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ADLM 2027

Quantifying diagnostic impact: Causal inference and real-world data in practice

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Course ID: 191206
3.0 ACCENT credits /3.0 CME credits
Sunday, July 26, 2026
Afternoon course | 1 p.m. – 4 p.m. US Pacific Time
Anaheim Marriott, Platinum Ballroom 3-4

Laboratories must increasingly move beyond analytic accuracy to demonstrate value within the broader health system. Determining the downstream impact of laboratory testing, as measured through metrics such as decreased length of stay or improved patient outcomes, is critical to this process but remains a complex challenge. Real-world data, including electronic health records, provide an important opportunity for quantifying diagnostic utility. This course will showcase advanced analytic methods and real-world studies that use causal inference modeling with large data sources to rigorously assess the consequences, both intended and unintended, of diagnostic strategies. This interactive workshop will convene laboratorians, clinicians, and informaticists for an immersive experience using synthetic and de-identified EHR datasets. Participants will explore key health system performance indicators that contribute to defining value in contemporary healthcare and gain practical skills in causal inference techniques, including directed acyclic graphs (DAGs), target trial emulation, and propensity score methods.

Faculty

  • Moderator: Lee Schroeder, MD, PhD, University of Michigan
  • John Donnelly, PhD, MSPH, MS, University of Michigan Medical School
  • Ryan O'Connell, MD, MBA, University of California, Irvine

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Course details

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If you missed a session at ADLM 2026, check for the session recording through the available list on our website. Member all access registrants get complimentary access!
Session Recording

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