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Continuous Quality Improvement in Healthcare
Healthcare Organization and Management · Chapter 12: Quality Improvement and Process Excellence in HealthcareSection 12.1 · lesson 69 of 10510 min · free with a free account
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In this lesson
Testing changes with PDSA cycles, clear aims, and run charts.
- Distinguish quality assurance, quality control, and quality improvement
- Apply plan, do, study, act cycles with a written prediction and small tests of change
- Write an aim statement and choose process, outcome, and balancing measures
- Interpret a run chart to tell common cause from special cause variation
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Transcript
Everything said in the video, section by section (1,394 words). Read along, or search it.
Opening Case
A Monday Launch for Teach-Back
Ellen Murphy, charge nurse on 4 North, has a good idea. Patients going home on new medications get their teaching in the rush of discharge day, and many leave confused. Her plan: do teach-back the day before, when there's time to ask the patient to explain each medicine in their own words. Her team agrees. She plans to start it across the whole unit on Monday. Farah Siddiqui, director of quality, asks two questions. What do you predict will happen? And how will you know it worked?
Definitions
Assurance, Control, and Improvement Differ
Farah starts with a distinction. The word quality covers three different activities. Quality assurance checks performance against a standard, and acts when it falls short. Its output is a judgment: audit, inspection, external review. Quality control watches a process while it runs, to catch departures early. Its output is a signal. Quality improvement changes a process so its ordinary performance is better. Its output is a new way of working. A unit meeting its standard can still have plenty to improve, because a standard is a floor. And more auditing applied to an improvement problem just finds the same problem again.
PDSA Cycle
Plan, Do, Study, Act With a Prediction
The basic unit of improvement work is the cycle: plan, do, study, act. It grew from Walter Shewhart's work at Bell Labs, and W. Edwards Deming made it famous. Plan states the change, who does what, and the predicted result. Do runs the test and records what happened, including what went wrong. Study compares the result with the prediction. Act decides whether to adopt, adapt, or abandon the change, and sets up the next cycle. The prediction is what separates this from trial and error. A result that misses it is even more valuable, because it shows your theory of the process was wrong. Skip the prediction, and you can always explain the result afterward, so you learn nothing you didn't already believe.
Model for Improvement
Three Questions in the Right Order
Before any cycle, three questions frame the work. They come from the Model for Improvement, developed by Associates in Process Improvement and taught by the Institute for Healthcare Improvement. 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 order matters, and teams usually reverse it. They arrive with a change someone wants, then work backward to justify it. Answering the first two first stops a unit from solving a problem it hasn't defined and can't measure. Ellen realizes she has only answered the third.
Aim Statement
A Measure, a Number, a Boundary, a Date
So Ellen writes an aim statement: what the effort will accomplish, with a measure, a numeric target, a boundary, and a date. 'Improve discharge teaching' is a topic. Her aim: raise the share of 4 North patients on new medications who complete teach-back before discharge day, from about forty percent to ninety percent, by June thirtieth. The number surfaces disagreement early. A team split between seventy and ninety-five percent, has an argument about ambition that would otherwise erupt in month four. And the date forces small tests instead of endless design.
Three Measures
Process, Outcome, and Balancing Measures
Every improvement needs three kinds of measure. A process measure shows whether the change is actually being done: the share of patients whose teach-back started the day before. An outcome measure shows whether what you care about improved: patients who can explain their medicines on the follow-up call two days after going home. And a balancing measure shows whether something else got worse: nurse time per patient, and discharge delays. Process measures move first, so watch them weekly. Balancing measures are the ones most often left out, and they separate a serious effort from an enthusiastic one.
Quick Check
What a Pain Reassessment Aim Lacks
Quick check. Danny Reyes, a new nurse on 4 North, drafts his own aim: improve pain reassessment on 4 North by March thirty-first. What is it missing? Pause, and decide. Something to count, and how much. It has a boundary, 4 North, and a date. But nothing says what will be counted, or how much better counts as success. A better aim: raise reassessments done within an hour of a pain medicine dose from sixty to ninety percent, on 4 North, by March thirty-first.
