning and have proven to be a viable technique for uncovering the underlying intrinsic geometry of data, diffusion embeddings are inherently limited due to their discrete nature. To this end, we propose neural FIM, a method for computing the Fisher information metric (FIM) from point cloud data - allowing for a continuous manifold model for the data. Neural FIM creates an extensible metric space from discrete point cloud data such that information from the metric can inform us of manifold characteristics such as volume and geodesics. We demonstrate Neural FIM's utility in selecting parameters for the PHATE visualization method as well as its ability to obtain information pertaining to local volume illuminating branching points and cluster centers embeddings of a toy dataset and two single-cell datasets of IPSC reprogramming and PBMCs (immune cells). Copyright © 2023, The Authors. All rights reserved.

关键词： Embeddings

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Using Cluster Analysis and Dynamic programming for Demand Response Applied to Electricity Load in Residential Homes

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Journal of Engineering for Sustainable Buildings and Cities 2020年第1期1卷 011006页

作者： Chanpiwat, Pattanun Gabriel, Steven A. Moglen, Rachel L. Siemann, Michael J. Department of Mechanical Engineering University of Maryland College Park 20742 MD United States Department of Mechanical Engineering and the Applied Mathematics Statistics and Scientific Computation Program University of Maryland College Park 20742 MD United States Department of Industrial Economics and Technology Management Norwegian University of Science and Technology Trondheim NO-7491 Norway Operations Research and Industrial Engineering The University of Texas at Austin Austin 78712 TX United States Resideo Technologies Austin 78702 TX United States

This paper develops means to analyze and cluster residential households into homogeneous groups based on the electricity load. Classifying customers by electricity load profiles is a top priority for retail electric providers (REPs), so they can plan and conduct demand response (DR) effectively. We present a practical method to identify the most DR-profitable customer groups as opposed to tailoring DR programs for each separate household, which may be computationally prohibitive. Electricity load data of 10,000 residential households from 2017 located in Texas was used. The study proposed the clustered load-profile method (CLPM) to classify residential customers based on their electricity load profiles in combination with a dynamic program for DR scheduling to optimize DR profits. The main conclusions are that the proposed approach has an average 2.3% profitability improvement over a business-as-usual heuristic. In addition, the proposed method on average is approximately 70 times faster than running the DR dynamic programming separately for each household. Thus, our method not only is an important application to provide computational business insights for REPs and other power market participants but also enhances resilience for power grid with an advanced DR scheduling tool. Copyright © 2020 by ASME.

关键词： cluster analysis customer classification demand response dynamic programming energy efficiency integrated systems occupant behavior optimization residential household

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Manifold learning with arbitrary norms

arXiv

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arXiv 2020年

作者： Kileel, Joe Moscovich, Amit Zelesko, Nathan Singer, Amit Department of Mathematics Oden Institute University of Texas at Austin United States Department of Statistics and Operations Research Tel-Aviv University Israel Department of Mathematics Brown University United States Department of Mathematics and Program in Applied and Computational Mathematics Princeton University

Manifold learning methods play a prominent role in nonlinear dimensionality reduction and other tasks involving high-dimensional data sets with low intrinsic dimensionality. Many of these methods are graph-based: they associate a vertex with each data point and a weighted edge with each pair. Existing theory shows that the Laplacian matrix of the graph converges to the Laplace-Beltrami operator of the data manifold, under the assumption that the pairwise affinities are based on the Euclidean norm. In this paper, we determine the limiting differential operator for graph Laplacians constructed using any norm. Our proof involves an interplay between the second fundamental form of the manifold and the convex geometry of the given norm's unit ball. To demonstrate the potential benefits of non-Euclidean norms in manifold learning, we consider the task of mapping the motion of large molecules with continuous variability. In a numerical simulation we show that a modified Laplacian eigenmaps algorithm, based on the Earthmover's distance, outperforms the classic Euclidean Laplacian eigenmaps, both in terms of computational cost and the sample size needed to recover the intrinsic geometry. Copyright © 2020, The Authors. All rights reserved.

