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Workflow Deep Dive

How Revedy Builds a Data-Driven Appeal

Transform the appeal process from a manual struggle into an intelligent, automated workflow that wins.

The Manual Way (Before Revedy)

Time-Consuming Research

Manually digging through clinical notes and payer websites.

Confusing Payer Rules

Policies are often 100+ pages long and cover many procedures.

Weak, Generic Arguments

Using the same template for every appeal is ineffective.

Low Success Rate

Payers easily dismiss appeals that aren't specific and evidence-backed.

IS TRANSFORMED INTO

The Revedy Way (After Revedy)

Automated Evidence Gathering

The AI instantly analyzes all clinical notes and payer communications.

Pinpoint Policy Extraction

Our engine finds the exact policy rules relevant to your case, ignoring the noise.

Case-Specific Arguments

Every appeal is custom-built, quoting the payer's own rules as evidence.

Data-Driven Success

Appeals are so well-supported and specific that they are hard to deny.


How It Works: From Raw Data to Winning Argument

Part A: We Gather the Essential Evidence

1. Your Clinical Documentation

We analyze all case files (Op notes, reports, etc.).

Output: The "Procedure Context"

2. The Payer's Communication

We read the denial letter to identify the payer, plan, and state.

Output: The "Payer Context"

3. Our Payer Policy Library

We access our comprehensive library of payer rules and guidelines.

Output: The "Policy Document"

Part B: The Revedy AI Engine Connects the Dots

1. Define Case: Uses the "Procedure Context" to understand the specific medical scenario.

2. Identify Rules: Uses the "Payer Context" to select the right "Policy Document".

3. EXTRACT RELEVANCE (The Key Step):

The AI reads the entire policy and pulls out **only the specific paragraphs and rules** that apply to this exact case.

Part C: The Output is a Precise, Evidence-Based Appeal

Generated Appeal Letter

  • Clinical Justification: Cites evidence directly from your clinical documentation.
  • Medical Necessity Argument: Quotes the payer's own rules back to them using the extracted policy text.
  • Evidence Summary: Lists all sources used to build the argument, creating an audit-proof record.

The Result: A Smarter, Faster, More Effective Process

Solves Research

Automates the gathering of clinical and policy data.

Solves Confusion

Pinpoints the exact rules that matter, ignoring the rest.

Solves Generic Arguments

Creates a unique, evidence-based appeal for every case.

Drives Success

Makes the argument so specific and well-supported it's hard to deny.

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