Diagnostic Accuracy in Congested Environments

We investigate decision-making and judgments in the context of diagnostic services. As an example, consider a triage nurse who orders diagnostic tests and makes diagnoses for patients. Accumulating information by running additional tests on a patient is likely to improve the diagnosis, and diagnostic accuracy affects the value of the service provided. However, additional tests take time and thus increase congestion in the system. The service provider (i.e., the nurse) thus needs to weigh the benefit of running an additional test against the cost of delaying the provision of services to others.

For systems without congestion, diagnostic accuracy has been well explored in the literature on sequential hypothesis testing, both theoretically and empirically. This paper investigates the effect of congestion on diagnostic accuracy. We conduct controlled laboratory experiments to test the predictions of a formal sequential testing model that captures the key trade-off between accuracy and congestion in the context described above.