关键词： Mathematical operators

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The Subtype-Free Average Causal Effect for Heterogeneous Disease Etiology

arXiv

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arXiv 2022年

作者： Sasson, Amit Wang, Molin Ogino, Shuji Nevo, Daniel Department of Statistics and Operations Research Tel Aviv University Tel Aviv Israel Department of Biostatistics Harvard T.H. Chan School of Public Health BostonMA United States Department of Epidemiology Harvard T.H. Chan School of Public Health BostonMA United States Channing Division of Network Medicine Department of Medicine Brigham and Women's Hospital Harvard Medical School BostonMA United States Broad Institute of MIT and Harvard CambridgeMA United States Program in MPE Molecular Pathological Epidemiology Department of Pathology Brigham and Women's Hospital Harvard Medical School BostonMA United States

Studies have shown that the effect an exposure may have on a disease can vary for different subtypes of the same disease. However, existing approaches to estimate and compare these effects largely overlook causality. In this paper, we study the effect smoking may have on having colorectal cancer subtypes defined by a trait known as microsatellite instability (MSI). We use principal stratification to propose an alternative causal estimand, the Subtype-Free Average Causal Effect (SF-ACE). The SF-ACE is the causal effect of the exposure among those who would be free from other disease subtypes under any exposure level. We study non-parametric identification of the SF-ACE, and discuss different monotonicity assumptions, which are more nuanced than in the standard setting. As is often the case with principal stratum effects, the assumptions underlying the identification of the SF-ACE from the data are untestable and can be too strong. Therefore, we also develop sensitivity analysis methods that relax these assumptions. We present three different estimators, including a doubly-robust estimator, for the SF-ACE. We implement our methodology for data from two large cohorts to study the heterogeneity in the causal effect of smoking on colorectal cancer with respect to MSI subtypes. Copyright © 2022, The Authors. All rights reserved.

关键词： Diseases

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On the cross-validation bias due to unsupervised preprocessing

arXiv

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arXiv 2019年

作者： Moscovich, Amit Rosset, Saharon Program in Applied and Computational Mathematics Princeton University PrincetonNJ United States Department of Statistics and Operations Research Tel-Aviv University Tel-Aviv Israel

Cross-validation is the de facto standard for predictive model evaluation and selection. In proper use, it provides an unbiased estimate of a model's predictive performance. However, data sets often undergo various forms of data-dependent preprocessing, such as mean-centering, rescaling, dimensionality reduction, and outlier removal. It is often believed that such preprocessing stages, if done in an unsupervised manner (that does not incorporate the class labels or response values) are generally safe to do prior to cross-validation. In this paper, we study three commonly-practiced preprocessing procedures prior to a regression analysis: (i) variance-based feature selection;(ii) grouping of rare categorical features;and (iii) feature rescaling. We demonstrate that unsupervised preprocessing can, in fact, introduce a substantial bias into cross-validation estimates and potentially hurt model selection. This bias may be either positive or negative and its exact magnitude depends on all the parameters of the problem in an intricate manner. Further research is needed to understand the real-world impact of this bias across different application domains, particularly when dealing with small sample sizes and high-dimensional *** Codes 62-07 Copyright © 2019, The Authors. All rights reserved.

关键词： Regression analysis

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Note on optimal sampling strategy for structural variant detection using optical mapping

arXiv

引用

arXiv 2019年

作者： Li, Weiwei Hannig, Jan Jones, Corbin D. Department of Statistics and Operations Research University of North Carolina Chapel Hill United States Department of Biology and Integrative Program for Biological Genome Sciences University of North Carolina Chapel Hill United States

Structural variants compose the majority of human genetic variation, but are difficult to assess using current genomic sequencing technologies. Optical mapping technologies, which measure the size of chromosomal fragments between labeled markers, offer an alternative approach. As these technologies mature towards becoming clinical tools, there is a need to develop an approach for determining the optimal strategy for sampling biological material in order to detect a variant at some threshold. Here we develop an optimization approach using a simple, yet realistic, model of the genomic mapping process using a hyper-geometric distribution and probabilistic concentration inequalities. Our approach is both computationally and analytically tractable and includes a novel approach to getting tail bounds of hyper-geometric distribution. We show that if a genomic mapping technology can sample most of the chromosomal fragments within a sample, comparatively little biological material is needed to detect a variant at high confidence. Copyright © 2019, The Authors. All rights reserved.

关键词： Probability distributions

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Bayesian change point detection for functional data

arXiv

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arXiv 2018年

作者： Li, Xiuqi Ghosal, Subhashis Operations Research Graduate Program North Carolina State University United States Department of Statistics North Carolina State University United States

We propose a Bayesian method to detect change points for functional data. We extract the features of a sequence of functional data by the discrete wavelet transform (DWT), and treat each sequence of feature independently. We believe there is potentially a change in each feature at possibly different time points. The functional data evolves through such changes throughout the sequences of observations. The change point for this sequence of functional data is the cumulative effect of changes in all features. We assign the features with priors which incorporate the characteristic of the wavelet coefficients. Then we compute the posterior distribution of change point for each sequence of feature, and define a matrix where each entry is a measure of similarity between two functional data in this sequence. We compute the ratio of the mean similarity between groups and within groups for all possible partitions, and the change point is where the ratio reaches the minimum. We demonstrate this method using a dataset on climate change. Copyright © 2018, The Authors. All rights reserved.

关键词： Bayesian networks

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