**Core Concept**
The physician's continued use of the same diagnostic test for a disease with a reduced prevalence affects the test's characteristics, particularly its sensitivity and specificity. This phenomenon is known as the "prevalence effect" or "Bayes' theorem," which highlights the impact of disease prevalence on test performance.

**Why the Correct Answer is Right**
When the prevalence of a disease decreases, the sensitivity of the test increases. This is because the test is now less likely to produce false positives, as there are fewer true cases of the disease to begin with. Conversely, the specificity of the test remains relatively unchanged, but the positive predictive value (PPV) increases, making the test more effective at ruling in true cases of the disease.

**Why Each Wrong Option is Incorrect**
**Option A:** This option is incorrect because specificity remains relatively unchanged, not decreased. The test's ability to correctly identify non-diseased individuals is not significantly affected by changes in disease prevalence.
**Option B:** This option is incorrect because the test's negative predictive value (NPV) may actually decrease, not increase. With fewer true cases of the disease, the test is less effective at ruling out false negatives.
**Option C:** This option is incorrect because the test's diagnostic accuracy does not necessarily improve with reduced disease prevalence. The test's characteristics are affected, but not necessarily in a straightforward manner.

**Clinical Pearl / High-Yield Fact**
Remember that disease prevalence affects test performance, particularly sensitivity and positive predictive value. This is crucial when interpreting test results, especially in settings where disease prevalence is changing.

**Correct Answer:** A.