Built for how your
organization actually bills

Problems

• Each specialty codes differently, and cross-specialty claims get denied for medical necessity mismatches.
• A central billing office tries to apply one process to fifteen very different specialties.
• The billing team is stretched thin trying to stay current on every specialty's rules.

Current Workflow

• Notes route to specialty-specific coders who each apply their own mental checklist.
• Specialty rule-checking is manual, inconsistent, and easy to miss under volume.
• Denials trigger frequent, slow resubmission cycles.

How Althais Solves It

• Specialty-aware AI models are trained per department, not applied generically.
• One unified claims builder replaces fifteen inconsistent manual processes.
• A cross-specialty payer rule engine catches conflicts before submission.

"Our cardiology and ortho claims used to get denied for completely different reasons. Now both teams work off the same intelligence."

Before vs. After Althais

Problems

• There's no dedicated coding staff — the provider or office manager handles it after hours.
• Denials often get written off entirely because there's no bandwidth to appeal.
• Billing knowledge lives in one person's head, which is a single point of failure.

Current Workflow

• The provider or office manager manually codes notes at the end of a long day.
• Claims go out in batches whenever there's time to submit them.
• Denials pile up in a folder that rarely gets revisited.

How Althais Solves It

• AI does first-pass coding the moment the note is finished — no after-hours catch-up.
• Appeal letters draft themselves from the denial reason and clinical note.
• A simple review queue fits into five minutes between patients, not an evening.

"I used to do billing on Sunday nights. Now review takes ten minutes between patients and I actually appeal denials instead of eating them."

Before vs. After Althais

Problems

• High patient volume leaves no time for careful E/M level selection.
• Seasonal volume spikes overwhelm billing staff without warning.
• Under- and over-coding both happen when speed is prioritized over precision.

Current Workflow

• Coders batch-process notes at the end of the day, long after the visit.
• E/M leveling is done manually against a guideline sheet, encounter by encounter.
• High staff turnover means consistency resets every few months.

How Althais Solves It

• E/M levels are suggested in real time with documentation-based justification.
• The system scales instantly with volume — a flu-season spike doesn't slow it down.
• Coding logic stays consistent regardless of who's staffing the front desk.

"Flu season used to mean a two-week claims backlog. Now Tuesday's visits are submitted by Tuesday night."

Before vs. After Althais

Problems

• Sliding-fee and wraparound billing rules are complex and payer-specific.
• A small billing team serves a very high patient volume with thin margins.
• Strict compliance requirements turn every audit into a multi-week scramble.

Current Workflow

• A small team manually tracks FQHC-specific billing rules payer by payer.
• Compliance audit prep means pulling documentation by hand across months of claims.
• Every denial is disproportionately costly given already-thin reimbursement.

How Althais Solves It

• FQHC PPS and wraparound billing logic is built in, not bolted on.
• Every AI decision is logged, timestamped, and audit-ready by default.
• Denial prevention is tuned specifically to Medicaid managed care plan behavior.

"Audit prep went from three weeks of pulling files to two days of exporting a report that was already correct."

Before vs. After Althais

Problems

• Highly specific coding requirements don't fit generic billing software.
• Modifier errors are common and directly cause denials.
• Payer medical necessity policy varies procedure by procedure.

Current Workflow

• Coders lean on specialty cheat sheets that go stale as payer policy changes.
• Modifier application is manual and inconsistent across coders.
• Medical necessity documentation gaps are only caught after a denial arrives.

How Althais Solves It

• Specialty-trained models understand procedure-specific modifier logic natively.
• Medical necessity is cross-checked against current LCD policy automatically.
• Prior auth documentation drafts itself from the clinical note.

"Our modifier denial rate was the thing we could never fix. It's now the metric we stopped worrying about."

Before vs. After Althais

Problems

• Manual coding review is the bottleneck that throttles everything downstream.
• Denial root causes are usually discovered a month late, in a report.
• Coding, billing, and analytics live in separate, disconnected tools.

Current Workflow

• Coders and billers work in separate systems that don't talk to each other.
• Denial trends surface only when someone builds a monthly report.
• Leadership makes decisions without real-time visibility into KPIs.

How Althais Solves It

• One system spans coding, claims, submission, tracking, and payment.
• Denial root causes surface live, not thirty days later.
• Team-wide productivity and quality dashboards update in real time.

"We used to find out what went wrong a month later. Now we see it the day it happens."

Before vs. After Althais

Problems

• Repetitive manual entry between coding systems and claim software wastes hours.
• Chasing denial documentation across payer portals eats the rest of the day.
• Appeal letters get written from scratch, every single time.

Current Workflow

• Approved codes get re-keyed by hand into the claim software.
• Appeal letters are drafted manually, often reusing an old one as a template.
• Claim status is tracked by logging into six different payer portals.

How Althais Solves It

• Approved codes flow directly into claims — zero re-entry, zero transcription errors.
• AI drafts a complete, payer-ready appeal letter in seconds.
• One dashboard tracks status across every payer, not six separate logins.

"Appeal letters used to be the task everyone put off. Now it's a two-minute review, not a twenty-five minute write."

Before vs. After Althais