  Regular tuberculosis (TB) screening is required for healthcare employees
since they can come into contact with infected patients. TB is a serious,
contagious, and potentially deadly disease. Early detection of the disease,
even when it is in latent form, prevents the spread of the disease and helps
with treatment. Currently, there are two types of TB diagnostic tests on the
market: skin test and blood test. The cost of the blood test is much higher
than the skin test. However, the possibility of getting a false positive or
false negative result in skin test is higher especially for persons with
specific characteristics, which can increase costs. In this study, we
categorize healthcare employees into multiple risk groups based on the
department they work in, the specific job they do, and their birth country. We
create a Markov decision process (MDP) model to decide which TB test should be
taken by each employee group to minimize the total costs related to testing,
undetected infections, employees' time lost. Due to the curse of
dimensionality, we use approximate dynamic programming (ADP) to obtain a
near-optimal solution. By analyzing this solution to the ADP we specify not
only the type but the frequency with which each test should be taken. Based on
this analysis, we propose a simple policy that can be used by healthcare
facilities since such facilities may not have the expertise or the resources to
develop and solve sophisticated optimization models.
