Health Administration · Quality and Safety

Quality Improvement

On this page 9 sections

In 30 seconds
Why this matters
The college version
Eli explains
Worked example
Key takeaway
Quick check
Study tools
Sources & references

In 30 seconds

Quality improvement is the disciplined method healthcare organizations use to make care processes work better. Teams pick an aim, choose measures, and test small changes with Plan-Do-Study-Act cycles, tracking results over time. strips waste from workflows and reduces variation, while run charts and control charts separate ordinary process noise from real, attributable change. The point is learning by doing, not one big redesign.

Why this matters

Every hospital, clinic, and health plan runs on processes that can fail quietly: delayed test results, missed medications, long waits. Quality improvement gives administrators a repeatable way to fix those processes and prove the fix worked, rather than relying on exhortation or a single audit. Students who understand the , PDSA testing, Lean, Six Sigma, and time-series measurement can read a run chart, design a small test, and tell a real improvement from random fluctuation. These methods underlie accreditation expectations, value-based payment, and daily operational management, so the vocabulary appears throughout later coursework and in nearly every healthcare management role.

The college version

Improvement as a method, not a slogan

is the systematic, data-driven work of redesigning how care is delivered so that outcomes, safety, timeliness, and efficiency get measurably better. It is prospective and iterative: teams change a process, watch what happens, and adjust, rather than waiting for an adverse event and investigating it after the fact. That prospective stance is what separates QI from root cause analysis, which is a retrospective investigation of a specific failure. QI also differs from simply measuring quality. Defining quality and picking the right metrics is one job; using a repeatable engine to move those metrics is another, and that engine is what this lesson covers. The most widely taught engine in healthcare is the Model for Improvement, developed by Associates in Process Improvement and spread through the Institute for Healthcare Improvement (IHI). It frames every project with 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? The first sets a clear, measurable aim; the second forces a measurement plan; the third generates concrete changes to test.

The PDSA cycle and small tests of change

The Model for Improvement drives change through the Plan-Do-Study-Act (PDSA) cycle. In Plan, the team predicts what will happen and decides how to try a change on a small scale. In Do, they run the test in the real setting, often with one patient, one nurse, or one day. In Study, they compare results against the prediction. In Act, they adopt, adapt, or abandon the change and set up the next cycle. The power comes from keeping tests small and fast: a change tested on one clinic afternoon costs little if it fails and teaches quickly if it works, so teams run many linked cycles before rolling anything out system-wide. PDSA is the scientific method adapted for action. Its lineage runs back to Walter Shewhart's 1939 specification-production-inspection cycle and W. Edwards Deming's 1950 cycle; the Japanese Union of Scientists and Engineers turned it into the Plan-Do-Check-Act (PDCA) form in 1951, and Deming later favored 'Study' over 'Check' because he wanted teams to analyze results, not merely inspect them. In healthcare the PDSA name is standard, but PDCA in industry is the same idea.

Lean and Six Sigma

Two improvement traditions from manufacturing are now common in healthcare. Lean, rooted in the Toyota Production System, defines value from the customer's point of view and removes everything that does not add value: waiting, unnecessary motion, rework, overproduction, and other 'waste'. A signature Lean tool is value stream mapping, which draws every step in a flow of care, from a patient's arrival to discharge, so a team can see and cut the non-value-adding steps; 5S workplace organization is another. Six Sigma comes from a different angle: it attacks defects and unwanted variation using statistical analysis, following the roadmap: Define the problem, Measure current performance, Analyze root causes, Improve the process, and Control the gains. Its name reflects a demanding target: a true six-sigma process is expected to be 99.99966% defect-free. Many organizations combine the two as Lean Six Sigma, using Lean to remove waste and speed flow while Six Sigma tightens consistency. All three approaches, the Model for Improvement, Lean, and Six Sigma, share a commitment to defining aims, measuring, testing changes, and standardizing what works; they differ mainly in emphasis and toolkit.

Variation and measurement over time

The heart of QI measurement is telling signal from noise. Every process varies. is the ordinary, inherent fluctuation of a stable process, the routine ups and downs you expect week to week. comes from an attributable, non-routine cause, a signal that something genuinely changed. Confusing the two is expensive: reacting to common-cause noise as if it were a signal ('tampering') can make a process worse, while ignoring a real special cause misses a problem or an improvement. QI teams separate the two by plotting data over time rather than comparing two static averages. A run chart plots measurements in time order against the median; simple rules flag non-random patterns, such as a shift of six or more consecutive points all above or all below the median. A control chart, or Shewhart chart, adds a centerline and statistically derived upper and lower control limits; points beyond the limits or specified patterns indicate special-cause variation. Walter Shewhart introduced these charts in the 1920s. This time-series view is why QI answers 'did it get better?' more credibly than a single before-and-after snapshot: it shows whether a change produced a sustained, non-random shift.

