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Coding Manager

Conduct coding quality audits

Enhances✓ Available Now

What You Do Today

Pull a sample of coded encounters, compare coding to documentation, check for missed diagnoses, sequencing errors, and compliance risks.

AI That Applies

AI audit — NLP independently codes the same encounters and compares against human coding, flagging discrepancies for review instead of requiring manual chart-by-chart audit.

Technologies

How It Works

The system ingests instead of requiring manual chart-by-chart audit as its primary data source. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What Changes

You audit 100% of charts instead of 5%. The AI identifies systematic patterns: 'This coder consistently misses secondary diagnoses that affect DRG assignment.'

What Stays

The education — explaining why a code is wrong, teaching documentation requirements, and building coding judgment — that's your expertise.

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 conduct coding quality audits, understand your current state.

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Map your current process: Document how conduct coding quality audits 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: The education — explaining why a code is wrong, teaching documentation requirements, and building coding judgment — that's your expertise. 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 3M CodeAssist 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 conduct coding quality audits 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

“Which compliance checks are we doing manually that could be continuous and automated?”

They're prioritizing which operational processes to automate

your process improvement or lean lead

“How would our regulator react to AI-assisted compliance monitoring — have we asked?”

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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