Welcome to the topic of continuous quality improvement (CQI)! CQI: Data is a video course meant to show how data can be documented and analyzed for continuous quality improvement in your organization. This video lesson, How Much Day Do You Need?, is the final in a series of five. From it, the viewer will learn how to determine the amount of data to collect for both qualitative and quantitative data sets. For quantitative data, this lesson also shows how to compute and use standard error to determine sample size.
Overview
COURSE DIFFICULTY
COURSE DURATION
6m
Skills Learned
After completing this online training course, students will be able to:
Determine the appropriate amount of data needed for effective quality improvement
Analyze data requirements to support decision-making processes
Implement strategies for data collection and analysis
Evaluate data quality and relevance in continuous improvement initiatives
Quality Improvement Professionals, Data Analysts, Healthcare Administrators, Project Managers
Completion of Continuous Quality Improvement: Data (Part 4 of 5)
01. Overview of Data Requirements in Quality Improvement
02. Methods for Data Collection and Analysis
03. Assessing Data Quality and Relevance
04. Best Practices for Data Utilization in Continuous Improvement
05. Case Studies and Real-World Applications
SKILLS LEARNED
Skills Learned
After completing this online training course, students will be able to:
Determine the appropriate amount of data needed for effective quality improvement
Analyze data requirements to support decision-making processes
Implement strategies for data collection and analysis
Evaluate data quality and relevance in continuous improvement initiatives
WHO SHOULD ATTEND
Quality Improvement Professionals, Data Analysts, Healthcare Administrators, Project Managers
PREREQUISITES
Completion of Continuous Quality Improvement: Data (Part 4 of 5)
COURSE OUTLINE
01. Overview of Data Requirements in Quality Improvement
02. Methods for Data Collection and Analysis
03. Assessing Data Quality and Relevance
04. Best Practices for Data Utilization in Continuous Improvement
05. Case Studies and Real-World Applications
