The diagnostic two by two table
Every test result falls into one of four cells: true positive, false positive, false negative, or true negative. Sensitivity is true positives divided by all who actually have the disease, that is TP over (TP plus FN). Specificity is true negatives divided by all who are actually disease free, TN over (TN plus FP). One reads down the diseased column, the other down the healthy column.
Because they condition on true disease status, sensitivity and specificity stay stable across populations with different prevalence, unlike predictive values.
Using the two metrics to rule in or rule out
A common mnemonic is that a highly sensitive test, when negative, helps rule a condition out, while a highly specific test, when positive, helps rule it in. Few tests excel at both, which is why sensitivity and specificity are reported as a pair rather than a single accuracy figure.
This is general educational information, not medical advice. How a result should change management depends on the clinical setting and is a matter for a qualified clinician.