Early Agentic AI and Payer-Provider Ecosystems

Hi and Happy Tuesday.

In our previous newsletters, we covered Q1 and the rollout of plug-in AI across the revenue cycle, Q2 to make prior authorization, worklists, posting, and estimates operate as one flow and Q3 to use RCM data to guide contracts.

This week, we are focused on early agentic AI and payer-provider ecosystems.

By late 2026, many health systems will have several AI tools live in the revenue cycle, but they will still operate mostly as separate components. 

The next stage is more experimental: 

Agent-style systems that coordinate multi-step workflows, and data flows that link providers and payers more directly.

1. Agentic AI orchestrating multi-step workflows

The gap today:

46% of hospitals use AI in RCM, but tools operate as separate components. Each handoff requires manual coordination.

22% Using domain-specific AI

7x Growth over 2024

The next evolution: Simple "agents" that orchestrate multi-step sequences, intake, eligibility, prior auth, coding, submission, denial follow-up, calling humans only when confidence drops.

• Intake patient data and verify demographics

• Run eligibility and benefits verification

• Initiate and monitor prior authorization workflow

• Support or execute coding decisions

• Submit claims with pre-submission validation

• Handle basic denial follow-up and appeals

Q4 target 1:

Launch 1-2 agent-style pilots for high-volume, lower-complexity service lines with clear safety thresholds and human escalation rules

What to measure:

• Percentage of cases handled end-to-end without manual intervention

• Accuracy and safety metrics at each decision point

• Time from intake to claim submission

• Staff time freed for complex cases

2. Dynamic ingestion of payer rules and policies

The problem:

Payer rules change constantly. Portals, PDFs, and bulletins require manual interpretation. By the time rules are updated, denials have already occurred.

The shift: AI continuously extracts and interprets rules from payer PDFs, portals, and policy bulletins, automatically updating RCM logic.

• Monitor payer portals for policy updates

• Extract requirements from PDFs and bulletins using document AI

• Translate policy language into executable rules

• Update eligibility, prior auth, and scrubbing logic automatically

Q4 experiment 1:

Test dynamic rule ingestion for a narrow set of services and 1-2 high-impact payers, measuring the lag between policy change and rule implementation.

What to measure:

• Time from policy publication to rule update (target: hours, not weeks)

• Reduction in avoidable denials tied to outdated rules

• Staff hours saved on manual rule interpretation

• Rule accuracy compared to manual interpretation

3. Payer–provider interaction optimization

The friction point:

Providers and payers operate on separate systems. Every interaction, prior auth, claims, and appeals, requires data translation and manual coordination.

~40 Prior auths per physician weekly

29% Report serious adverse events from delays

The opportunity: Shared data platforms and structured exchanges that reduce friction in prior auth, claims submission, and appeals powered by both provider and payer AI.

• Structured clinical documentation shared in real-time

• Prior authorization status visible to both sides

• Claims data pre-validated against payer requirements

• Appeal documentation submitted with supporting evidence

Q4 experiment 2:

Participate in at least one structured data-sharing pilot with a key payer, even if the scope is narrow (e.g., prior auth for 2-3 high-volume procedures)

What to measure:

• Prior auth turnaround time for pilot procedures

• Initial claim acceptance rate (fewer information requests)

• Staff time per prior auth or appeal

• Patient wait time for care authorization

In our next issue, we will show you how to stop leaving money on the table by mining your 835 ERA files to detect underpayments, quantify impact, and automate recovery.

Dino Gane-Palmer