Emergency Department Efficiency in an Academic Hospital: A Simulation Study

Gemstone Team HOPE (Hospital Optimal Productivity Enterprise)This study examined the effects of the resident education model on the efficiency of a teaching hospital emergency department. Patient data was collected from the University of Maryland Medical Center in Baltimore, MD. This data consisted of both patient information physically collected in the emergency department, as well as historical patient information accessed through the hospital’s electronic databases. Simulation modeling was then used to analyze in a statistically significant manner the effects of the resident education model on patient throughput in the emergency department. We determined that the presence of residents in the ED improves patient throughput for both high-priority and low-priority patients. However, this improvement is higher for lowpriority patients than for high-priority patients, which is a novel result. Future studies will entail determining how replacing residents with other types of personnel, such as nurse practitioners or other types of physicians, affects patient throughput

Analytics for Improved Cancer Screening and Treatment

Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2015.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 139-156).Cancer is a leading cause of death both in the United States and worldwide. In this thesis we use machine learning and optimization to identify effective treatments for advanced cancers and to identify effective screening strategies for detecting early-stage disease. In Part I, we propose a methodology for designing combination drug therapies for advanced cancer, evaluating our approach using advanced gastric cancer. First, we build a database of 414 clinical trials testing chemotherapy regimens for this cancer, extracting information about patient demographics, study characteristics, chemotherapy regimens tested, and outcomes. We use this database to build statistical models to predict trial efficacy and toxicity outcomes. We propose models that use machine learning and optimization to suggest regimens to be tested in Phase II and III clinical trials, evaluating our suggestions with both simulated outcomes and the outcomes of clinical trials testing similar regimens. In Part II, we evaluate how well the methodology from Part I generalizes to advanced breast cancer. We build a database of 1,490 clinical trials testing drug therapies for breast cancer, train statistical models to predict trial efficacy and toxicity outcomes, and suggest combination drug therapies to be tested in Phase II and III studies. In this work we model differences in drug effects based on the receptor status of patients in a clinical trial, and we evaluate whether combining clinical trial databases of different cancers can improve clinical trial toxicity predictions. In Part III, we propose a methodology for decision making when multiple mathematical models have been proposed for a phenomenon of interest, using our approach to identify effective population screening strategies for prostate cancer. We implement three published mathematical models of prostate cancer screening strategy outcomes, using optimization to identify strategies that all models find to be effective.by John Silberholz.Ph. D

What Works Best When? A Systematic Evaluation of Heuristics for Max-Cut and QUBO

Though empirical testing is broadly used to evaluate heuristics, there are shortcomings with how it is often applied in practice. In a systematic review of Max-Cut and Quadratic Unconstrained Binary Optimization (QUBO) heuristics papers, we found only 4% publish source code, only 14% compare heuristics with identical termination criteria, and most experiments are performed with an artificial, homogeneous set of problem instances. To address these limitations, we implement and release as open-source a code-base of 10 MaxCut and 27 QUBO heuristics. We perform heuristic evaluation using cloud computing on a library of 3,296 instances. This large-scale evaluation provides insight into the types of problem instances for which each heuristic performs well or poorly. Because no single heuristic outperforms all others across all problem instances, we use machine learning to predict which heuristic will work best on a previously unseen problem instance, a key question facing practitioners

Clinical benefit, toxicity and cost of metastatic breast cancer therapies: systematic review and meta-analysis

Abstract Purpose Oncologists, clinical trialists, and guideline developers need tools that enable them to efficiently review the settings and results of previous studies testing metastatic breast cancer (MBC) drug therapies. Methods We searched the literature to identify clinical trials testing MBC drug therapies. Key eligibility criteria included at least 90% of patients enrolled in the trial having MBC, therapeutic clinical trials, and Phase II–III studies. Studies were stratified based on patients’ tumor receptor statuses and prior exposure to therapy. Survival and toxicity of each drug therapy were estimated from randomized controlled trials using network meta-analysis and from all studies using meta-analysis. These results, along with estimated drug costs, are presented in a web-based visualization tool. Results We included 1865 studies containing 2676 treatment arms and 184,563 patients in the tool ( http://www.cancertrials.info ). Meta-analysis-based efficacy and toxicity estimates are available for 85 HER-2-directed therapies, 84 hormonal therapies, and 442 undirected therapies. Network meta-analysis-based estimates are available for 16 HER-2-directed therapies, 26 hormonal therapies, and 131 undirected therapies. Conclusions In this era of increasing choices of MBC therapeutic agents and no superior approach to choosing a treatment regimen, the ability to compare multiple therapies based on survival, toxicity and cost would enable treating physicians to optimize therapeutic choices for patients. For investigators, it can point them in research directions that were previously non-obvious and for guideline designers, enable them to efficiently review the MBC clinical trial literature and visualize how regimens compare in the key dimensions of clinical benefit, toxicity, and cost

