Assessing the Clinical Impact of the Laboratory: Causal Inference from Real World Data

Clinical laboratories must increasingly move beyond analytic accuracy to demonstrate value within the broader health system. Determining the downstream impact of laboratory testing, including avoided procedures, improved patient outcomes, and cost reduction, remains a complex challenge. This presentation will showcase analytic methods and case studies that enable causal inference using electronic health record data to rigorously assess the consequences, both intended and unintended, of diagnostic strategies. After viewing this lecture, participants should be able to: 1. Describe the need for measuring the clinical impact of laboratory and diagnostic strategies. 2. Introduce key epidemiological tools including directed acyclic graphs (DAGs) and propensity score methods for evaluating the impact of laboratory interventions. 3. Demonstrate the application of these methodologies through case studies assessing the impact of celiac disease and ANA-testing algorithms on downstream clinical management. Lee Schroeder, MD, PhD, Professor, University of Michigan, Director, Clinical Pathology, Michigan Medicine 10/22/25 The University of Washington is committed to ensuring digital accessibility in our services, programs, and activities. If you encounter accessibility barriers using videos found on this channel, please contact UW Video at uwvideo [at] uw [dot] edu.