Six Key Steps For Any Quality Improvement Project
1. Opportunity / problem identification and desired
outcome
The opportunity or problem statement is a brief, clear
description of the issue to be studied. Ideally this will be identified through previously
collected data. The opportunity statement must be specific, and describe an observable,
measurable, and manageable issue. The scope should be clearly defined and addressable in a
short time frame, i.e., several weeks to a few months. The desired outcome is the
specific, measurable objective of the project. The opportunity statement and desired
outcome should reflect input from all areas impacted by the
proposed study.
2. Identify most likely cause(s) through data
The cause(s) of a problem may be identified by reviewing
relevant existing data, collecting baseline data on several items thought to be most
likely causes of the problem, and/or by best guesses of those individuals with the most
knowledge of the issue.
3. Identify potential solution(s) and data needed for
evaluation
Utilizing the most-likely causes identified in step 2, list
potential solutions that may result in the desired outcome(s). Such solutions may be based
on experience of others, published reports, and/or best guesses of those with knowledge of
the issue. Following this, choose one or more solutions that can be reasonable instituted.
For each solution to be implemented identify those data
elements required to determine whether or not the change(s) produced the improvement
desired. Data collected should be the absolute minimum and of importance to
patients and to the people doing the work. Once the required data elements have been
specified, the source of these data must be identified or developed.
4. Implement solution(s) and collect data needed for
evaluation
The solution(s) most likely to be successful should be
implemented. It is often preferable to do this on a small scale to see if the change(s)
will work.
Make the data collection easy enough and the time frames
short enough so that data collection can be repeated frequently to allow for trending of
changes over time. Avoid collecting data for improvement and research purposes
simultaneously, as the time frames and amount of data required are dramatically
different. If not already available, build in baseline measures before implementing change
so that it will be possible to measure whether an improvement has been produced. Finally,
provide training, appropriate tools, and examples to those who will be collecting the data
before data collection occurs.
5. Analyze data and develop conclusions
The objective of data analysis is to test your theory
regarding whether or not the change(s) made has led to the desired outcome. It is
essential that both the data elements and the anticipated analysis be planned before
changes are implemented. This will often require the advice of a statistical
consultant, a service offered by the Center for Clinical Effectiveness. In the absence of
a reasonable statistical analysis of carefully collected data, it is often impossible to
determine whether or not the tested solution has produced the improvement desired.
6. Recommendation for further study / action
Action in this step depends upon the results of data
analysis. If the tested solution was shown to produce the desired change, one may wish to
more broadly implement if the initial test was done on a small scale. Effectively
communicating the results of the test as well as rewarding those involved in the
improvement is important. Finally, one should decided whether or not it is important to
continue collecting data to monitor whether the observed improvement is sustained over
time.
If the tested solution did not achieve the desired
improvement, those with knowledge of the process should meet to try to determine why
success was not achieved, i.e. return to Step 2 of this process and then repeat the cycle
to test other potential solutions to the problem at hand.
