VP of Revenue Cycle
Lead revenue cycle technology and automation initiatives
What You Do Today
Drive automation across the revenue cycle — registration, eligibility verification, charge capture, claim submission, payment posting, and follow-up. Each automation reduces cost and errors.
AI That Applies
End-to-end revenue cycle automation using RPA and AI — from automated eligibility checks at registration to intelligent claim status inquiries and payment posting.
Technologies
How It Works
The system ingests RPA and AI — from automated eligibility checks at registration to intelligent cl as its primary data source. 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
Manual, repetitive revenue cycle tasks are increasingly automated. Claim status checks, payment posting, and eligibility verification can run without human involvement.
What Stays
Designing the automation strategy, managing the change, and handling the exceptions that fall outside automated workflows — those require experienced revenue cycle leadership.
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 lead revenue cycle technology and automation initiatives, 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 lead revenue cycle technology and automation initiatives 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 board chair or lead independent director
“Which steps in this process are fully rule-based with no judgment required?”
They shape expectations for how AI appears in governance
your CTO or CIO
“What's the error rate on the manual version, and what would "good enough" look like from an automated version?”
They own the technology infrastructure that enables AI adoption
Check Your Prerequisites
Confirm readiness before you invest
Check items as you confirm them.