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  • Open (Access) Journal-Article 
    Using Six Sigma DMAIC Methodology and Discrete Event Simulation to Reduce Patient Discharge Time in King Hussein Cancer Center

    Using Six Sigma DMAIC Methodology and Discrete Event Simulation to Reduce Patient Discharge Time in King Hussein Cancer Center

    Short discharge time from hospitals increases both bed availability and patients and families satisfaction. In this study, the Six Sigma process improvement methodology was applied to reduce patients discharge time in a cancer treatment hospital. Data on the duration of all activities, from the physician signing the discharge form to the patient leaving the treatment room, were collected through patient shadowing. These data were analyzed using detailed process maps and cause-and-effect diagrams. Fragmented and unstandardized processes and procedures and a lack of communication among the stakeholders were among the leading causes of long discharge times. Categorizing patients by their needs enabled better design of the discharge processes. Discrete event simulation was utilized as a decision support tool to test the effect of the improvements under different scenarios. Simplified and standardized processes, improved communications, and system-wide management are among the proposed improvements, which reduced patient discharge time by 54 from 216 minutes. Cultivating the necessary ownership through stakeholder analysis is an essential ingredient of sustainable improvement efforts.

    Subjects:
    Health Sciences and Management
    Keywords:
    Hospitals -- Administration Process control Six sigma (Quality control stard)
    Resource Type:
    Open (Access) Journal-Article
     
    OER
  • Courseware 
    15.024 Applied Economics for Managers | Sloan School of Management

    15.024 Applied Economics for Managers | Sloan School of Management

    The fact of scarcity forces individuals, firms, and societies to choose among alternative uses – or allocations – of its limited resources. Accordingly, the first part of this summer course seeks to understand how economists model the choice process of individual consumers and firms, and how markets work to coordinate these choices. It also examines how well markets perform this function using the economist's criterion of market efficiency. Overall, this course focuses on microeconomics, with some topics from macroeconomics and international trade. It emphasizes the integration of theory, data, and judgment in the analysis of corporate decisions and public policy, and in the assessment of changing U.S. and international business environments.

    Subjects:
    Management
    Keywords:
    Microeconomics
    Resource Type:
    Courseware
     
    OER
  • Courseware 
    EPID 757 - Introduction to Systematic Reviews and Meta-Analyses

    EPID 757 - Introduction to Systematic Reviews and Meta-Analyses

    Systematic reviews and meta-analyses are useful for evidence-based clinical and public health practice. The widespread and growing application of systematic review methods for the synthesis of evidence on important or pressing research and clinical questions underscore the need for health-care professionals to understand and critique this research design. This course will provide a detailed description of the systematic review process, discuss the strengths and limitations of the method, and provide step-by-step guidance on how to actually perform a systematic review and meta-analysis. Specific topics to be covered include: formulation of the review question, searching of literature, quality assessment of studies, data extraction, meta-analytic methods, assessment of heterogeneity and report writing. The course will also cover statistical issues such as selection of statistical models for meta-analysis, practical examples of fixed and random effects models, best evidence syntheses (qualitative systematic reviews) as well as examples of methods to evaluate heterogeneity and publication bias. STATA statistical software will be used to perform meta-analysis during the computer lab, along with tutorials on how to effectively use tools such as PubMed for conducting reviews.

    Subjects:
    Statistics and Research Methods
    Keywords:
    Meta-analysis Research -- Methodology Systematic reviews (Medical research)
    Resource Type:
    Courseware
     
  • Courseware 
    Data Analysis: Take it to the MAX

    Data Analysis: Take it to the MAX

    This course is for all of those struggling with data analysis. You will learn: - Overcome data analysis challenges in your work and research - Increase your productivity and make better business decisions - Enhance your data analysis skills using spreadsheets - Learn about advanced spreadsheet possibilities like array formulas and pivottables - Learn about Excel 2013 features like PowerPivot & PowerMap - Learn to organize and test your spreadsheets

