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Afrexai Medical Billing

Medical Billing & Revenue Cycle Management

1kalin0 installations selon la source☆ 0 Étoiles du dépôt

Installation

Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.

SkillHub CLI

skillhub install 1kalin/afrexai-medical-billing

Examinez la source et la compatibilité, puis suivez les étapes d’installation.

Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.

Voir la source ↗Guides

Instructions

Medical Billing & Revenue Cycle Management

Analyze medical billing workflows, identify revenue leaks, optimize claim submissions, and reduce denial rates. Built for healthcare practices, billing companies, and revenue cycle teams.

What This Covers

CPT/ICD-10 Coding Accuracy
  • Common coding errors by specialty (top 10 per specialty)
  • Modifier usage: 25, 59, 76, 77, AI, AS — when required vs when it triggers audit
  • E/M level selection (2021 guidelines): time-based vs MDM-based
  • Evaluation matrix: does documentation support the code billed?
Claim Denial Analysis
  • Denial reason code lookup (CARC/RARC codes)
  • Top 20 denial reasons across commercial + Medicare + Medicaid
  • Root cause mapping: front-desk error, coding error, clinical documentation, payer policy
  • Appeal letter framework by denial type (with timelines)
  • Clean claim rate benchmark: 95%+ target
Revenue Cycle KPIs
Metric Target Red Flag
Days in A/R <35 >50
Clean claim rate >95% <90%
First-pass resolution >90% <80%
Denial rate <5% >10%
Collection rate >95% <90%
Cost to collect <4% >7%
Net collection rate >96% <92%
Payer Contract Analysis
  • Fee schedule comparison: Medicare vs commercial rates by CPT
  • Allowed amount benchmarking (what you should be getting paid)
  • Underpayment detection: compare ERA/835 to contracted rates
  • Rate negotiation prep: volume data, market rates, quality metrics
Compliance & Audit Readiness
  • OIG Work Plan items relevant to your specialty
  • Stark Law / Anti-Kickback safe harbors checklist
  • False Claims Act risk factors
  • Internal audit sampling methodology (statistically valid)
  • Documentation improvement programs (CDI)
Charge Capture Optimization
  • Missed charge identification by department
  • Charge lag analysis (days from service to charge entry)
  • Superbill/encounter form design best practices
  • Common missed revenue: vaccines, injections, supp...

Informations sur la source

Source
github
Repository
openclaw/skills
Path
skills/1kalin/afrexai-medical-billing
Version
—
Commit
773a034989cd95af4bae932a9d78a5df7f04658a
Source de classification
rules
Statut
active
Visibilité
public

Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.

README

Medical Billing & Revenue Cycle Management

AI agent skill for medical billing analysis, claim denial management, revenue cycle optimization, and healthcare compliance.

Features

  • CPT/ICD-10 coding accuracy — error detection, modifier guidance, E/M level validation
  • Denial analysis — CARC/RARC code lookup, root cause mapping, appeal letter templates
  • Revenue cycle KPIs — days in A/R, clean claim rate, denial rate, collection benchmarks
  • Payer contract analysis — fee schedule comparison, underpayment detection, negotiation prep
  • Compliance — OIG Work Plan, Stark Law, False Claims Act, audit readiness
  • Charge capture — missed charges, lag analysis, superbill optimization
  • Patient responsibility — eligibility verification, prior auth, No Surprises Act compliance

Who This Is For

  • Medical practices (any specialty)
  • Revenue cycle management companies
  • Healthcare billing departments
  • Practice management consultants
  • Healthcare IT teams building billing automation

Quick Start

Install this skill in your AI agent (OpenClaw, Claude, etc.) and ask:

Our family medicine practice has 45 days in A/R and an 8% denial rate.
Payer mix: 40% Medicare, 30% commercial, 20% Medicaid, 10% self-pay.
What should we fix first?

More Healthcare AI Tools

Get the complete Healthcare AI Context Pack — 50+ frameworks covering billing, compliance, clinical operations, and patient experience optimization.

