From Stent to Statement: Unlocking Heart and Vascular Revenue with Lean AI-Driven Coding

Estimated reading time: 6 minutes

Introduction

Aspirus Health’s recent growth spurt—from acquiring seven Wisconsin hospitals to bringing St. Luke’s in Duluth under its wing—has created exactly the kind of “good problem” every not-for-profit health system wants: more patients, more service lines, and more mission impact. Yet with 19 hospitals, 130 outpatient sites, and an Epic rollout clock already ticking, even a single percent of avoidable revenue leakage can translate into millions of dollars.

Nowhere is the risk–reward balance sharper than in Heart and Vascular services. A single cath-lab encounter may involve professional and facility claims, multiple devices, moderate sedation, imaging guidance, and complex National Coverage Determination rules—often spanning 20 or more CPT codes. Getting that claim right the first time is tough when coders are scarce and payer edits evolve weekly.

This article outlines a capital-efficient, pilot-friendly path to shore up those dollars using narrow-focus AI for coding and documentation—without a rip-and-replace project or multi-year budget cycle.

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1. The Post-Affiliation Revenue Cycle Puzzle

Operational reality

1. Three EHR ecosystems (legacy Wisconsin instance, Upper Peninsula instance, and pending St. Luke’s Epic build)
2. Variations in charge master rules between 19 hospitals
3. A 321-bed flagship with a Level II trauma center that just doubled its ED footprint

Each variation multiplies the chance of:

• Missed add-on codes for intracoronary imaging
• Device-to-procedure mismatches triggering NCCI edits
• Inconsistent use of CPT modifiers leading to denials or down-codes

Financial impact
Industry data show cardiovascular encounters account for less than ten percent of volume yet over twenty percent of net revenue at many regional systems. Even a three percent denial rate here can erase the margin gains Aspirus posted in FY 2023.

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2. Why Heart and Vascular Coding Is Uniquely Complex

• Multi-step procedures: Example—Diagnostic coronary angiography converts to percutaneous intervention, changing the billing construct on the fly.
• Device-dependent codes: Stents, atherectomy catheters, or LV assist devices each add layers of specificity.
• Bundling rules: Moderate sedation now bundled in many cases; intravascular ultrasound payable only with certain primary codes.
• Payer carve-outs: Medicare vs. regional commercial plans differ on medical necessity for FFR vs. iFR.

Traditional coding teams must cross-check clinical notes, imaging reports, and supply logs—often spread across separate source systems after an acquisition. Turnaround times stretch, and coder fatigue grows.

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3. A Capital-Efficient AI Strategy

Revedy’s approach favors small, high-yield pilots instead of enterprise-wide moonshots. Key tenets:

1. Narrow Scope
   Target a single specialty (Heart and Vascular) or even a single encounter type (diagnostic cath with or without PCI). Depth beats breadth during early validation.

2. Minimal IT Lift
   An API-first design means clinical notes or transcripts can be pushed securely from existing middleware or SFTP, avoiding direct EHR integration until value is proven.

3. Design-Partner Economics
   Because we are a growth-stage vendor, pilot pricing is volume-based and month-to-month. Your data guides our product roadmap, and your success metrics become shared IP.

4. Rapid Time-to-Insight
   A typical pilot moves from signed BAA to first coded encounters in six weeks:
   Week 1: Data extract (100–200 recent cath-lab encounters)
   Week 2-3: AI model fine-tuning on Aspirus clinical language
   Week 4: Parallel coding—AI versus human baseline
   Week 5: Accuracy validation and denial prediction
   Week 6: Executive read-out with ROI forecast

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4. Under the Hood—What the AI Actually Does

1. Document Ingestion: OCR fallback converts scanned cath-lab forms into structured text; PHI stays inside a HIPAA-compliant environment.
2. Specialty Identification: A machine-learning layer tags each encounter as Interventional Cardiology, EP, or Vascular Surgery to apply the right coding logic.
3. Agentic CPT Selection: Purpose-built coding agents walk through an XML-toolchain decision tree—first validating medical necessity, then selecting primary procedure codes (for example 92928), followed by add-ons such as 92978 for IVUS.
4. Denial Simulation: Before export, the system cross-checks payer specific edits; if documentation will not support FFR, the agent flags it for CDI attention rather than letting it hit the clearinghouse.
5. Audit Trail: Every LLM prompt, tool call, and code recommendation is versioned; compliance teams can trace exactly why 93458 was billed instead of 93454.

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5. Early Proof Points from Peer Organizations

A regional 300-bed hospital in the Southeast ran a 90-day design-partner pilot focused on diagnostic cardiac caths. Results:

• First-pass yield improved from 91 percent to 97 percent, worth an annualized 1.2 million dollars.
• Coder productivity doubled (from 8 to 16 encounters per hour) by offloading initial code drafts.
• Denial overturn time fell by 35 percent because the AI exposed documentation gaps before submission.

While every market differs, these numbers illustrate the upside available when complex cardiovascular encounters are the pilot focus.

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6. Implementation Blueprint for Aspirus Health

1. Choose a Lighthouse Facility
   The Wausau flagship or the soon-to-be Epic site in Duluth—either provides a clean cohort.

2. Define Success Metrics
   a. Reduction in cardiovascular claim denials
   b. Coder hours saved per encounter
   c. Net revenue lift per case

3. Data Governance
   Existing secure SFTP or AWS S3 buckets already used for document exchange in your enterprise can relay de-identified files for model training.

4. Feedback Loop
   Revenue Integrity staff review AI results in a web dashboard; one-click approval or edit trains the model in near real time.

5. Scale Decision
   After two quarters and an NPV-positive ROI, expand to Orthopedics or Neurosciences where similar complexity exists.

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7. Why Act Now?

• Coder Workforce Shrinkage: AAPC projects a 29 percent shortage by 2028—automation is becoming necessity, not luxury.
• Epic Consolidation Window: Rolling AI coding capabilities into your upcoming Epic build avoids re-work later.
• Margin Pressure: FY 2024’s slight positive operating income shows a turnaround; locking in incremental revenue through AI keeps that trend alive.

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Conclusion

Heart and Vascular services are the pulse of your revenue engine, but also its bleeding edge. By piloting narrow-focus, AI-driven coding you can capture dollars otherwise lost to complexity—without a multi-million-dollar, multi-year rollout.

Revedy’s design-partner model fits health systems that value capital efficiency, measurable impact, and the agility to fine-tune solutions shoulder-to-shoulder with clinicians and coders.

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Next Step

If improving cardiovascular first-pass yield by two to five points sounds worthwhile, let’s schedule a 30-minute discovery call. We will:

1. Sketch a six-week pilot timeline specific to your cath-lab workflow.
2. Identify the minimum data needed for rapid value proof.
3. Provide a fixed-fee pilot proposal you can include in next month’s Finance Committee packet.

Email thor at revedy dot io or connect on LinkedIn to reserve a slot. Your patients’ hearts will thank you—and so will your balance sheet.