Quality Improvement and Patient Safety
What this domain tests
This domain asks how care systems fail and how they are measurably improved: the frameworks used to describe quality, the tools for analyzing incidents, and the measurement discipline that distinguishes real improvement from random variation.
Core rules
- Donabedian frames quality as structure (resources), process (what is done), and outcome (what results). Process measures tell you whether the change is working; outcome measures tell you whether it matters.
- The six aims of health care quality are safe, effective, patient-centered, timely, efficient, and equitable.
- The Model for Improvement uses three questions (what are we trying to accomplish, how will we know a change is an improvement, what changes can we make) followed by small Plan-Do-Study-Act cycles, testing before scaling.
- Measurement needs a clear aim, a family of measures (outcome, process, balancing), and analysis over time. Run charts and statistical process control distinguish special-cause from common-cause variation; comparing two averages hides the pattern.
- Root cause analysis is retrospective and used after a serious event; failure mode and effects analysis is prospective and used before a process change.
- Safety thinking assumes error is expected: the Swiss cheese model of stacked defenses, human factors design, standardization, checklists, and bundles such as the central line bundle.
- Just culture separates human error (console), at-risk behavior (coach), and reckless behavior (sanction). Never events such as wrong-site surgery or retained instruments require disclosure and reporting.
- Handoffs and medication reconciliation are high-yield failure points; structured tools such as SBAR and I-PASS reduce omission.
Worked example
A unit wants fewer central line bloodstream infections. Reasoning: choose the bundled process measures (hand hygiene, full barrier precautions, chlorhexidine skin antisepsis, optimal site selection, daily review of line necessity) and plot monthly infection rates on a run chart rather than comparing two quarterly averages. A sustained shift of several consecutive points after the intervention suggests special-cause improvement; a balancing measure such as insertion complications confirms no new harm. The intervention is then standardized and spread.
Traps that cost marks
- Blaming an individual for a system failure.
- Using a before-and-after average to declare improvement without a run chart.
- Confusing root cause analysis with failure mode and effects analysis.
- Measuring only outcomes and being unable to see whether the change was delivered.
- Scaling a change nationally before testing it locally.