PDSA Experiment Planner & Run-Chart Template

PDSA Experiment Planner & Run-Chart Template

Purpose: Run faster, rigorous frontline experiments that produce clear learning. This tool helps you plan a focused PDSA (Plan-Do-Study-Act), collect a small-but-useful set of data, and track change over time using a run chart. Use this worksheet for short improvement cycles (days to a few weeks) and scale up when you have reliable signals.

How to use this tool

1. Clarify the problem and pick one measurable outcome to change.
2. Write a concise hypothesis using the template below.
3. Plan the PDSA: who, what, where, when, and how you will measure.
4. Collect data using the small-sample form each time the process runs.
5. Each week, update the run chart, annotate changes, and use the weekly checklist to decide next steps.

Quick PDSA Hypothesis Template (fill in)

We believe that [describe change] for [which patients/process population] will lead to [specific measurable improvement] measured by [measure, unit] within [timeframe] because [rationale].

Example: We believe implementing a standard triage checklist for same-day clinic calls will reduce average patient waiting time from call to scheduled appointment by 15 minutes within 4 weeks because checklists reduce triage variability.

PDSA Plan (use this as a checklist)

• Plan: What exactly are we testing? Define change idea and steps.
• Do: Who will carry out the test, when, and where? Small scale, short duration.
• Study: What data will we collect? How often? Who will review?
• Act: Based on results, will we adapt, adopt, or abandon the change?

Small-sample Data Collection Form (repeat for each data point)

Collect only what you need. Record each event or aggregated daily/shift measure depending on context.

Weekly Review Checklist

• Have we collected the planned data each day/shift?
• Is the run chart updated with the latest points and median line?
• Are there any obvious process or measurement problems (missing data, inconsistent definitions)?
• Is the observed change consistent with our hypothesis?
• Do we need to adjust the intervention, the measurement, or the scale?
• Decision: Continue same test / Modify test / Stop and try something else / Adopt more widely
• Document actions and update the annotation on the run chart for transparency.

Run-Chart Template & Instructions

A run chart displays your measure over time. It helps you see whether a change produced a signal worth acting on. Steps to build a run chart:

1. Plot time on the horizontal axis (consistent intervals: daily, shift, weekly) and the measured value on the vertical axis.
2. Include at least 10 data points before relying on statistical rules; fewer points still provide useful visual learning for quick PDSAs.
3. Draw the median line using baseline data (preferably the first 7–14 points if available).
4. Add annotations for test start, changes, or events (label who changed what and when).

Simple Run-Chart Interpretation Rules (practical frontline rules)

• Shift: Six or more consecutive points all above or all below the median suggest a non-random change.
• Trend: Five or more consecutive points steadily increasing or decreasing suggest a trend.
• Runs: Count runs across the median; too few or too many runs can indicate non-randomness (useful when you have 10+ points).
• Outliers: Single extreme points should be investigated—were they process errors, measurement errors, or real effects?
• Use these rules as signals, not proof. Combine rule signals with operational knowledge before deciding to scale.

Annotation Best Practices

• Label each change test (e.g., Test #1 — standard triage checklist started 2026-01-08).
• Note who implemented the change and any contextual events (staffing changes, EHR downtime).
• When you adapt the test, add a new annotation rather than overwriting old notes.

Practical tips for healthcare teams

• Keep measures simple and objective (time, count, proportion) and define them clearly (what counts as a data point?).
• Train data collectors briefly and use a short written data definition to reduce measurement error.
• Start with small tests — a single clinic, one shift, or a single practitioner — then scale thoughtfully when you see reliable improvement.
• Prefer frequent, small-sample data collection over rare large audits when testing quick changes.

When to use a control chart instead

Run charts are excellent for early learning and quick PDSAs. Switch to control charts (statistical process control) when you: want formal control limits, have larger sample sizes, or plan to sustain and monitor a stable process over time. Consider consulting a quality analyst for control-chart setup.

Next steps & conversion to interactive experiment

This HTML worksheet is ready to use as a printable or digital guide. For better capture and team reporting, convert this into an interactive experiment form that:

• collects each data point via structured fields,
• stores submissions as JSON for audit/history, and
• auto-renders an up-to-date run chart with annotations.

See CapabilityEnhancementNotes for implementation ideas and optional packagings (toolkit for frontline PDSAs).