UK Medical Licensing Assessment (UKMLA) Professional Knowledge, Ethics and the Practising Doctor Flashcards

24 sample cards from the Professional Knowledge, Ethics and the Practising Doctor deck

Sampled from the end of the deck, so these are different cards from the ones shown on the syllabus page.

1. What are the seven pillars (components) of clinical governance?

   Clinical effectiveness/research, audit, risk management, education and training, patient/public involvement, using information/IT, and staffing & staff management. (It is the framework through which organisations are accountable for continuously improving quality of care.)

2. Describe the clinical audit cycle.

   A quality improvement cycle: 1) Identify problem and set standards/criteria; 2) Measure current practice against the standard; 3) Compare results with the standard; 4) Implement change; 5) Re-audit to close the loop and confirm improvement. It compares practice against an explicit standard.

3. Contrast clinical audit with research.

   Research creates new generalisable knowledge ('what should we do?'), may involve randomisation/experimentation, and needs ethics committee approval. Audit measures whether current practice meets an already-established standard ('are we doing what we should?'), changes nothing experimentally, and does not require ethics approval.

4. What is the Plan-Do-Study-Act (PDSA) cycle in quality improvement?

   A small-scale iterative method for testing change: Plan (the change and predictions), Do (test it on a small scale and collect data), Study (analyse results vs prediction), Act (adopt, adapt or abandon). Multiple ramped PDSA cycles build evidence for scaling up a successful change.

5. What is a 'never event' in patient safety?

   A serious, largely preventable patient safety incident that should not occur if available preventative measures are implemented (e.g. wrong-site surgery, retained foreign object post-procedure, wrong-route administration of medication). They require investigation and reporting.

6. Name common categories of prescribing/medication error and a key system safeguard against them.

   Errors include wrong drug, dose, route, frequency, patient, omission, and interactions/allergies. Safeguards: electronic prescribing with decision support, the 'five rights' (right patient, drug, dose, route, time), allergy documentation, double-checking high-risk drugs, and reconciliation at transitions of care.

7. Rank the hierarchy of evidence from strongest to weakest study designs.

   From top to bottom: Systematic reviews/meta-analyses of RCTs > Randomised controlled trials > Cohort studies > Case-control studies > Cross-sectional studies > Case series/case reports > Expert opinion/background information.

8. Contrast cohort and case-control study designs.

   Cohort: prospective, groups defined by exposure status and followed to measure incidence of outcome; yields relative risk; good for rare exposures, poor for rare outcomes. Case-control: retrospective, groups defined by outcome (cases vs controls) and compared for past exposure; yields odds ratio; good for rare outcomes/long latency, prone to recall and selection bias.

9. Define sensitivity, specificity, and write the formulas.

   Sensitivity = proportion of true positives correctly identified: $\text{Sens} = \frac{TP}{TP+FN}$. Specificity = proportion of true negatives correctly identified: $\text{Spec} = \frac{TN}{TN+FP}$. A highly sensitive test rules out (SnNOut); a highly specific test rules in (SpPIn).

10. Define positive and negative predictive value and state how they depend on prevalence.

    $PPV = \frac{TP}{TP+FP}$ (probability disease present given a positive test); $NPV = \frac{TN}{TN+FN}$ (probability disease absent given a negative test). Unlike sensitivity/specificity, predictive values depend on disease prevalence: as prevalence falls, PPV falls and NPV rises.

11. Define relative risk, absolute risk reduction, and number needed to treat.

    Relative risk $RR = \frac{\text{risk in exposed}}{\text{risk in unexposed}}$. Absolute risk reduction $ARR = |\text{risk}_{control} - \text{risk}_{treatment}|$. Number needed to treat $NNT = \frac{1}{ARR}$ — the number of patients treated to prevent one additional adverse outcome.

12. What does a p-value represent and what is the conventional significance threshold?

    The p-value is the probability of obtaining results at least as extreme as those observed if the null hypothesis were true. Conventionally $p < 0.05$ is deemed statistically significant. It does not measure effect size or the probability that the hypothesis is true.

