Browsing by Subject "Efficiency"

Now showing 1 - 6 of 6
  • Thumbnail Image
    Item
    Current State of Women in Academic Surgical Subspecialties: How a New Metric in Measuring Academic Productivity May Change the Equation
    (The Southeastern Surgical Congress, 2018-05-01) Fecher, Alison M.; Valsangkar, Nakul; Bell, Teresa M.; Lisy, Megan E.; Rozycki, Grace S.; Koniaris, Leonidas G.; Surgery, School of Medicine
  • Thumbnail Image
    Item
    Efficient Inference and Dominant-Set Based Clustering for Functional Data
    (2024-05) Wang, Xiang; Wang, Honglang; Boukai, Benzion; Tan, Fei; Peng, Hanxiang
    This dissertation addresses three progressively fundamental problems for functional data analysis: (1) To do efficient inference for the functional mean model accounting for within-subject correlation, we propose the refined and bias-corrected empirical likelihood method. (2) To identify functional subjects potentially from different populations, we propose the dominant-set based unsupervised clustering method using the similarity matrix. (3) To learn the similarity matrix from various similarity metrics for functional data clustering, we propose the modularity guided and dominant-set based semi-supervised clustering method. In the first problem, the empirical likelihood method is utilized to do inference for the mean function of functional data by constructing the refined and bias-corrected estimating equation. The proposed estimating equation not only improves efficiency but also enables practically feasible empirical likelihood inference by properly incorporating within-subject correlation, which has not been achieved by previous studies. In the second problem, the dominant-set based unsupervised clustering method is proposed to maximize the within-cluster similarity and applied to functional data with a flexible choice of similarity measures between curves. The proposed unsupervised clustering method is a hierarchical bipartition procedure under the penalized optimization framework with the tuning parameter selected by maximizing the clustering criterion called modularity of the resulting two clusters, which is inspired by the concept of dominant set in graph theory and solved by replicator dynamics in game theory. The advantage offered by this approach is not only robust to imbalanced sizes of groups but also to outliers, which overcomes the limitation of many existing clustering methods. In the third problem, the metric-based semi-supervised clustering method is proposed with similarity metric learned by modularity maximization and followed by the above proposed dominant-set based clustering procedure. Under semi-supervised setting where some clustering memberships are known, the goal is to determine the best linear combination of candidate similarity metrics as the final metric to enhance the clustering performance. Besides the global metric-based algorithm, another algorithm is also proposed to learn individual metrics for each cluster, which permits overlapping membership for the clustering. This is innovatively different from many existing methods. This method is superiorly applicable to functional data with various similarity metrics between functional curves, while also exhibiting robustness to imbalanced sizes of groups, which are intrinsic to the dominant-set based clustering approach. In all three problems, the advantages of the proposed methods are demonstrated through extensive empirical investigations using simulations as well as real data applications.
  • Thumbnail Image
    Item
    Inverse probability weighting for covariate adjustment in randomized studies
    (Wiley, 2014-02) Shen, Changyu; Li, Xiaochun; Li, Lingling; Biostatistics, School of Medicine
    Covariate adjustment in randomized clinical trials has the potential benefit of precision gain. It also has the potential pitfall of reduced objectivity as it opens the possibility of selecting a 'favorable' model that yields strong treatment benefit estimate. Although there is a large volume of statistical literature targeting on the first aspect, realistic solutions to enforce objective inference and improve precision are rare. As a typical randomized trial needs to accommodate many implementation issues beyond statistical considerations, maintaining the objectivity is at least as important as precision gain if not more, particularly from the perspective of the regulatory agencies. In this article, we propose a two-stage estimation procedure based on inverse probability weighting to achieve better precision without compromising objectivity. The procedure is designed in a way such that the covariate adjustment is performed before seeing the outcome, effectively reducing the possibility of selecting a 'favorable' model that yields a strong intervention effect. Both theoretical and numerical properties of the estimation procedure are presented. Application of the proposed method to a real data example is presented.
  • Thumbnail Image
    Item
    Measuring success: perspectives from three optimization programs on assessing impact in the age of burnout
    (Oxford University Press, 2020-12) Lourie, Eli M.; Stevens, Lindsay A.; Webber, Emily C.; Pediatrics, School of Medicine
    Electronic health record (EHR) optimization has been identified as a best practice to reduce burnout and improve user satisfaction; however, measuring success can be challenging. The goal of this manuscript is to describe the limitations of measuring optimizations and opportunities to combine assessments for a more comprehensive evaluation of optimization outcomes. The authors review lessons from 3 U.S. healthcare institutions that presented their experiences and recommendations at the American Medical Informatics Association 2020 Clinical Informatics conference, describing uses and limitations of vendor time-based reports and surveys utilized in optimization programs. Compiling optimization outcomes supports a multi-faceted approach that can produce assessments even as time-based reports and technology change. The authors recommend that objective measures of optimization must be combined with provider and clinician-defined value to provide long term improvements in user satisfaction and reduce EHR-related burnout.
  • Thumbnail Image
    Item
    Physician Productivity and Supervision
    (Department of Emergency Medicine, School of Medicine, University of California, Irvine, 2023-05-09) Schreyer, Kraftin E.; Kuhn, Diane; Norton, Vicki; Emergency Medicine, School of Medicine
  • Thumbnail Image
    Item
    Techniques and Strategies to Optimize Efficiencies in the Office and Operating Room: Getting Through the Patient Backlog and Preserving Hospital Resources
    (Elsevier, 2021) Meneghini, R. Michael; Orthopaedic Surgery, School of Medicine
    The effects of the coronavirus disease 2019 pandemic are pervasive and have decreased the volume of hip and knee arthroplasty procedures since the mandated cessation of elective surgical procedures at the height of the pandemic in early 2020. Therefore, a backlog of patients in need of these elective procedures is a probable consequence and increased productivity and efficiency in patient care delivery is essential now and into the future. This article outlines multiple strategies and techniques to develop and optimize efficiency in the hip and knee arthroplasty practice. Techniques for increasing surgical efficiency are detailed, along with perioperative strategies in the hospital, ambulatory surgery center, and office settings are outlined and discussed.