Ethics, Professionalism, Patient Safety, and Surgical Education
This domain tests lawful consent, safe systems and the ability to appraise evidence. Questions are often the 'best answer' type where more than one option is clinically reasonable but only one is ethically or methodologically correct.
Consent and communication
- Valid consent requires capacity, adequate information and voluntariness, obtained by someone able to perform or fully describe the procedure.
- The Montgomery standard (2015) requires disclosure of material risks a reasonable patient would want to know, not simply what a reasonable doctor would disclose.
- Document the discussion, including alternatives and the option of no treatment. In a life-threatening emergency with no capacity, treat under the doctrine of necessity.
- Breaking bad news: prepare, establish what the patient knows, invite questions, give information in chunks, respond to emotion, summarise and plan (SPIKES framework).
Patient safety and quality improvement
- The WHO checklist reduces mortality and complications; sign in, time out and sign out are separate steps.
- Never events include wrong-site surgery, retained swabs and wrong implants; they trigger formal investigation.
- Human factors - fatigue, hierarchy, poor communication - cause most surgical error. SBAR structures handover.
- Open disclosure after harm is an ethical duty; incident reporting should be blame-free.
- Audit compares practice with a standard and closes the loop with re-audit; the PDSA cycle drives improvement.
Research methods and statistics
- Study designs: randomised controlled trial (best for intervention), cohort, case-control (good for rare outcomes), cross-sectional survey.
- Sensitivity is the proportion of diseased patients correctly identified; specificity the proportion of healthy patients correctly identified. Positive predictive value depends on prevalence.
- Number needed to treat is the reciprocal of absolute risk reduction.
- A p-value is the probability of the observed result if the null hypothesis were true - not the probability that the null is true.
- Confidence intervals convey precision; GRADE and levels of evidence rank certainty.
Worked example
A test has sensitivity 90% and specificity 95% in a population with 10% prevalence. Of 1,000 patients, 100 have disease and 90 are detected; of 900 without disease, 45 are falsely positive. PPV = 90/(90+45) = 67%. The same test in a 1% prevalence population would have a PPV near 15% - a reminder that sensitivity and specificity are stable, but predictive values are not.
Traps that lose marks
Treating consent as a signature; saying p = 0.05 means a 5% chance the null is true; confusing sensitivity with PPV; ignoring prevalence when interpreting a test.