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Quality and Performance Improvement in Healthcare: Theory, Practice, and Management Sixth Edition

Chapter 5

Aggregating and Analyzing Performance Improvement Data

Objectives

Differentiate between internal and external benchmark comparisons

Identify common healthcare data collection tools

Introduce the concept of data aggregation in support of data analysis

Describe the various data types

Recognize the correct graphic presentation for a specific data type

Design graphic displays for a given set of data

Analyze the data for changes in performance displayed in graphic form

Aggregating and Analyzing PI Data

Sources of data

Surveys

Patient records

Incident reports

Employee performance evaluations

Staff competency results

Internal and external data comparisons or benchmarking must be accomplished

Check Sheets

Used to gather data based on sample observations in order to detect a pattern

The team should consider the four W questions

Who will collect the data?

What data will be collected?

Where will the be collected?

When will the data be collected?

Types of Data

Nominal

Also called categorical data

Include values assigned to name-specific categories

Male or female

Usually displayed on bar graphs or pie charts

Types of Data

Ordinal

Also called ranked data

Expresses the comparative evaluation of various characteristics or entities

Likert scales

Best displayed on bar graphs or pie charts

Types of Data

Discrete or count data

Numerical values that represent whole numbers

Number of children in a family

Number of non-billable patient accounts

Best displayed in bar graphs

Continuous data

Assume an infinite number of possible values

Weight, blood pressure, temperature, and so forth

Best displayed in histograms or line charts

Other Data Analysis Terms

Absolute frequency

The number of times that a score or value occurs in the data set

Relative frequency

The percentage of the time that the characteristic or score appears in the data set

Management of Data Sets

Use of data sets for PI purposes

Examine the nature of the data to be sure they accurately reflect the subject under investigation

Examine the reliability of the data

Correct interpretation of data

Accurate external reporting

Statistical Analysis

Mean—The arithmetic average

Median—The middle number

Skewing—Very high or low values that distort the calculated mean

Standard deviation—Shows the spread of the values

Bar Graphs

Used to display discrete categories of data

Histogram

Bar graph that displays data proportionally

Displays large amounts of continuous data that are difficult to interpret in lists or other nongraphic forms

Shows the relative frequency of the various data categories indirectly using the height of the bars

Shows the distribution of the absolute frequencies of the data in the grouped intervals

Histogram

Pareto Chart

Bar graph that uses data to determine priorities in problem solving

Help the PI team to focus on problems and their causes

Pie Chart

Used to show the relationship of each part to the whole

Line Chart

A simple, plotted chart of data that shows the progress of a process over time

PI Teams can identify trends, shifts, or changes in a process over time

Control Charts

Used to measure key processes over time

Focuses attention on any variation in the process

Helps the PI team determine whether that variation is normal or a result of special circumstances

Control Charts (cont’d)

Common cause variation

Also called normal variation

The expected variance in a process due to the fact that the process will not or cannot be performed in exactly the same manner each and every time

Special cause variation

When a special circumstance or unexpected event occurs in the process

It is this special cause variation that the PI process needs to investigate

Example of a Control Chart