Clinical benefit, toxicity and cost of metastatic breast cancer therapies: systematic review and meta-analysis

Abstract Purpose Oncologists, clinical trialists, and guideline developers need tools that enable them to efficiently review the settings and results of previous studies testing metastatic breast cancer (MBC) drug therapies. Methods We searched the literature to identify clinical trials testing MBC drug therapies. Key eligibility criteria included at least 90% of patients enrolled in the trial having MBC, therapeutic clinical trials, and Phase II–III studies. Studies were stratified based on patients’ tumor receptor statuses and prior exposure to therapy. Survival and toxicity of each drug therapy were estimated from randomized controlled trials using network meta-analysis and from all studies using meta-analysis. These results, along with estimated drug costs, are presented in a web-based visualization tool. Results We included 1865 studies containing 2676 treatment arms and 184,563 patients in the tool ( http://www.cancertrials.info ). Meta-analysis-based efficacy and toxicity estimates are available for 85 HER-2-directed therapies, 84 hormonal therapies, and 442 undirected therapies. Network meta-analysis-based estimates are available for 16 HER-2-directed therapies, 26 hormonal therapies, and 131 undirected therapies. Conclusions In this era of increasing choices of MBC therapeutic agents and no superior approach to choosing a treatment regimen, the ability to compare multiple therapies based on survival, toxicity and cost would enable treating physicians to optimize therapeutic choices for patients. For investigators, it can point them in research directions that were previously non-obvious and for guideline designers, enable them to efficiently review the MBC clinical trial literature and visualize how regimens compare in the key dimensions of clinical benefit, toxicity, and cost

Optimal healthcare decision making under multiple mathematical models: application in prostate cancer screening

Important decisions related to human health, such as screening strategies for cancer, need to be made without a satisfactory understanding of the underlying biological and other processes. Rather, they are often informed by mathematical models that approximate reality. Often multiple models have been made to study the same phenomenon, which may lead to conflicting decisions. It is natural to seek a decision making process that identifies decisions that all models find to be effective, and we propose such a framework in this work. We apply the framework in prostate cancer screening to identify prostate-specific antigen (PSA)-based strategies that perform well under all considered models. We use heuristic search to identify strategies that trade off between optimizing the average across all models’ assessments and being “conservative” by optimizing the most pessimistic model assessment. We identified three recently published mathematical models that can estimate quality-adjusted life expectancy (QALE) of PSA-based screening strategies and identified 64 strategies that trade off between maximizing the average and the most pessimistic model assessments. All prescribe PSA thresholds that increase with age, and 57 involve biennial screening. Strategies with higher assessments with the pessimistic model start screening later, stop screening earlier, and use higher PSA thresholds at earlier ages. The 64 strategies outperform 22 previously published expert-generated strategies. The 41 most “conservative” ones remained better than no screening with all models in extensive sensitivity analyses. We augment current comparative modeling approaches by identifying strategies that perform well under all models, for various degrees of decision makers’ conservativeness. Keywords: Comparative modeling, Decision analysis, Sensitivity analysis, Model averaging, Optimization, Prostate cancer screening, Simulation modelin

Moneyball for academics: network analysis for predicting research impact

Can we predict the academic impact of scholars and papers at early stages using quantitative tools? Abstract How are scholars ranked for promotion, tenure and honors? How can we improve the quantitative tools available for decision makers when making such decisions? Can we predict the academic impact of scholars and papers at early stages using quantitative tools? Current academic decisions (hiring, tenure, prizes) are mostly very subjective. In the era of “Big Data,” a solid quantitative set of measurements should be used to support this decision process. This paper presents a method for predicting the probability of a paper being in the most cited papers using only data available at the time of publication. We find that highly cited papers have different structural properties and that these centrality measures are associated with increased odds of being in the top percentile of citation count. The paper also presents a method for predicting the future impact of researchers, using information available early in their careers. This model integrates information about changes in a young researcher’s role in the citation network and co-authorship network and demonstrates how this improves predictions of their future impact. These results show that the use of quantitative methods can complement the qualitative decision-making process in academia and improve the prediction of academic impact