Small Tests
Two Patients on One Day Shift
Then Ellen does the opposite of a Monday launch. Danny tests the change with two patients, on one day shift. A one-nurse, one-day test costs almost nothing, so it runs this week. It surfaces the practical obstacles no design anticipates. And it fails safely, on one shift instead of a unit. Danny predicted ten minutes per patient. It took twenty, because the pharmacy's discharge medication list usually isn't ready the day before. That's exactly the information the team needed, found cheaply. They adapt: pharmacy agrees to send the list by two p.m. Then one nurse for a week, then a shift, then the unit, each step decided on the evidence.
Run Charts
Common Cause Versus Special Cause Variation
To read the results, the team uses a run chart: the measure plotted in time order, with the median drawn across it. It separates two kinds of variation, an idea that also goes back to Shewhart. Common cause variation is the ordinary wobble of a stable process. Special cause variation means something actually happened. Confuse them, and you pay either way. Chase every bad week, and you waste attention and may destabilize the process. Dismiss a genuine shift, and you miss the signal worth acting on. Comparing this month with last month can never tell them apart. One widely used rule: six or more points in a row on one side of the median signal a shift.
Quick Check
Seven Points Above the Median
Quick check. The team made its change in week ten. Weeks eleven through seventeen all land above the median. Week fourteen was lower than week thirteen, and someone is worried. What does the chart show? Pause, and decide. The new level is holding. Seven points in a row above the median is too unlikely to be ordinary wobble. The dip in week fourteen is the tempting distraction. Comparing two neighboring points can't separate signal from noise, and the dip stays above the median anyway.
Common Failures
Most Changes Are Not Improvements
Every improvement requires a change, but most changes aren't improvements. That's why measurement is the work, not paperwork. Three failures recur. The change was launched but never adopted, so the process measure never moved. The change was adopted, but the outcome didn't follow, so the theory linking them was wrong. That's valuable, if the team will say so. Or the outcome improved while a balancing measure got worse, so the problem was moved, not solved.
Spread and Sustainment
Testing Locally and Holding the Gain
Success on one unit raises two new problems. Spread is other units adopting a proven change. Sustainment is the change lasting after the project ends. Spread fails when a unit is handed a conclusion instead of a test. What works: invite 4 South to test it locally and adapt it, trading some standardization for real adoption. Sustainment fails because the attention holding the change was temporary. Structures hold it instead: standard work, a named owner after the team disbands, and lighter but continued measurement.
Four Absences
Protected Time Underpins Improvement Work
Most improvement work fails from one of four absences. No aim, so it can't be finished. No measure, so nobody knows if anything changed. No test, so everyone meets the obstacles at once. No owner, so the work belongs to a meeting. Underneath all four is something only a manager can provide: protected time. Priya Nair, the chief nursing officer, gives Ellen and two nurses one hour a week, with their shifts covered. Work squeezed in between patients is done by whoever's least busy, and it stops in the first short-staffed week.
Case Resolved
Five Small Tests and a Real Shift
Back to Ellen. There was no Monday launch. In the first month, her team ran five small tests, and each one changed the design. The pharmacy timing problem surfaced with two patients, not forty. And her balancing measure caught a new one: teach-back was spilling into evening medication rounds, so they moved it to mid-afternoon. By week seventeen, the run chart shows a real shift. She isn't at ninety percent yet, and she knows exactly what the next test is. So Farah's two questions get answers. Ellen predicted, and she knows, because she measured.
Key Takeaways
Improvement Habits Worth Keeping
To recap. Assurance judges against a standard; improvement redesigns the process. Run plan, do, study, act cycles, and always write the prediction down. An aim has a measure, a number, a boundary, and a date. Track process, outcome, and balancing measures. Test small, then read a run chart, not this month against last.
About Healthcare Organization and Management at UPI Study
Healthcare Organization and Management (Health 302) is a self-paced online course of 105 video lessons in 18 chapters, from health systems and the law to ethics, leadership, teams, strategy, informatics, finance, quality, human resources, marketing and evidence-based practice. It carries a NCCRS credit recommendation for 3 semester credits.
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About Continuous Quality Improvement in Healthcare
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How do you read a run chart to tell whether a change worked?
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