Overview

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This course teaches healthcare professionals how to apply data analysis to enhance leadership decision-making and patient care. Through the study of Shewhart charts, patient feedback, and epidemic data, learners will develop the skills to interpret and act on data insights to improve both patient outcomes and organizational performance. The course will help learners master the use of Shewhart charts for individual patient data, understand how patient feedback can guide care improvements, and explore specialized applications like epidemic monitoring. By focusing on real-world case studies and practical techniques, this course prepares professionals to apply data analysis effectively in clinical and leadership contexts. What sets this course apart is its focus on applying advanced data analysis techniques directly to healthcare leadership and patient care. With a mix of theory and practical applications, you will be able to translate data insights into impactful quality improvement initiatives. Healthcare leaders, managers, and professionals in patient care who want to use data-driven strategies to improve their organizations will benefit most from this course. A foundational understanding of healthcare data is helpful but not necessary. This course is part three of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization. Copyright © 2022 John Wiley & Sons, Inc. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.

Syllabus

Learning from Individual Patient Data

This module introduces the use of run charts and Shewhart charts to interpret individual patient data, supporting improved clinical decisions and patient self-management. Learners will review real-world examples to understand how visual data analysis can reveal important health trends and variations.

Learning from Patient Feedback to Improve Care

This module guides learners through the process of collecting, summarizing, and analyzing patient feedback data to inform quality improvement in healthcare settings. Participants will explore both quantitative and qualitative methods, including the use of Shewhart charts and small multiples, and address challenges in interpreting patient satisfaction and experience data. Practical examples illustrate how feedback can drive meaningful changes in care delivery.

Using Shewhart Charts in Health Care Leadership

This module introduces health care leaders to the use of Shewhart charts and balanced vectors of measures (VOMs) for system-level organizational learning and improvement. Learners will discover how to select, display, and interpret multiple measures to guide strategic decision-making and optimize performance across complex health care systems.

Shewhart Charts for Epidemic Data

This module introduces the use of Shewhart charts to analyze epidemic data, with a focus on distinguishing between common and special cause variation for informed public health decisions. Learners will explore real-world examples from the COVID-19 pandemic and address challenges such as day-of-the-week reporting effects. By the end, participants will be equipped to interpret and adjust Shewhart charts for epidemic monitoring.

Case Studies

This module guides learners through real-world healthcare improvement case studies, demonstrating how to use Shewhart charts and related methodologies to analyze variation and drive process improvements. Learners will interpret different chart types, apply rational subgrouping, and make data-driven decisions to address challenges such as access, infection rates, and telemedicine outcomes.