Vijay Venkatesan, Chief Analytics Officer, Horizon BCBS of New Jersey, speaks with Ron Rogers, Principal Consultant, AHEAD, in a video interview, about overcoming AI resistance, using generative AI (GenAI) as a tool, use cases to improve efficiencies, enhancing member experience with AI, and the eligibility factor for AI applications.

Venkatesan begins by focusing on healthcare as an industry and sharing strategies to overcome AI resistance. He states that healthcare, being an extremely regulated industry, is risk-averse.

Breaking down the risk aspect further, Venkatesan says that the maximum focus lies on how to manage risk and create the right framework around it. Consequently, when it comes to bringing AI into healthcare, the core of the discussion is establishing the right governance model.

This could include assessing the need for a new framework for AI governance, its alignment with the data governance model, or its unique characteristics, says Venkatesan. An organization with an appetite for AI must have these in motion, starting with governance, he asserts.

Digging deeper, Venkatesan states that most healthcare systems have succeeded in having a role-based approach to security, with solid models and fulfilled requirements of PHI and PII. The incorporation is done not just from the data perspective but from the application perspective as well, he adds.

However, Generative AI or AI discussions in general revolve around the construct of what can be considered as public and private data, observes Venkatesan. He continues that data being the foundation of AI, remains the hotspot of AI conversations along with security.

The security aspect decides on the access, usability, and accountability, notes Venkatesan. He believes that application security has come a long way, and now the focal point is on creating that AI security framework, aiming not just at AI benefits but also the potential harm.

When asked about GenAI elements, Venkatesan states that he thinks of GenAI as one of the tools in a toolbox. He continues that there is no one-size-fits-all all and organizations must aptly comprehend which is the best tool to apply for a particular business problem.

Whether the solutional value lies in member experience, operational optimization, or business transformation, the question remains “What is the right thing to do?” With increased AI applications in healthcare, such as predictive modeling, organizations must ensure that the models built are bias-free.

However, with GenAI, it boils down to assessing which private data can go into the public domain, Venkatesan asserts. He adds that organizations must think about the effectiveness of GenAI as well as its shortcomings such as hallucinations.

Commenting on use cases to improve efficiencies and member service, Venkatesan discusses reimagining the IT Function considering tomorrow’s needs. For instance, leveraging intelligent bots to enhance customer experience and streamline help desk functions.

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