Achieving Excellence in Lung Cancer Diagnosis through System-Level Analysis of Diagnostic Performance - PROJECT SUMMARY Lung cancer remains the most lethal cancer in the United States, with its diagnosis often hindered by delays. The complexity of these delays stems from a patient's diagnostic journey that spans across multiple care systems. In many instances, key decision-influencing factors are unrecorded, and medical records frequently miss essential elements of the patient's experience. Recognizing this, the focus of my career development and current research proposal is to undertake a multi-faceted analysis utilizing a mixed-methods approach to characterize the lung cancer diagnostic journey, incorporating both physicians' and patients' perspectives to construct a comprehensive narrative of the diagnostic process. The project will answer key questions: Q1: How does the diagnostic process begin? Q2: How do patients progress from presentation to diagnosis? Q3: How long does the diagnostic process take? Q4: Are there risks or harms associated with a prolonged diagnostic process? Q5: Are minority patients at a higher risk of experiencing extended diagnosis durations and/or associated harm? Q6: Do healthcare use patterns suggest earlier diagnosis could be possible? Q7: Could anything has been done differently to reach the correct diagnosis sooner? The proposed research project will develop novel measurement and intervention strategies to prevent lung cancer diagnostic delay and harm by generating novel insights about patients' journeys toward lung cancer diagnosis. We seek to apply the Theory of Constraints paradigm to identify the weakest links in the diagnostic process chain for lung cancer. Leveraging data from the Johns Hopkins Health System, we will (1) measure delays in patient, provider, and system time intervals for the lung cancer diagnostic journey; (2) estimate associated harms and health disparities, and identify if certain clinical presentations were more likely to be missed or lead to diagnostic delay; and (3) conduct in-depth mixed-methods case reviews, provider input, and patient interviews to identify obstacles that could impact diagnostic performance and patients' health outcomes. This study will yield novel insights that answer the seven key questions above related to lung cancer diagnosis. These answers will inform subsequent efforts to prevent avoidable harm due to lung cancer diagnostic delay more broadly in Maryland as well as nationally. The proposed study and training activities will uniquely position me to launch a career as an independent investigator developing and testing potential intervention strategies to improve lung cancer diagnostic safety. I anticipate a pilot intervention study during the follow-on R01 phase.
