How to write a QI measurement plan: healthcare example

A QI measurement plan sets out what you will measure, how each measure is defined, who will collect the data and how the team will review it. It connects your improvement aim to a small, useful set of outcome, process and balancing measures so you can learn whether changes are helping.

This is the planning stage of our guide to running a successful QI project. Start with an agreed aim statement and involve the people who will collect, interpret and use the data.

What should a QI measurement plan include?

For each measure, record its purpose, operational definition, data source, collection method, sampling approach, frequency, owner and display method. Also agree when you will review the results and what will prompt a response.

A measure name such as “patient experience” is not enough. Specify the question you will ask, the response options, which patients you will invite and how you will report responses. Someone outside the project should be able to follow your instructions and calculate the same measure.

1. Choose outcome, process and balancing measures

Start with the question: “How will we know that a change is an improvement?” The Institute for Healthcare Improvement’s guidance on establishing measures describes three complementary types.

Outcome measures: is the result improving?

Outcome measures track the result that matters to patients, staff or the service. For a project to reduce outpatient waiting, this could be the median time from arrival to the start of the consultation. Link the measure directly to the aim rather than choosing a convenient metric that answers a different question.

Process measures: is the change happening as intended?

Process measures track the steps expected to produce the result. If the team is testing a room-readiness checklist, it could measure the percentage of clinic sessions in which all required preparation is complete before the first appointment.

Balancing measures: are we creating problems elsewhere?

Balancing measures look for unintended consequences. A shorter wait would be less useful if it came with rushed consultations or additional staff overtime. Select measures that reflect plausible trade-offs in your setting, and ask patients and staff what the team might otherwise miss.

There is no single correct number of measures for every project. Choose the smallest set that supports your decisions and covers the important risks. Remove measures that add collection work without helping the team learn.

2. Write an operational definition for every measure

An operational definition explains exactly what counts and how to calculate it. Agree:

• Population and setting: which service, sessions, patients or events are included?
• Inclusions and exclusions: what qualifies, what does not and why?
• Data items and units: which timestamps, categories or values are needed?
• Calculation: for a percentage, define both numerator and denominator; for elapsed time, define the start and end points.
• Exceptions: how will you handle missing, duplicate or implausible records?
• Interpretation: does a higher or lower value represent the intended direction?

Test the definition with two people using the same small set of records. If they reach different answers, refine the instructions before expanding collection. Keep a version date so later changes to the definition are visible.

3. Set a realistic collection and review schedule

Use existing data where it answers your question reliably. Check how it was recorded before assuming a routinely available field means what you need it to mean.

If collecting every eligible event is impractical, describe a repeatable sampling method. Sampling only easy-to-reach patients or quieter shifts can give a misleading picture. Include the conditions that matter to the project and record the limits of your sample.

Choose a frequency that fits the pace of the process, the volume of activity and the decisions you need to make. A weekly clinic may need a point for each session; a busy daily process may need a different approach. Collecting more often is not automatically better if the workload makes the data unreliable.

Name a collection owner and a backup, and agree when the team will review the chart. Build collection into the workflow where possible. For the detailed next steps, see how to collect data for a QI project.

Healthcare example: an outpatient measurement plan

This is an illustrative plan, not a report of achieved results. A team wants to reduce median arrival-to-consultation waiting time from 40 to 30 minutes by 31 March 2027 for adults attending Tuesday morning general outpatient clinics. Its first change idea is a short preparation huddle supported by a room-readiness checklist.

Outcome: median waiting time per clinic session

• Definition: minutes from recorded arrival to the start of the consultation for eligible patients who attend and are seen. Report the median for each session.
• Source: arrival and consultation-start timestamps, after checking how staff enter them.
• Exceptions: exclude cancelled appointments and non-attenders from waiting-time calculation. Record missing timestamps separately rather than treating them as zero.
• Owner and frequency: the agreed data lead extracts and checks data after each Tuesday clinic.
• Review: plot sessions in time order and review alongside patient volume, missing records and notes about unusual conditions.

Process: preparation completed before the first appointment

Define the required checklist items and the deadline. Record a yes/no result for each session. Over an agreed review period, calculate the number of eligible sessions meeting all requirements divided by the total eligible sessions, multiplied by 100. Retain the individual session results so the team can connect preparation to its tests.

Balancing: staff overtime and patient experience

Record staff minutes worked beyond the planned session finish using an agreed definition. Add a brief, consistently asked patient-experience question about whether there was enough time to discuss concerns. Track the number invited and the number responding; voluntary responses may not represent everyone.

These measures help the team consider whether apparent gains in waiting time come with additional workload or a worse consultation experience. They do not, by themselves, prove the change caused the result.

4. Plan a baseline and display data over time

A baseline describes the process before the change. Use comparable historical data if available, or collect initial data using the agreed definitions. Explain any gaps and avoid choosing only an unusually good or bad period.

Plot observations in time order and annotate when tests, staffing changes or other relevant events occur. A run chart can support early review; an appropriate statistical process control chart can support more detailed assessment of variation when the data and analysis meet its requirements. Choose the chart to suit the data, not simply the appearance you prefer.

A single before-and-after average can hide variation and timing. Review the pattern across repeated observations, the process measures, balancing measures and what patients and staff report. Seek improvement or analytical support when selecting charts or interpreting signals.

5. Pilot the plan before relying on it

Try collection during one session or with a small set of records. Check whether the fields exist, the definitions are understood and the work is manageable. Record how long collection takes and what is missed.

Resolve problems, date the revised plan and then collect consistently. If you change a definition later, annotate the chart and assess whether the old and new values remain comparable. Do not silently combine different measures.

QI measurement plan template

Use one row per measure with these headings: measure name; type; link to aim; operational definition; calculation; inclusion and exclusion rules; source; sampling method; collection frequency; owner and backup; missing-data rules; chart; review date; version.

Download the QI measurement plan workbook to create your own plan using an editable Excel template and healthcare example.

Frequently asked questions

What is the difference between a measurement plan and a data collection sheet?

The plan explains what you will measure and why, including definitions and responsibilities. The collection sheet records the individual observations needed to calculate those measures.

How much baseline data do we need?

There is no universal minimum that suits every process and chart. Consider the frequency and variation of the process, the chart you intend to use and the decision at stake. Ask for analytical advice rather than assuming a small sample is automatically statistically reliable.

Can a measurement plan change during a QI project?

Yes. Early tests may show that a measure is unclear or burdensome. Document the change, preserve the earlier definition and check comparability before interpreting the combined series.

Put the plan into practice

Agree the definitions with your team, pilot collection and schedule the first review. Continue with collecting QI data, or explore Simana’s charting tools to see how you can display and review improvement data over time.