Rakshith Yashvanth, Senior Product Manager, Advanced Analytics & AI at ZeOmega, emphasizes that effective AI implementation in clinical settings requires comprehensive data across clinical, social, utilization, and behavioral elements, not cherry-picked variables. Real-world data indicates that traditional social determinants of health often miss critical behavioral patterns and healthcare engagement. Rakshith points out that AI must be positioned as a probabilistic tool that supports clinical decision-making rather than replacing clinical judgment, with a mechanism that lets clinicians provide feedback to improve model alignment with clinical reality.    

Rakshith explains, "ZeOmega focuses more on the population health technology space. Our clients usually are risk-bearing organizations. We have health plans that cover a wide range of populations, including Medicaid, Medicare, commercial, ACOs, and other MSOs and similar entities. So we primarily focus on providing tools to these organizations so they can measure risk, manage risk, and manage their members' overall health and well-being."   

"AI is definitely a new technology. It has immense power and capability to go through millions, if not billions, of data points and look for patterns that have traditionally not been baked into clinical training or clinical intuition, or were treated as edge cases. So when it comes to organizations experimenting with and adopting AI right now, the knee-jerk reaction or tendency is to try to take what they have already been doing, which is a lot of these deterministic models based on clinical intuition. They try to map that onto AI, almost trying to evolve these deterministic rule sets or workflows into the AI domain. And this is not a great strategy because, one, we're not really using the true power of AI, which can comb through a lot of data, find things, and handle a lot of complexity."

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