Module Components

• Overview
• Assignments

Topics

• Data
• Poster Planning

NDNP 813 - DNP Project Evaluation and Dissemination

Module 1: Introduction

Data

Quality Improvement looks to show that strategies and tactics worked to bring about measurable change. This change is measured by tracking data over time, commonly referred to as time series analysis. Change from pre-intervention throughout an intervention phase, best illustrated by run chart.

Illustrating Data

The basic anatomy of a run chart includes time on the x-axis (for our purposes weeks) and process or outcome values on the y-axis. Individual data points are plotted in the body of the chart. As data is plotted, a median line is generated which is the measure of central tendency – the median being the point at which half the points are above and half below.

Provide a label for the x and y axis and a clear title for the run chart. Insert comments or notes to illustrate the timeline or when changes in the system occurred. This will allow a visual illustration to interpret the system response

Interpreting Data

There is probability of detecting a true change once a run chart contains a least 14 data points, based on the probability of replication. This replication allows you to determine that a change has occurred. The next question is why the change occurred. Analysis of what occurred in the system (a new process, an unrelated influence, a change in personnel) will offer insight into why the system responded the way it did. Unlike research, Quality Improvement offers little control for confounding variables. When a run, trend, or shift occurs, it is essential to consider the context for the data change to accurately understand why.

Monitoring the system during an improvement initiative is essential to accurate interpretation of the data. A significant change in the data occurs when:

• There is an increasing or decreasing trend of seven or more points, or
• A shift of seven or more points on one side of the median, or
• Too few or too may runs (a run is successive points on one side of the median)
  

Circle any trends, runs, and shifts in the data on your run chart. If one occurs, you can say a change occurred – you have data variation but must understand why. To do this, you must refer to the process of care during the initiative and the context and answer three critical questions:

1. At what point in the run chart was the change initiative introduced?
2. Are you able to say the change initiative was applied consistently week to week?
3. What other changes occurred in the system at the same time as the initiative that may explain why the system responded as it did, favorably or unfavorably?