2024 World Conference on Lung Cancer (WCLC) - ePosters-EP.17E.03 Using Healthcare Claims to Predict Costs by Stage for Medicare and Commercially Insured Patients with Non-Small Cell Lung Cancer

EP.17E.03 Using Healthcare Claims to Predict Costs by Stage for Medicare and Commercially Insured Patients with Non-Small Cell Lung Cancer

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The study titled "Using Healthcare Claims to Predict Costs by Stage for Medicare and Commercially Insured Patients with Non-Small Cell Lung Cancer" examines the relationship between the stage of non-small cell lung cancer (NSCLC) at diagnosis and subsequent healthcare costs using administrative claims data. The focus is on evaluating cost differences for Medicare and commercially insured patients.<br /><br />The research utilized datasets covering Medicare (SEER-Medicare linked dataset for 2016-2017) and commercial insurance (Milliman’s 2019-2020 Consolidated Health Cost Guidelines) by excluding patients without consistent enrollment or pre-existing cancer-related treatment. A predictive machine learning model was applied to sort patients into AJCC stages 0/1/2, 3, or 4 using claims-derived data, including demographic and treatment variables.<br /><br />Results indicated that higher cancer stages at diagnosis correlate with increased healthcare expenses over the three years following diagnosis. Commercially insured patients' first-year costs after diagnosis were found to be 1.3 to 2.5 times higher than those for Medicare patients, with stage 4 patients experiencing around three times the costs than those diagnosed at stage 0/1/2. Notably, high-stage disease (stage 3 or 4) saw continued elevated costs up to 36 months post-diagnosis for commercially insured patients vs. those under Medicare.<br /><br />The findings suggest that early diagnosis of NSCLC could significantly reduce healthcare costs by identifying cancer stages sooner, emphasizing the importance of lung cancer screening in shifting diagnoses to earlier stages. Nonetheless, commercially insured patients bear a higher financial burden than their Medicare counterparts, especially in advanced stage cases. The study, funded by Exact Sciences Corporation, highlights the potential value of using claims data in healthcare cost management but acknowledges reliance on the accuracy of the datasets used.

Asset Subtitle

Bruce Pyenson

Meta Tag

Speaker Bruce Pyenson

Topic Global Health, Health Services, and Health Economics

Keywords

non-small cell lung cancer

NSCLC

healthcare costs

Medicare

commercial insurance

cancer stages

claims data

machine learning

early diagnosis

lung cancer screening