13. Interpret a 95% confidence interval, including for a ratio measure.

    A 95% CI is the range within which the true population value is expected to lie 95% of the time on repeated sampling; narrower intervals indicate greater precision. For a ratio (RR or OR), if the CI crosses 1 the result is not statistically significant; for a difference, if it crosses 0 it is not significant.

14. Distinguish Type I and Type II statistical errors.

    Type I error (α, false positive): rejecting a true null hypothesis — concluding an effect exists when it does not. Type II error (β, false negative): failing to reject a false null hypothesis — missing a real effect. Statistical power $= 1 - \beta$, the probability of detecting a true effect.

15. State the Wilson and Jungner principles for a screening programme.

    Key criteria: the condition is an important health problem with a recognisable latent/early stage and understood natural history; there is an acceptable, safe, valid test; an effective, accepted treatment exists with agreed policy on whom to treat; facilities for diagnosis/treatment are available; and screening is cost-effective and a continuing process.

16. Define lead-time and length-time bias in screening.

    Lead-time bias: screening detects disease earlier, so survival time from diagnosis appears longer even if death occurs at the same time (no true benefit). Length-time bias: screening preferentially detects slow-growing, less aggressive disease (which spends longer in the detectable phase), overstating the apparent benefit.

17. What are the requirements for valid research ethics and governance in the UK?

    Voluntary informed consent, favourable Research Ethics Committee (REC) approval, Health Research Authority (HRA) approval, adherence to Good Clinical Practice and the Declaration of Helsinki, independent scientific review, data protection, and provisions for participant safety, confidentiality and the right to withdraw.

18. What four principles underpin the ethics of human research participation (per the Declaration of Helsinki/Belmont)?

    Respect for persons (informed, voluntary consent and protection of those with reduced autonomy), beneficence and non-maleficence (favourable risk-benefit balance), and justice (fair selection of participants and equitable distribution of burdens/benefits). The wellbeing of the participant takes precedence over the interests of science and society.

19. Distinguish incidence from prevalence and give their relationship.

    Incidence: number of NEW cases arising in a population at risk over a time period (measures risk). Prevalence: total number of EXISTING cases at a point/period (measures burden). Relationship: $\text{Prevalence} \approx \text{Incidence} \times \text{average duration of disease}$.

20. Define the basic reproduction number $R_0$ and the herd immunity threshold.

    $R_0$ is the average number of secondary cases produced by one infectious case in a fully susceptible population. An epidemic spreads if $R_0 > 1$. The herd immunity threshold (proportion that must be immune to halt spread) is $1 - \frac{1}{R_0}$.

21. Distinguish the main categories of bias and confounding in epidemiology.

    Selection bias: systematic error in how participants are chosen/retained. Information (measurement) bias: error in measuring exposure/outcome (e.g. recall, observer bias). Confounding: a third factor associated with both exposure and outcome that distorts the apparent relationship (controlled by randomisation, restriction, matching, stratification or multivariable adjustment).

22. Name the stages of the Transtheoretical (Stages of Change) model of behaviour change.

    Precontemplation, Contemplation, Preparation, Action, and Maintenance (with relapse as a possible part of the cycle). Interventions should be tailored to the patient's current stage.

23. Describe the determinants of health using the Dahlgren-Whitehead 'rainbow' model.

    Layered influences on health: 1) fixed individual factors (age, sex, genetics) at the core; surrounded by 2) individual lifestyle factors; 3) social and community networks; 4) living and working conditions (housing, education, employment, healthcare, food); and 5) general socioeconomic, cultural and environmental conditions.

24. What is the concept of 'planetary health' / environmental determinants of health, with an example relevant to medicine?

    Planetary health recognises that human health depends on the health of natural systems; climate change and environmental degradation are major threats (heat-related illness, vector-borne disease spread, air-pollution respiratory/cardiovascular disease, food/water insecurity). The healthcare sector is also a significant carbon emitter, motivating sustainable ('green') clinical practice.

What this deck covers

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