🌞 AI Finds Next Ozempic?

Plus: Rethinking AI Beyond ROI


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Welcome to your briefing:

  • AI ROUNDUP: Depression in black people goes unnoticed by AI models

  • INDUSTRY ROUNDUP: FDA approves first-in-class drug for rare lung disorder

  • WELLNESS BYTES: Can you live to 130? Longevity scientist believes it’s possible with the right diet


  • TRIVIA: In May 2018, the FDA granted approval to the first AI-based software that can detect what specific medical condition in adults without human oversight?



— ### —

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In my recent LinkedIn post, I discussed why we shouldn’t be focused on the immediate ROI of AI initiatives, rather we should embrace them as part of a learning journey.

Let me explain.

This insight emerged after listening to a podcast where health plan leaders discussed the ROI of AI, revealing a tendency to measure AI's worth in terms of financial, operational, or staffing savings. This approach seems premature given the current stage of AI adoption.

Why rethink ROI in AI?

Many health plans are at the infancy of their AI journey, dabbling in analytics, data management, and pilot projects. The lack of extensive AI expertise isn't a drawback; rather, it marks the beginning of a learning curve. Viewing AI as a learning journey enables these organizations to progressively build AI expertise, eventually facilitating more outcome-driven applications where ROI becomes a relevant metric.

Building AI expertise

For health plans, the initial focus should be on building expertise in AI. Starting with foundational projects like analytics and pilot chatbots allows these organizations to develop the necessary skills and knowledge gradually. This approach ensures that when the time comes to evaluate ROI, they have the experience and expertise to do so effectively.

Laying the right foundations

Success in AI transcends financial returns. It requires the right infrastructure, including high-quality data and efficient processes. The principle of "Garbage in, garbage out" highlights the need for a solid foundation, emphasizing that AI systems are only as effective as the data they process. Before expecting tangible ROI, it's crucial to prioritize data integrity and process optimization.

Empowering Teams

The real power of AI is unleashed when domain experts and technology specialists collaboratively lead initiatives, moving beyond isolated innovation teams. This multidisciplinary approach ensures that AI projects are grounded in practical, real-world applications, combining technical and operational expertise.

A practical example: Improving provider directory accuracy

To illustrate, consider the challenge of improving the accuracy of provider directories.

The process involves:

  • Identify specific challenges: Conduct a detailed assessment to pinpoint the most critical issues in provider directory accuracy, such as frequent changes in provider details or outdated contact information. This precision will direct the AI efforts to where they are most needed.

  • Develop a robust data strategy: Evaluate the current data landscape. If data is scattered or obsolete, consolidate and cleanse it to ensure it’s comprehensive and current. This step is crucial, as high-quality data is the foundation for effective AI applications. Consider incorporating synthetic data to augment real datasets, enhancing the AI model's training without compromising privacy.

  • Implement a targeted pilot with vendor evaluation: Initiate a focused pilot project on the most pressing issues. At this stage, assess potential AI vendors or tools that specialize in healthcare data management and AI solutions. Choose a partner whose technology aligns with your specific needs, and can integrate with your existing systems. Assume there will be work here.

  • Evaluate and expand with synthetic data testing: After implementing the pilot, rigorously evaluate its impact on directory accuracy and operational efficiency. Employ synthetic data to test the scalability and robustness of the AI solution in various scenarios, ensuring it can handle real-world complexities. If the pilot proves successful, systematically expand the AI implementation, gradually covering more aspects of the provider directory.

By redefining how we view and evaluate the ROI of AI initiatives, we can unlock the full potential of AI in health plans.

Building in-house AI expertise, establishing a solid foundation, and empowering teams are essential steps in this journey.

This strategic approach not only improves operational efficiency but also fosters innovation and drives long-term growth.


In May 2018, the FDA granted approval to the first AI-based software that can detect what specific medical condition in adults without human oversight?

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