Dynamic Tuberculosis Screening for Healthcare Employees

TL;DR

This paper develops a cost-effective, risk-based TB screening policy for healthcare workers using a Markov decision process and approximate dynamic programming, balancing test accuracy, costs, and infection risks.

Contribution

It introduces a novel risk-based TB screening model for healthcare employees using MDP and ADP, providing practical policies for resource-limited healthcare facilities.

Findings

Optimal test type and frequency vary by employee risk group.

Proposed policy reduces total testing and infection detection costs.

Approximate solutions are effective for complex decision models.

Abstract

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.