8th Global Gastroenterology and Artificial Intelligence Summit Separating AI Hype from Clinical Reality | September 2026-10 - Chennuru _What Payors Need to see Before Supporting AI Adoption

10 - Chennuru _What Payors Need to see Before Supporting AI Adoption

Pdf Summary

The document argues that payors should support AI adoption only when three conditions are met: a trusted data foundation, evidence that can be traced, and proof the solution works at enterprise scale.<br /><br />First, it says the real need is not more payer data, but shared, near real-time clinical data that gives payers and clinicians one complete picture of the patient. Claims data alone is too delayed and incomplete; clinical data such as labs, pathology, medications, and admission events provides the context needed to reduce friction, speed decisions, and improve care.<br /><br />Second, AI must be built on a trusted, transparent data foundation. The presentation describes a “Health OS” that collects, enriches, and delivers clinical data across provider and payer workflows. It emphasizes interoperability, data quality, and near real-time exchange across many provider connections, with AI-assisted tagging to turn unstructured records into structured, decision-ready information.<br /><br />Third, every AI output must be traceable back to source evidence. The system extracts information from PDFs, charts, and faxes, summarizes it, tags it with structured codes, traces each finding to the original page and line, and surfaces it inside the clinician’s existing workflow. This creates explainability, supports clinician oversight, and helps address governance, bias, hallucinations, and compliance concerns. The message is that responsible AI is a prerequisite for scale.<br /><br />Finally, the document provides proof points showing the approach already works at enterprise scale, including prior authorization automation, fewer denials, faster decisions, improved payment accuracy, and real-time clinical alerts. Overall, the central message is that payors should invest in AI that reduces administrative friction, supports clinicians, and operates on trusted, traceable, scalable clinical data—not black-box automation.

Keywords

AI adoption

payors

trusted data foundation

clinical data interoperability

near real-time data

Health OS

traceable evidence

source evidence

enterprise scale

prior authorization automation