Research, Evidence-Based Practice, and Quality Improvement
Research, Evidence-Based Practice, and Quality Improvement
This domain tests critical appraisal, evidence hierarchies, measurement, and improvement methods. Items often ask what a study design can establish or which QI measure fits an aim.
Core rules
- Evidence hierarchy: systematic reviews and meta-analyses of RCTs > RCTs > cohort > case-control > cross-sectional > case series. GRADE rates certainty and strength of recommendations.
- Screening statistics: sensitivity and specificity are test properties; positive predictive value depends on prevalence. A high-sensitivity test rules out disease when negative; a high-specificity test rules in when positive.
- Clinical significance differs from statistical significance; inspect confidence intervals and effect size, not p-values alone.
- QI: use the Model for Improvement with aim, measures, and changes; test with PDSA cycles. Track outcome, process, and balancing measures on run charts or control charts.
- Root cause analysis and morbidity reviews identify system failures, not individual blame.
- Equity: stratify measures by race, language, and payer to detect disparities.
Worked example
A clinic aims to increase first-trimester prenatal visits. The team sets an aim, measures the percentage of new patients seen before 14 weeks, tests reminder calls and open-access scheduling in PDSA cycles, and plots weekly data on a run chart. Reasoning: a measurable aim and process measure show whether the change works before spread.
Traps
Confusing sensitivity with PPV; calling a p<0.05 result clinically important; using a single PDSA as proof; blaming individuals instead of systems.