Solution 02 · AI-Driven Solutions
Intelligence where the revenue cycle actually breaks.
Not a chatbot. Models trained on claim and remittance data that predict denials before submission, suggest codes from documentation, draft appeals with the right evidence, and keep payer rules current. Every recommendation is reviewed by a person until it earns autonomy.
- Denial prediction
- Coding assist
- Appeal drafting
- Payer rule intelligence
- Propensity to pay
- Anomaly detection
What is broken
- 01
Payer rules change faster than any team can read
Medical policies, LCDs, and edit logic shift monthly. Yesterday’s clean claim is today’s CO-197.
- 02
Appeals are written from scratch, every time
Analysts spend forty-five minutes assembling the same evidence for the same denial reason, then do it again tomorrow.
- 03
AI vendors sell autonomy before accuracy
A model that is wrong eight percent of the time, unsupervised, creates more rework than it removes.
What we do
Capabilities, in the order we usually deploy them.
- Denial prediction
- A pre-submission risk score with the likely reason code, so the edit happens before the claim leaves the building.
- Coding assist
- CPT and ICD-10 suggestions from the note, each citing the supporting text. The coder accepts, edits, or rejects.
- Appeal drafting
- Evidence-backed appeal letters generated from the denial, the record, and the payer’s own policy language.
- Payer rule intelligence
- A living rulebook per payer and plan, updated from remittances and policy bulletins as they land.
- Graduated autonomy
- Every model starts in review mode and earns straight-through processing per task, per payer, per confidence band.
- Transparency by default
- Every recommendation shows its reasoning, its confidence, and the person who approved it.
What moves
Denials prevented pre-submission
0%31%
Time to draft an appeal
45 min6 min
Coding throughput
1.0x1.4x
Appeal overturn rate
52%71%
Representative targets for a mid-sized physician group with a mixed payer base. Your baseline sets the actual numbers; we agree on them in the diagnostic.
In the portal
Recommendations, confidence, and outcomes are visible per claim in the portal. You see what the models see.
Cash collected, MTD
$21.3M
First-pass denial rate
5.9%
Days in A/R
33.8
Clean claim rate
96.1%
Cash collected
12 months · vs target
Needs attention
56 claims within 7 days of timely filing
$339.2K at stake
CO-197 denials awaiting retro-authorization
$338.4K at stake
Underpayment pattern: BCBS paying 82% of contract on 27447
$128K at stake
Speak to an expert
Let’s find the revenue you’re leaving on the table.
What happens next
- 01
A 30-minute call with an operator
Not a sales rep. Someone who has run a revenue cycle and will ask about yours.
- 02
A scoped diagnostic proposal
Fixed fee, 30 days, with the data we need listed up front.
- 03
Findings with dollars attached
You keep the report whether or not we continue together.