Browse all context packs →

  • Healthcare Pack — $47
  • Pick any 3 packs — $97
  • All 10 industry packs — $197

Free AI Revenue Calculator → — Find out how much revenue your practice is losing to manual processes.


Built by AfrexAI

Contenu extrait

SKILL.md
# Medical Billing & Revenue Cycle Management

Analyze medical billing workflows, identify revenue leaks, optimize claim submissions, and reduce denial rates. Built for healthcare practices, billing companies, and revenue cycle teams.

## What This Covers

### CPT/ICD-10 Coding Accuracy
- Common coding errors by specialty (top 10 per specialty)
- Modifier usage: 25, 59, 76, 77, AI, AS — when required vs when it triggers audit
- E/M level selection (2021 guidelines): time-based vs MDM-based
- Evaluation matrix: does documentation support the code billed?

### Claim Denial Analysis
- Denial reason code lookup (CARC/RARC codes)
- Top 20 denial reasons across commercial + Medicare + Medicaid
- Root cause mapping: front-desk error, coding error, clinical documentation, payer policy
- Appeal letter framework by denial type (with timelines)
- Clean claim rate benchmark: 95%+ target

### Revenue Cycle KPIs
| Metric | Target | Red Flag |
|--------|--------|----------|
| Days in A/R | <35 | >50 |
| Clean claim rate | >95% | <90% |
| First-pass resolution | >90% | <80% |
| Denial rate | <5% | >10% |
| Collection rate | >95% | <90% |
| Cost to collect | <4% | >7% |
| Net collection rate | >96% | <92% |

### Payer Contract Analysis
- Fee schedule comparison: Medicare vs commercial rates by CPT
- Allowed amount benchmarking (what you should be getting paid)
- Underpayment detection: compare ERA/835 to contracted rates
- Rate negotiation prep: volume data, market rates, quality metrics

### Compliance & Audit Readiness
- OIG Work Plan items relevant to your specialty
- Stark Law / Anti-Kickback safe harbors checklist
- False Claims Act risk factors
- Internal audit sampling methodology (statistically valid)
- Documentation improvement programs (CDI)

### Charge Capture Optimization
- Missed charge identification by department
- Charge lag analysis (days from service to charge entry)
- Superbill/encounter form design best practices
- Common missed revenue: vaccines, injections, supp...
README
# Medical Billing & Revenue Cycle Management

AI agent skill for medical billing analysis, claim denial management, revenue cycle optimization, and healthcare compliance.

## Features

- **CPT/ICD-10 coding accuracy** — error detection, modifier guidance, E/M level validation
- **Denial analysis** — CARC/RARC code lookup, root cause mapping, appeal letter templates
- **Revenue cycle KPIs** — days in A/R, clean claim rate, denial rate, collection benchmarks
- **Payer contract analysis** — fee schedule comparison, underpayment detection, negotiation prep
- **Compliance** — OIG Work Plan, Stark Law, False Claims Act, audit readiness
- **Charge capture** — missed charges, lag analysis, superbill optimization
- **Patient responsibility** — eligibility verification, prior auth, No Surprises Act compliance

## Who This Is For

- Medical practices (any specialty)
- Revenue cycle management companies
- Healthcare billing departments
- Practice management consultants
- Healthcare IT teams building billing automation

## Quick Start

Install this skill in your AI agent (OpenClaw, Claude, etc.) and ask:

```
Our family medicine practice has 45 days in A/R and an 8% denial rate.
Payer mix: 40% Medicare, 30% commercial, 20% Medicaid, 10% self-pay.
What should we fix first?
```

## More Healthcare AI Tools

Get the complete Healthcare AI Context Pack — 50+ frameworks covering billing, compliance, clinical operations, and patient experience optimization.

**[Browse all context packs →](https://afrexai-cto.github.io/context-packs/)**

- Healthcare Pack — $47
- Pick any 3 packs — $97
- All 10 industry packs — $197

**[Free AI Revenue Calculator →](https://afrexai-cto.github.io/ai-revenue-calculator/)** — Find out how much revenue your practice is losing to manual processes.

---

Built by [AfrexAI](https://afrexai-cto.github.io/context-packs/)

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