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AI + Automation

AI and automation built for the messy reality of healthcare operations.

Iris Health uses AI and workflow automation to help hospital-based physician groups move faster, reduce manual drift, and gain more control over the places where revenue is commonly lost. Our model combines AI agents, structured automation, and skilled human teams to reconcile census activity, support coding, prioritize work queues, accelerate follow-up, and create a more predictable revenue cycle.

Applied AI Areas
Likely missed encounters surfacedAI Assist
Chart image interpreted for coding cluesAI + Human
Workflow queue triaged by urgencyAutomated
AR exception routed for actionPrioritized
Practical AI

AI should make operations tighter, not more opaque.

Too many healthcare technology stories talk about AI in general terms. What buyers want to know is simple: what does it actually improve? At Iris Health, AI is applied to real operational problems. It helps identify likely missed encounters, interpret chart images and documents, triage work, surface coverage issues, prioritize queues, and create more speed and consistency across the revenue cycle.

Just as important, AI does not operate alone. Human experts stay in control where coding judgment, payer knowledge, patient communication, compliance sensitivity, and persistent follow-up matter.

AI Census Reconciliation

Compares census activity, ADT movement, and charge patterns to help identify likely missed encounters and workflow gaps.

Image-Based Record Review

Supports coding workflows by helping interpret chart images, scanned documents, and other medical record inputs.

Reconciliation

AI-assisted census reconciliation.

Iris Health uses AI-assisted reconciliation to compare census movement, ADT data, provider activity, and charge patterns. This helps surface likely missed encounters earlier, so your team can review exceptions and resolve issues before revenue is permanently lost. It is especially valuable for multi-hospital groups where manual reconciliation becomes unreliable at scale.

ADT admission without matching chargeFlagged
Discharge activity matched to claimResolved
Cross-cover visit likely unbilledReview
Coding Support

Image-based medical record review and human + AI coding.

Medical records do not always arrive in a clean structured format. Iris Health uses AI capabilities to help interpret those inputs, surface likely coding signals, and accelerate review. Skilled human coders then validate the information, apply final judgment, and maintain control over compliance-sensitive decisions.

  • AI-assisted reading of chart images and medical record documents
  • Suggested coding clues for faster expert review
  • Experienced coders stay in control of final decisions
  • Better throughput without black-box decision making
Queue Operations

AI agents and skilled human agents working together.

Iris Health uses AI agents to help sort, classify, and prioritize tasks across workflow queues. Skilled human agents then work the items that require persistence, payer knowledge, patient communication, facility follow-up, or judgment.

Where AI helps most

  • Identifying likely missed encounters
  • Reading and interpreting chart images
  • Sorting and prioritizing queues
  • Flagging incomplete records or coverage issues
  • Routing work to the right human team faster

Where humans stay essential

  • Final coding decisions and expert review
  • Hospital and HIM follow-up for records
  • Payer escalation and denial resolution
  • Patient outreach and balance resolution
  • Operational judgment and exception handling
AI + Human Model

See how Iris Health combines AI, automation, and human expertise in one operating model.

We can show you how our AI-assisted reconciliation, image-based coding support, workflow queues, and automation model are designed to improve control without sacrificing accountability.