Semantic Web Framework to Computerize Staged Reflex Testing Protocols to Mitigate Underutilization of Pathology Tests for Diagnosing Pituitary Disorders

The complex and insidious presentation of certain health conditions, such as pituitary disorders, makes it challenging for primary care providers (PCP) to render a timely diagnosis—often delaying appropriate treatment for years. In contemporary clinical laboratories, laboratory interventions can appropriately add-on extra tests to help confirm or rule out complex disorders. For these protocols to be clinically valid and economically efficient, they require combining knowledge on abnormal test result patterns and patient health data to automatically “reflex” add-on tests and issue comments subsequent to their results. In this paper, we present a Semantic Web based framework for the computerization of reflex testing protocols. To avoid casting too wide a net in terms of add-on tests, a reflex (testing) protocol may include an arbitrary number of stages, where test result patterns in stagen can trigger add-on tests in stagen+1. Our evaluation applies a computerized reflex protocol for pituitary dysfunction on 1-year retrospective data, and compares its accuracy and financial cost with a combined reflex/reflective approach that included manual laboratory clinician intervention.