Quality improvement relies on the ability to measure process inputs and outputs, analyze them and infer conclusions. Several techniques are common: Data aggregation and grouping, Pareto analysis, simple statistics, Ishikawa (fishbone) diagrams, run charts and quality matrices are all useful in specific situations.
Students completing this course will be able to gather data, manipulate it to be useful, analyze using basic methods and draw conclusions
Quality improvement requires measurement. It is the bedrock upon which all improvement processes are built. A reliable method of evaluation of quality levels is essential before problems can be evaluated and countermeasures designed.
This course investigates several common data types in the healthcare environment, how to find them, extract and perform simple analysis to gather knowledge of the system. Students completing this course will be able to identify data, have examples of typical data types and some pitfalls in collection and analysis