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Coding Manager

Prepare for external coding audit

Enhances✓ Available Now

What You Do Today

When RAC, MAC, OIG, or a commercial payer audits your coding, you prepare the response — pull records, review coding accuracy, and coordinate appeal documentation.

AI That Applies

Audit preparation — AI pre-screens charts against the audit criteria, identifies potential vulnerabilities, and generates appeal documentation for contested codes.

Technologies

How It Works

The system pulls operational data and maps it against risk frameworks, control requirements, and historical incident patterns. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The output — appeal documentation for contested codes — surfaces in the existing workflow where the practitioner can review and act on it.

What Changes

You know your vulnerabilities before the auditor arrives. The AI pre-screens: 'Of the 50 charts requested, 3 have potential DRG accuracy concerns. Here's the documentation support for appeal.'

What Stays

Managing the audit relationship, preparing the response narrative, and coaching your team through the audit process.

What To Do Next

This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.

1

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for prepare for external coding audit, understand your current state.

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Map your current process: Document how prepare for external coding audit works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
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Identify the judgment points: Managing the audit relationship, preparing the response narrative, and coaching your team through the audit process. These are the boundaries AI won't cross.
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Assess your data readiness: AI tools for this area need data to work. Check whether your organization has the historical data, integrations, and data quality to support MDaudit tools.

Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.

2

Define Your Measures

What to track and how to calculate it

Time per cycle

How to calculate

Measure how long prepare for external coding audit takes end-to-end today, then after AI adoption.

Why it matters

The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.

Quality of output

How to calculate

Track error rates, rework frequency, or stakeholder satisfaction scores before and after.

Why it matters

Speed without quality is just faster mistakes. Measure both.

When to check: Check after 30 days of consistent use, then quarterly.
The commitment: Give new tools at least 30 days before judging. The first week is always awkward.
What NOT to measure: Don't measure AI adoption rate as a KPI. Adoption follows value — if the tool helps, people use it.
3

Start These Conversations

Who to talk to and what to ask

your VP Operations or COO

“What's the biggest bottleneck in prepare for external coding audit today — and would AI address the bottleneck or just speed up something that's already fast enough?”

They're prioritizing which operational processes to automate

your process improvement or lean lead

“If we automated the routine parts of prepare for external coding audit, what would the team do with the freed-up time?”

They understand the workflow dependencies that AI tools need to respect

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.

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