    Subjects:
    Computing, Data Science and Artificial Intelligence
    Keywords:
    Python (Computer program language) Microsoft Excel (Computer file) Electronic spreadsheets -- Computer programs Information visualization Visual analytics
    Resource Type:
    Courseware
     
  • Courseware 
    Data Analysis: Visualization and Dashboard Design

    Data Analysis: Visualization and Dashboard Design

    This course covers the main tasks required from data analysts today, including importing, summarizing, interpreting, analyzing and visualizing data. It aims to equip you with the tools that will enable you to be an independent data analyst. Most techniques will be taught in Excel with add-ons and free tools available online. You will learn: - How to make data come to life with well-known types of visualizations such as line and bar graphs and new types of visualizations such as spark lines, contour plots and population pyramids. - How to create dashboards in Excel based on live data that can meet managerial and business needs. - How to connect data from different sources, such as the web and exports from your CRM, ERP, SAP or data warehouse. - Some hands-on data science and how to use actionable analysis tools. - Deep dive into known tools like PivotTables and introduce new ones like the analysis toolpak

    Subjects:
    Computing, Data Science and Artificial Intelligence
    Keywords:
    Microsoft Excel (Computer file) Information visualization Dashboards (Management information systems) Industrial management -- Data processing Visual analytics
    Resource Type:
    Courseware
     
  • Courseware 
    Data Analysis: Building your Own Business Dashboard

    Data Analysis: Building your Own Business Dashboard

    Are you ready to leave the sandbox and go for the real deal? Have you followed Data Analysis: Take It to the MAX() and Data Analysis: Visualization and Dashboard Design and are ready to carry out more robust data analysis? In this project-based course you will engage in a real data analysis project that simulates the complexity and challenges of data analysts at work. Testing, data wrangling, Pivot Tables, sparklines? Now that you have mastered them you are ready to apply them all and carry out an independent data analysis. For your project, you will pick one raw dataset out of several options, which you will turn into a dashboard. You will begin with a business question that is related to the dataset that you choose. The datasets will touch upon different business domains, such as revenue management, call-center management, investment, etc.

    Subjects:
    Computing, Data Science and Artificial Intelligence
    Keywords:
    Industrial management -- Data processing Information visualization Dashboards (Management information systems) Visual analytics
    Resource Type:
    Courseware
     
  • Courseware 
    Statistics 250 - Introduction to Statistics and Data Analysis

    Statistics 250 - Introduction to Statistics and Data Analysis

    Statistics is the science that turns data into information and information into knowledge. This class covers applied statistical methodology from an analysis-of-data viewpoint. Topics covered include frequency distributions; measures of location; mean, median, mode; measures of dispersion; variance; graphic presentation; elementary probability; populations and samples; sampling distributions; one sample univariate inference problems, and two sample problems; categorical data; regression and correlation; and analysis of variance. Use of computers in data analysis is also explored.

    Subjects:
    Mathematics and Statistics
    Keywords:
    Statistics
    Resource Type:
    Courseware
     
  • Courseware 
    Statistics for Brain and Cognitive Science

    Statistics for Brain and Cognitive Science

    Provides students with the basic tools for analyzing experimental data, properly interpreting statistical reports in the literature, and reasoning under uncertain situations. Topics organized around three key theories: Probability, statistical, and the linear model. Probability theory covers axioms of probability, discrete and continuous probability models, law of large numbers, and the Central Limit Theorem. Statistical theory covers estimation, likelihood theory, Bayesian methods, bootstrap and other Monte Carlo methods, as well as hypothesis testing, confidence intervals, elementary design of experiments principles and goodness-of-fit. The linear model theory covers the simple regression model and the analysis of variance. Places equal emphasis on theory, data analyses, and simulation studies.

    Subjects:
    Mathematics and Statistics and Biology
    Keywords:
    Cognitive science Statistics
    Resource Type:
    Courseware