Accurate diagnosis and procedure coding has become a critical factor in identifying patients eligible for advanced obesity treatments, including GLP-1 therapies. Yet systematic undercoding and overcoding can obscure the true burden of obesity, leaving high-risk patients effectively invisible to payers, life sciences organizations, and care teams. This limits the ability to accurately understand treatment eligibility and unmet need. 

Drawing on real-world data and case studies, this session explores how coding inaccuracies affect patient identification, treatment eligibility, and access to obesity therapies. We’ll examine where coding errors occur most frequently, how they influence population health insights, and practical steps organizations can take to improve coding accuracy and better identify patients who could benefit from evidence-based interventions. 

Key topics will include: 

• Common patterns of undercoding and overcoding that distort the obesity population 

• How coding inaccuracies affect patient identification, treatment eligibility, and population-level insights 

• Where coding breakdowns are most likely to occur 

• Opportunities to improve patient identification and engagement strategies 

• Practical steps to close coding gaps and reduce missed opportunities for treatment