Unveiling AI's Impact on Healthcare Economics—with Akshay Sharma
During a podcast interview at the Becker’s Payer Issues Roundtable, Akshay Sharma delves deeper into how Lyric uses large language models (LLMs) to simplify complicated healthcare workflows. He describes real scenarios: payers and providers often struggle with denied claims and unclear policies. With large language models, Akshay shows how AI-powered co-pilots can instantly explain complex denial reasons in plain English—saving providers weeks of confusion.
Instead of layering new tools that disrupt current workflows, Lyric aims to embed co-pilots where users already work—like Word documents for product managers or internal clinician-facing systems. The result? Teams can focus on real clinical or strategic matters, not decoding PDFs or policies. Akshay emphasizes that while AI can handle complex reasoning, trust and safety measures remain paramount. That means controlling hallucinations, ensuring privacy, watermarking AI outputs, and reinforcing responsible AI governance.
Akshay also highlights multi-agent systems that might emerge, where specialized “agents” handle different domains—coordination of benefits, prepay editing, or subrogation. These agents can converse with each other, reducing the friction of manual back-and-forth. Even though AI can’t solve all data engineering challenges overnight, using retrieval augmented generation (RAG) methods allows large language models to tap into existing data sources more flexibly.
He acknowledges that some payers and providers will be cautious, but early signs show these tools streamline processes, cut administrative workload, and free humans for higher-value tasks. Ultimately, Akshay’s message is that well-implemented generative AI can reduce friction, improve timeliness, and deliver meaningful benefits to every stakeholder—patient, provider, or payer—without uprooting established workflows.
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