Q&A: How to Properly Implement Data to Ensure Effective Generative AI Use

Anita Mahon, Executive Vice President and Global Head of Healthcare at EXL, discusses their role in guiding payers and providers to effectively implement data into generative AI for healthcare. The uniqueness of healthcare organizations' data, operations, and strategies demands tailored generative AI solutions. EXL collaborates closely with clients to address bias mitigation, set data standards, and provide security, privacy, and monitoring tools to ensure unbiased and effective AI integration. Mahon emphasizes the importance of starting with quality data to avoid unintended consequences in healthcare AI applications.

Medigy Insights

In a Q&A session with Anita Mahon, Executive Vice President of Healthcare at EXL, the discussion revolves around the effective utilization of data in generative AI for healthcare. EXL collaborates with a diverse range of healthcare organizations and emphasizes the uniqueness of their data, operations, and strategies. They work closely with clients to ensure unbiased data, prioritize use cases, establish standards, and offer data management, security, and privacy to prevent health inequities. Addressing potential biases is a critical aspect of their generative AI solution platform. Mahon highlights the need to start with quality data and maintain vigilance throughout the AI application process to avoid unintended consequences in healthcare. Clients are advised to focus on enhancing the capabilities of individuals who interact with patients and members through AI-driven systems.

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