Step 1 :To solve this problem, we need to use the formula for sample size in estimating a population mean: \(n = (Z_{\alpha/2} * \sigma / E)^2\)
Step 2 :Where: \(n\) is the sample size, \(Z_{\alpha/2}\) is the z-score corresponding to the desired confidence level, \(\sigma\) is the standard deviation of the population, and \(E\) is the desired margin of error.
Step 3 :For a 99% confidence level, the z-score (\(Z_{\alpha/2}\)) is approximately 2.576. The standard deviation (\(\sigma\)) is given as 19.3, and the desired margin of error (\(E\)) is 2.
Step 4 :Substituting these values into the formula, we get: \(n = (2.576 * 19.3 / 2)^2\)
Step 5 :Calculating the above expression, we get: \(n = (49.8688)^2\)
Step 6 :Further simplifying, we get: \(n = 2486.88\)
Step 7 :Since we can't have a fraction of a subject, we round up to the nearest whole number. So, the doctor needs a sample size of approximately 2487 subjects for a 99% confidence level.
Step 8 :So, the final answer is \(\boxed{2487}\)