A quality improvement project in nursing tests a change in a real care setting and measures whether it leads to improvement, usually through rapid, small cycles. It is the most common format for MSN and DNP projects.
This guide explains how QI differs from research and evidence-based practice, the main improvement models, how to write an aim and choose measures, how to run PDSA cycles and read run charts, and how to report the project.
A Nursing QI Project in Seven Steps
- Define the problem with local baseline data.
- Understand the process, for example with a process map or fishbone diagram.
- Write a SMART aim.
- Choose outcome, process and balancing measures.
- Select change ideas grounded in evidence.
- Test them in PDSA cycles, tracking data over time.
- Plan to sustain and spread what works, and report it.
QI, Research and Evidence-Based Practice
| Aspect | Quality improvement | Evidence-based practice project | Research |
|---|---|---|---|
| Main question | Does this change improve care here? | What does the best evidence say, and how do we apply it? | What is true, generally? |
| Design | Iterative tests of change | Appraisal, then implementation | Fixed protocol |
| Data | Small, frequent samples over time | Varies; often pre and post | Planned sample, often large |
| Generalization | Local learning | Local adoption of existing evidence | Intended to generalize |
In practice, many DNP projects blend EBP and QI: evidence selects the change, and QI methods test and refine it. Whether your project counts as QI or research for ethics purposes is decided by your institution, not by the label you give it. Most require a formal determination before you collect data.
Improvement Models
| Model | Core idea | Good fit when |
|---|---|---|
| IHI Model for Improvement | Three questions plus PDSA cycles | Most unit-level nursing projects |
| FOCUS-PDCA | Find, Organize, Clarify, Understand, Select, then Plan-Do-Check-Act | Organizations that use this framework already |
| Lean | Remove waste and delays from processes | Flow problems, such as discharge delays or supply hunting |
| Six Sigma (DMAIC) | Define, Measure, Analyze, Improve, Control to reduce variation | Data-rich processes with measurable defects |
The Model for Improvement
Developed by Associates in Process Improvement and widely promoted by the Institute for Healthcare Improvement, it asks three questions. What are we trying to accomplish? How will we know that a change is an improvement? What change can we make that will result in improvement? PDSA cycles then test the answers. Its simplicity makes it the default for many nursing programs.
Understanding the Problem
Before choosing a change, understand why the problem happens. Jumping to a solution, often staff education alone, is the most common reason QI projects show little effect.
- Process map: draw each step as it really happens, not as policy says.
- Fishbone diagram: group causes under headings such as people, equipment, methods and environment.
- Five whys: ask "why" repeatedly to move from symptom to root cause.
- Pareto chart: find the few causes behind most of the problem.
Writing a SMART Aim
A good aim is specific, measurable, achievable, relevant and time-bound. It says how much, by when, for whom.
Weak aim. "Improve pain reassessment on the surgical unit."
SMART aim. "Increase the percentage of post-operative patients with documented pain reassessment within 60 minutes of an analgesic dose from a baseline of 54 percent to 80 percent on Ward 7 by the end of a 12-week project period."
Set the target from your baseline and the evidence, and agree it with unit leaders. An ambitious but plausible target motivates; an impossible one discourages staff.
Choosing Measures
| Measure | Purpose | Example for the pain aim |
|---|---|---|
| Outcome | Shows whether patients or the system improved | Patient-reported pain scores at reassessment |
| Process | Shows whether the change is happening | Percentage of doses with timely reassessment documented |
| Balancing | Catches unintended harm | Nurse-reported time pressure or missed other tasks |
Define each measure precisely: numerator, denominator, data source, sampling and frequency. Weekly data points work well for a run chart over a typical project period.
Testing Changes with PDSA Cycles
A PDSA cycle tests one change on a small scale, learns from it and adapts. Start very small, such as one nurse for one shift, then widen the test as confidence grows.
- Plan: state the change, a prediction and how you will collect data.
- Do: carry out the test and note problems and observations.
- Study: compare results with your prediction.
- Act: adopt, adapt or abandon the change, and plan the next cycle.
Example cycles. Cycle 1: one nurse uses a reassessment reminder sticker on the medication record for one shift; she reports it is easy but sometimes covered by other labels. Cycle 2: the sticker moves to the top of the record, tested by four nurses over three days; reassessment documentation rises in the sample. Cycle 3: the reminder is added to the electronic record alert, tested across all day shifts for two weeks.
Reading Run Charts
A run chart plots your measure over time with the median as a center line, and annotates when changes were made. It shows whether improvement is genuine and sustained rather than a one-off fluctuation.
Commonly used rules for a non-random signal include a shift of six or more consecutive points above or below the median, a trend of five or more points all rising or all falling, too few or too many runs, and an unusually distant single point. With more data, statistical process control charts add control limits.
Building the Team and Engaging Staff
QI is a team activity. A project led by one student with no frontline involvement rarely survives the first busy week. Recruit a small team early: a unit leader who can remove barriers, two or three staff nurses who do the work every day and, where relevant, colleagues from pharmacy, medicine or IT.
Share data openly. A run chart on the staff room wall, updated weekly, keeps the project visible and lets staff see the effect of their own work. Ask frontline nurses for change ideas; they often know exactly where a process breaks.
Common QI Project Mistakes
| Mistake | Better approach |
|---|---|
| Education as the only intervention | Pair it with system changes such as reminders, checklists or redesigned workflows |
| No baseline data | Collect several weeks of data before the first change |
| Only two data points, before and after | Track the measure weekly on a run chart |
| Scaling up after one test | Widen tests gradually across cycles |
| Ignoring balancing measures | Watch for unintended effects from the start |
Sustaining and Reporting the Project
Build the change into routine: update policy, the electronic record, orientation and audit schedules, and name a unit owner. Plan how measurement continues after your project ends.
The SQUIRE 2.0 guidelines are widely used for writing up QI work and are often requested by journals and programs. Report honestly, including cycles that failed and what you learned from them.
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Frequently Asked Questions
A project that tests changes to care in a specific setting and measures whether they lead to improvement, usually through small, rapid cycles such as PDSA.
QI aims to improve care locally through iterative testing; research aims to produce generalizable knowledge through a fixed protocol. Your institution decides how a project is classified for ethics.
The IHI Model for Improvement with PDSA cycles suits most nursing projects. Lean suits flow problems and Six Sigma suits data-rich processes with measurable defects. Follow your program's preference.
Plan, Do, Study, Act: a small test of change in which you predict, test, compare results with your prediction and decide whether to adopt, adapt or abandon the change.
State the measure, baseline, target, population and time frame, such as raising timely reassessment from 54 to 80 percent on one ward in 12 weeks.
Outcome, process and balancing measures, each precisely defined with a data source and collection frequency.
Plot data over time on a run chart, mark when changes were made and apply run chart rules to judge whether the change is a real signal.
Many institutions require a formal determination even for QI. Check with your university and clinical site before collecting any data.
It depends on your program and the measure. Many student projects collect several weeks of baseline data, then test changes over 8 to 12 weeks, which gives enough weekly points to read a run chart.