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Practice Points for Minimizing Weight Bias and Stigma in Obesity Care

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Weight bias and stigma contribute to negative health outcomes in obesity management. This resource highlights the scope and effects of weight bias and stigma in obesity management, including potential healthcare consequences for people with overweight or obesity. In addition, it provides strategies for reducing weight bias and stigma in practice, such as recognizing and addressing bias among healthcare professionals, fostering an inclusive care environment, and using appropriate language. Developed as a tool for practice improvement, this resource offers actionable recommendations to help healthcare teams reduce bias, strengthen patient engagement, and improve the quality of obesity care in their practices.

Released: July 21, 2026

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Provided by Clinical Care Options in partnership with Froedtert & Medical College of Wisconsin and Q Synthesis.

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Supporters

This activity is supported by an educational grant from Lilly.

Lilly

Partners

Medical College of Wisconsin

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Q Synthesis

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Target Audience

This activity is intended for primary care physicians.

Learning Objectives

Upon completion of this activity, participants should be able to:

  • Standardize care plans for adults with obesity

  • Improve timely access to antiobesity medications by streamlining prior authorization processes

  • Increase referrals to supportive services (e.g., nutrition, behavioral health) to provide comprehensive obesity care

  • Improve co-management and care coordination with other specialists (e.g., surgeons, endocrinologists)

  • Improve care coordination and transitions in care when patients with obesity are discharged from the hospital and instructed to follow-up with outpatient providers

  • Track and improve patient outcomes, including changes in weight and BMI

Financial Disclosures

Primary Author

Joseph Kim, MD, MPH, MBA, has no relevant financial relationships to disclose.

Additional Disclosures

Generative artificial intelligence (AI) tools were used for drafting educational content. All AI-assisted content underwent human review, editing, and validation by qualified faculty and accredited education staff to ensure scientific accuracy, balance, and compliance with the ACCME Standards for Integrity and Independence in Accredited Continuing Education.