Nurse Case Manager
Utilization review and pre-authorization
What You Do Today
Evaluate treatment requests against medical necessity criteria. Approve straightforward requests, refer complex cases for peer review, and communicate decisions to providers and adjusters within regulatory timeframes.
AI That Applies
AI pre-screens authorization requests against clinical criteria, auto-approving standard treatments that clearly meet guidelines and routing only edge cases to the nurse for review.
Technologies
How It Works
For utilization review and pre-authorization, the system draws on the relevant operational data and applies the appropriate analytical models. Machine learning models identify the patterns in historical data that most strongly predict the target outcome, then apply those patterns to score new inputs. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.
What Changes
Routine authorizations (physical therapy within guidelines, standard imaging) get auto-processed. Nurses focus on the 20-30% of requests that require clinical judgment.
What Stays
Denial decisions, peer-to-peer conversations with treating physicians, and the clinical reasoning that distinguishes a reasonable treatment plan from one that's drifting.
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.
Establish Your Baseline
Know where you are before you move
Before adopting AI tools for utilization review and pre-authorization, understand your current state.
Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.
Define Your Measures
What to track and how to calculate it
Time per cycle
How to calculate
Measure how long utilization review and pre-authorization 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.
Start These Conversations
Who to talk to and what to ask
your department medical director
“What data do we already have that could improve how we handle utilization review and pre-authorization?”
They set clinical practice guidelines that AI tools must align with
your health informatics lead
“Who on our team has the deepest experience with utilization review and pre-authorization, and what tools are they already using?”
They manage the EHR integrations and clinical decision support configuration
a nurse informaticist
“If we brought in AI tools for utilization review and pre-authorization, what would we measure before and after to know it actually helped?”
They bridge the gap between clinical workflow and technology implementation
Check Your Prerequisites
Confirm readiness before you invest
Check items as you confirm them.