Membership of the Royal College of General Practitioners (MRCGP) Applied Knowledge Test (AKT): Evidence, Administration and Health Informatics Syllabus

Applied Knowledge Test (AKT): Evidence, Administration and Health Informatics syllabus — full chapter and topic list

Expand any chapter to see its topics and sub-topics. This is the whole examinable outline for Applied Knowledge Test (AKT): Evidence, Administration and Health Informatics in Membership of the Royal College of General Practitioners (MRCGP), not a summary of it.

1. 1

   Critical Appraisal and Evidence-Based Practice

   6 topics

   • Study design hierarchy and bias
     • RCTs, cohort, case-control and cross-sectional studies
     • Confounding, selection and measurement bias
   • Statistical concepts and tests
     • P-values, confidence intervals and statistical power
     • Parametric vs non-parametric tests
   • Diagnostic test performance
     • Sensitivity, specificity, predictive values
     • Likelihood ratios and ROC curves
   • Treatment effect measures
     • Relative and absolute risk reduction
     • Number needed to treat and harm
   • Systematic reviews, meta-analysis and forest plots
   • Screening principles and Wilson-Jungner criteria
2. 2

   Health Informatics and Data in Primary Care

   5 topics

   • Reading and interpreting forest plots and graphs
   • Epidemiological measures
     • Incidence, prevalence and mortality rates
     • Standardised mortality ratios
   • Coding, audit and clinical data systems
   • Quality improvement methodology
     • Audit cycle and PDSA cycles
     • Significant event analysis
   • Confidentiality and data protection (GDPR)
3. 3

   Practice Organisation and Regulatory Frameworks

   6 topics

   • Fitness to work and sickness certification (Med3/fit notes)
   • Death certification and cremation processes
     • Medical certificate of cause of death
     • Referral to the coroner and medical examiner
   • DVLA fitness to drive standards
   • Benefits, disability and capacity-for-work assessments
   • NHS structure, commissioning and contracts
     • GP contract and QOF
     • Primary care networks
   • Notifiable diseases and public health reporting
4. 4

   Prescribing, Pharmacology and Therapeutics

   5 topics

   • Pharmacokinetics and dosing in special populations
     • Prescribing in renal and hepatic impairment
     • Prescribing in pregnancy and breastfeeding
   • Drug interactions and adverse drug reactions
   • Controlled drugs and safe opioid prescribing
   • Polypharmacy and deprescribing in older adults
   • Therapeutic monitoring and yellow card reporting

Applied Knowledge Test (AKT): Evidence, Administration and Health Informatics flashcards for Membership of the Royal College of General Practitioners (MRCGP)

19 of 58 cards from the Applied Knowledge Test (AKT): Evidence, Administration and Health Informatics deck — real questions with worked answers.

1. Rank the main study designs from strongest to weakest in the evidence hierarchy.

   From strongest to weakest: systematic reviews/meta-analyses of RCTs > individual RCTs > cohort studies > case-control studies > cross-sectional studies > case series/case reports > expert opinion.

2. What is selection bias, and give a classic example?

   Selection bias is a systematic error from how participants are recruited or retained, so the sample is not representative. Example: healthy worker effect, where employed people are healthier than the general population, distorting occupational study results.

3. Define recall bias and the study design most prone to it.

   Recall bias is differential accuracy of memory between groups (e.g. cases recall exposures more readily than controls). It most affects case-control studies, which rely on retrospective exposure recall.

4. What is a confounder, and name three ways to control for it.

   A confounder is a variable associated with both the exposure and the outcome that is not on the causal pathway. Control methods: at design stage by randomisation, restriction, or matching; at analysis stage by stratification or multivariable regression.

5. Differentiate the null hypothesis and the alternative hypothesis.

   The null hypothesis ($H_{0}$) states there is no difference or no association. The alternative hypothesis ($H_{1}$) states there is a real difference or association. A study seeks to reject $H_{0}$.

6. Define the p-value and the conventional significance threshold.

   The p-value is the probability of obtaining results at least as extreme as observed if the null hypothesis were true. Conventionally $p < 0.05$ is taken as statistically significant.

7. Distinguish a Type I from a Type II error.

   A Type I error ($\alpha$) is rejecting a true null hypothesis (false positive). A Type II error ($\beta$) is failing to reject a false null hypothesis (false negative). Statistical power $= 1 - \beta$.

8. What does a 95% confidence interval represent, and when is a difference/ratio significant?

   A 95% CI is the range within which the true population value lies with 95% confidence. A difference is significant if its CI excludes $0$; a ratio (RR/OR/HR) is significant if its CI excludes $1$.

9. Which statistical test compares means between two independent normally distributed groups?

   The unpaired (independent) Student's $t$-test. For more than two groups use ANOVA; for paired/repeated measures use the paired $t$-test.

10. Which test is used to compare proportions or categorical data in a contingency table?

    The chi-squared ($\chi^{2}$) test. Fisher's exact test is used instead when expected cell counts are small (typically $< 5$).

11. When should a non-parametric test (e.g. Mann-Whitney U) be used instead of a t-test?

    When data are not normally distributed, are ordinal, or have small sample sizes. The Mann-Whitney U test is the non-parametric equivalent of the unpaired $t$-test; the Wilcoxon signed-rank test replaces the paired $t$-test.

12. Define sensitivity and give its formula.

    Sensitivity is the proportion of people with the disease who test positive (true positive rate): $$\text{Sensitivity} = \frac{TP}{TP + FN}$$ A highly sensitive test, when negative, rules out disease (SnNOut).

13. Define specificity and give its formula.

    Specificity is the proportion of people without the disease who test negative (true negative rate): $$\text{Specificity} = \frac{TN}{TN + FP}$$ A highly specific test, when positive, rules in disease (SpPIn).

14. Give the formulas for positive and negative predictive value.

    $$PPV = \frac{TP}{TP + FP}, \qquad NPV = \frac{TN}{TN + FN}$$ Predictive values depend on disease prevalence, unlike sensitivity and specificity.

15. What are likelihood ratios, and why are they clinically useful?

    $$LR^{+} = \frac{\text{sensitivity}}{1-\text{specificity}}, \qquad LR^{-} = \frac{1-\text{sensitivity}}{\text{specificity}}$$ They are prevalence-independent and convert pre-test to post-test odds. $LR^{+} > 10$ or $LR^{-} < 0.1$ give large, often conclusive shifts in probability.

16. What does the area under a ROC curve indicate?

    The ROC curve plots sensitivity against $1-\text{specificity}$ across thresholds. The area under the curve (AUC) measures overall discriminative ability: $0.5$ = no better than chance, $1.0$ = perfect discrimination.

17. Define absolute risk reduction (ARR) and relative risk reduction (RRR).

    $$ARR = \text{CER} - \text{EER}, \qquad RRR = \frac{\text{CER} - \text{EER}}{\text{CER}}$$ where CER is control event rate and EER is experimental event rate. RRR often appears larger and can be misleading without the ARR.

18. Define number needed to treat (NNT) and how it is calculated.

    NNT is the number of patients who must be treated for one to benefit: $$NNT = \frac{1}{ARR}$$ Always round up to the next whole number. The number needed to harm (NNH) uses absolute risk increase.

19. Compare relative risk and odds ratio, including which study design uses each.

    Relative risk (RR) = risk in exposed / risk in unexposed, used in cohort studies and RCTs. Odds ratio (OR) = odds in exposed / odds in unexposed, used in case-control studies. OR approximates RR when the outcome is rare.

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