Foundations of Healthcare Data for Quality Improvement
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Foundations of Healthcare Data for Quality Improvement
This course is part of The Health Care Data Guide Specialization
Instructor: Wiley Skills Network
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What you'll learn
Learn to define quality improvement goals and select appropriate data measures.
Understand the basics of data collection, sampling, and using run charts.
Apply data analysis techniques to identify and address variations in healthcare settings.
Skills you'll gain
Tools you'll learn
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June 2026
4 assignments
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There are 4 modules in this course
This course introduces healthcare professionals to the foundational principles of healthcare data analysis aimed at improving clinical outcomes. Youβll learn how to collect, analyze, and interpret data to make informed decisions that directly impact quality improvement in healthcare settings.
By understanding how to define improvement goals, select relevant measures, and use various data types, youβll be equipped to contribute to data-driven initiatives. This course covers the basics of data collection, operational definitions, sampling, and how to use run charts to identify and understand variations in clinical settings. Unlike traditional courses, this one combines theoretical knowledge with real-world applications to ensure that the tools and techniques you learn are practical and actionable. The course is structured to provide hands-on examples and interactive quizzes, ensuring you gain the skills to initiate and evaluate improvement efforts effectively. Healthcare professionals looking to improve their data literacy will benefit from this course. While no advanced experience is required, basic knowledge of healthcare operations and data is helpful. This course is part one 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 Β© 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.
This module introduces the Model for Improvement and the PDSA cycle, guiding learners through setting clear aims, using data for measurement, and designing effective tests for change. Participants will gain practical skills in applying improvement methodologies to health care processes and analyzing results to drive sustainable improvements.
What's included
1 video7 readings1 assignment
1 videoβ’Total 1 minute
- Overviewβ’1 minute
7 readingsβ’Total 45 minutes
- Introductionβ’4 minutes
- What Are We Trying to Accomplish?β’5 minutes
- How Will We Know That a Change Is an Improvement?β’5 minutes
- THE PDSA CYCLE FOR IMPROVEMENTβ’7 minutes
- Tools and Methods to Support the Model for Improvementβ’4 minutes
- Designing PDSA Cycles for Testing Changesβ’9 minutes
- Analysis of Data from PDSA Cyclesβ’11 minutes
1 assignmentβ’Total 16 minutes
- Improvement Methodology Fundamentalsβ’16 minutes
This module introduces the foundational concepts of using data to drive improvement in healthcare settings. Learners will explore different types of data, the creation of meaningful measures, and the importance of operational definitions, sampling, and data stratification. Practical strategies for analyzing and presenting data to inform quality improvement efforts are also covered.
What's included
1 video12 readings1 assignment
1 videoβ’Total 1 minute
- Overviewβ’1 minute
12 readingsβ’Total 90 minutes
- Introductionβ’4 minutes
- How Are Data Used?β’13 minutes
- Types of Dataβ’14 minutes
- USING A FAMILY OF MEASURESβ’6 minutes
- THE IMPORTANCE OF OPERATIONAL DEFINITIONSβ’5 minutes
- EXHIBIT 2.1 OPERATIONAL DEFINITION ASPIRIN AT ARRIVALβ’8 minutes
- SAMPLINGβ’10 minutes
- What About Sample Size?β’6 minutes
- STRATIFICATION OF DATAβ’6 minutes
- TRANSFORMING DATAβ’5 minutes
- ANALYSIS AND PRESENTATION OF DATAβ’11 minutes
- SUMMARYβ’2 minutes
1 assignmentβ’Total 16 minutes
- Data-Driven Improvement Fundamentalsβ’16 minutes
This module introduces the use of run charts to visually analyze process data, detect signals of change, and support improvement initiatives. Learners will explore practical and statistical methods for interpreting run charts, including stratification and the use of cumulative sum statistics. By the end, participants will be able to effectively display, interpret, and draw actionable insights from small data sets.
What's included
1 video7 readings1 assignment
1 videoβ’Total 1 minute
- Overviewβ’1 minute
7 readingsβ’Total 70 minutes
- INTRODUCTIONβ’4 minutes
- USE OF A RUN CHARTβ’11 minutes
- Examples of Run Charts for Improvement Projectsβ’5 minutes
- Rules to Aid in Interpreting Run Chartsβ’15 minutes
- SPECIAL ISSUES IN USING RUN CHARTSβ’24 minutes
- Stratification with Run Chartsβ’4 minutes
- Using the Cumulative Sum Statistic with Run Chartsβ’7 minutes
1 assignmentβ’Total 16 minutes
- Analyzing Variation Through Run Chartsβ’16 minutes
This module introduces visual tools such as run charts and Shewhart charts to help you interpret variation in process data. You will learn how to set and revise chart limits, annotate charts for effective learning, and distinguish between different types of variation to guide improvement strategies.
What's included
1 video7 readings1 assignment
1 videoβ’Total 1 minute
- Overviewβ’1 minute
7 readingsβ’Total 61 minutes
- Introductionβ’13 minutes
- INTRODUCTION TO SHEWHART CHARTSβ’11 minutes
- DEPICTING AND INTERPRETING VARIATION USING SHEWHART CHARTSβ’9 minutes
- THE ROLE OF ANNOTATION WITH SHEWHART CHARTSβ’8 minutes
- REVISING LIMITS FOR SHEWHART CHARTSβ’11 minutes
- SHEWHART CHARTS AND TARGETS, GOALS, OR OTHER SPECIFICATIONSβ’5 minutes
- SPECIAL CAUSE: IS IT GOOD OR BAD?β’4 minutes
1 assignmentβ’Total 16 minutes
- Analyzing Process Stability and Variationβ’16 minutes
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The University of Sydney
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