Clinical epidemiology bridges two worlds: population science and individual patient care.

This chapter explores how epidemiological principles are applied to improve diagnosis, prognosis, and treatment decisions in clinical settings.

We examine:

* Measures of diagnostic accuracy (sensitivity, specificity)

* Predictive values and disease prevalence

* Likelihood ratios

* Risk stratification tools

* Prognostic modelling

* Evidence-based medicine frameworks

Clinical epidemiology recognises that every diagnostic test operates within a probabilistic context. Pre-test probability influences post-test interpretation. A test result never exists in isolation.

The chapter highlights the shift from authority-based medicine to evidence-based practice - integrating best available evidence, clinical expertise, and patient values.

Bias, confounding, and random error remain relevant - but now applied to patient-level decision-making.

Risk communication becomes critical. Clinicians must translate statistical probabilities into meaningful information for patients.

Ultimately, clinical epidemiology ensures that public health evidence informs bedside judgement - without losing sight of individual context.

It is epidemiology personalised.

Key Takeaways

* Clinical epidemiology applies population methods to patient care.

* Sensitivity and specificity measure test performance.

* Predictive values depend on disease prevalence.

* Likelihood ratios refine diagnostic reasoning.

* Risk stratification informs management decisions.

* Evidence-based medicine integrates research and clinical expertise.

* Communication of risk is central to patient care.

* Probabilistic reasoning underpins diagnostic decisions.

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