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Revenue Cycle Specialist

Handle prior authorizations

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What You Do Today

You submit prior authorization requests, track approvals, appeal denials, and ensure services aren't delayed while waiting for payer decisions.

AI That Applies

AI identifies authorization requirements proactively, auto-submits requests with clinical documentation, and predicts which requests will require peer-to-peer review.

Technologies

How It Works

For handle prior authorizations, the system identifies authorization requirements proactively. The automation engine executes each step in the process sequence — validating inputs, applying business rules, generating outputs, and routing exceptions to human review queues. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What Changes

Authorization turnaround improves when AI identifies requirements early and submits complete requests with proper clinical documentation.

What Stays

Managing the urgent cases where patients need care now but authorization is pending, and the peer-to-peer calls that require clinical argumentation.

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 handle prior authorizations, understand your current state.

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Map your current process: Document how handle prior authorizations 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 urgent cases where patients need care now but authorization is pending, and the peer-to-peer calls that require clinical argumentation. 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 Prior Auth Automation 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 handle prior authorizations 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 CFO or VP Finance

“What data do we already have that could improve how we handle handle prior authorizations?”

They're prioritizing which finance processes to automate first

your ERP or finance systems admin

“Who on our team has the deepest experience with handle prior authorizations, and what tools are they already using?”

They know what automation capabilities exist in your current stack

your FP&A counterpart at a peer company

“If we brought in AI tools for handle prior authorizations, what would we measure before and after to know it actually helped?”

They can share what worked and what didn't in their AI rollout

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.

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