Research, Evidence-Based Practice, and Quality Improvement
What to know
This domain integrates research literacy, evidence appraisal, and quality improvement. Understand study designs (RCT, cohort, case-control, cross-sectional, systematic review/meta-analysis), their strengths and biases, and the hierarchy of evidence. Know key statistics conceptually: sensitivity, specificity, positive/negative predictive value, relative risk, odds ratio, confidence intervals, and p-values. Be able to appraise validity, applicability, and levels of evidence. Understand evidence-based practice as the integration of best evidence, clinical expertise, and patient values. Know quality improvement frameworks (PDSA cycles), quality indicators, and the difference between research and QI.
Traps
Questions often test interpretation of predictive values-PPV depends on prevalence, while sensitivity/specificity do not. Watch for confusing relative risk (cohort) with odds ratio (case-control). Another trap is treating a statistically significant result as clinically important; effect size and confidence intervals matter.
Rule of thumb
For any evidence question, ask: What design? What bias? What effect size with what confidence interval? Does it apply to my patient? For a diagnostic test, remember PPV/NPV shift with prevalence.
Worked example
A screening test with high sensitivity but low specificity in a low-prevalence population will have many false positives and a low PPV. The stem asking "what happens to PPV when prevalence falls?"-answer: PPV decreases.