Eli explains

The same idea, in plain words

Explain it like I’m 10

Imagine you want your morning routine to get you out the door on time. Instead of changing everything at once, you try one small tweak, like laying out clothes the night before, for a few days and write down when you actually leave. If it helps, you keep it and try the next tweak; if it doesn't, you drop it. Quality improvement in hospitals works the same way: pick a goal, decide how you'll measure it, test a small change, and look at the numbers over many days to see if things really got better or if it was just a lucky morning.

Picture it like this

It's like a video game speedrun. You don't rewrite the whole run at once. You test one shortcut, watch the timer over several attempts, keep the tricks that reliably shave seconds, and toss the ones that only worked once by luck.

Where the picture stops working

A speedrun has one player chasing a single clock in a fixed game. Healthcare processes involve many people, changing patients, and safety limits, so a 'shortcut' has to be safe and repeatable for everyone, and you can't simply reset and retry a real patient's care.

Worked example

A unit wants to cut central-line bloodstream infections. Baseline: 12 infections over 4,000 central-line days. Rate = 12 / 4,000 x 1,000 = 3.00 infections per 1,000 line-days. The team runs several PDSA cycles on an insertion checklist and daily line review. After: 5 infections over 4,500 line-days = 5 / 4,500 x 1,000 = 1.11 per 1,000 line-days. Absolute reduction = 3.00 - 1.11 = 1.89 per 1,000; relative reduction = (3.00 - 1.11) / 3.00 = 63%. Rates, not raw counts, are compared because the denominators differ (4,000 vs 4,500 line-days). To be confident this is real improvement and not common-cause noise, the team plots monthly rates on a run chart and looks for a sustained shift, six or more consecutive points below the old median, rather than trusting a single two-point drop.

Key takeaway

Quality improvement is a repeatable engine: set a clear aim, decide how you'll measure it, test small changes with PDSA (borrowing Lean's focus on waste and Six Sigma's focus on variation), and track results over time so you can tell a real, sustained improvement from ordinary process noise.

Quick check

3 questions here, of 5 in this lesson’s practice set. Answers stay hidden until you check.

Question 1 of 3foundational

The Model for Improvement frames every project with three questions. Which set correctly lists them?

Choose an answer, then check it.

Question 2 of 3intermediate

Why do improvement teams test changes on a very small scale (for example, one patient or one shift) in a PDSA cycle?

Choose an answer, then check it.

Question 3 of 3intermediate

A stable clinic's daily wait times drift up and down within their usual range with no unusual pattern. How should this variation be classified?

Choose an answer, then check it.

Practice all 5

Keep learning

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Practice this lesson

Study tools & related lessonsYou’ll learn to · Common mistakes · Easily confused · Key vocabulary · Related

You’ll learn to

Explain the Model for Improvement and its three guiding questions.
Describe the Plan-Do-Study-Act cycle and the logic of small tests of change.
Distinguish Lean (waste reduction) from Six Sigma (variation reduction) and explain Lean Six Sigma.
Distinguish common-cause from special-cause variation and identify when a run or control chart signals real change.
Apply a before/after rate calculation to judge whether a process improved.

Common mistakes

Treating quality improvement and root cause analysis as the same thing.
QI is prospective and iterative, testing changes to make a process better; root cause analysis is a retrospective investigation of a specific failure after it happens.
Judging improvement by comparing two data points, before and after.
A two-point comparison can't tell signal from noise. Plot data over time on a run or control chart and look for a non-random pattern, such as a sustained shift.
Reacting to every up-and-down as if something changed.
Ordinary common-cause variation is expected. Responding to noise as though it were a special cause (tampering) can make a stable process worse.
Thinking Lean and Six Sigma are interchangeable labels.
Lean targets waste and flow (Toyota roots); Six Sigma targets defects and variation via DMAIC. Lean Six Sigma deliberately combines both.
Planning one large change and rolling it out system-wide immediately.
The Model for Improvement relies on small, fast PDSA tests so failures are cheap and learning accumulates before broad implementation.

Easily confused

Lean vs. Six Sigma

Lean removes waste and improves flow; Six Sigma reduces defects and variation with statistical tools and the DMAIC roadmap.

Common-cause variation vs. Special-cause variation

Common-cause is inherent, expected noise in a stable process; special-cause is an attributable signal that something genuinely changed.

Run chart vs. Control (Shewhart) chart

A run chart plots data against the median with simple pattern rules; a control chart adds statistical upper and lower limits to flag special-cause variation.