安全扫描（ClawHub）

• VirusTotal ：良性
• OpenClaw ：良性

OpenClaw 评估

The skill's requirements and runtime instructions align with its stated purpose (medical billing and revenue-cycle analysis); it is an instruction-only skill with no installs or credential requests, but you should confirm the publisher and avoid sharing protected health information (PHI).

安装（复制给龙虾 AI）

将下方整段复制到龙虾中文库对话中，由龙虾按 SKILL.md 完成安装。

请把本段交给龙虾中文库（龙虾 AI）执行：为本机安装 OpenClaw 技能「Medical Billing & Revenue Cycle」。简介：Analyze medical billing workflows, identify revenue leaks, optimize claims, red…。
请 fetch 以下地址读取 SKILL.md 并按文档完成安装：https://raw.githubusercontent.com/openclaw/skills/refs/heads/main/skills/1kalin/afrexai-medical-billing/SKILL.md
（来源：yingzhi8.cn 技能库）

SKILL.md

打开原始 SKILL.md（GitHub raw）

# 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, supplies, time-based codes

### Patient Financial Responsibility
- Eligibility verification workflow (real-time vs batch)
- Prior authorization tracking and requirements by payer
- Patient estimate generation (good faith estimate compliance)
- Collections strategy: statements → calls → agency threshold
- No Surprises Act compliance checklist

## Usage

Give the agent your:
- **Specialty** (orthopedics, cardiology, primary care, etc.)
- **Payer mix** (% Medicare, Medicaid, commercial, self-pay)
- **Current KPIs** (denial rate, days in A/R, collection rate)
- **Problem area** (denials, underpayments, coding, compliance)

The agent will analyze against benchmarks and give specific, actionable recommendations.

## Example Prompts

- "Our orthopedic practice has a 12% denial rate. Top reasons are CO-4 and CO-16. Analyze root causes."
- "Compare our cardiology fee schedule to Medicare rates for our top 20 CPTs."
- "Build an appeal letter for a CO-197 denial on CPT 99214 with modifier 25."
- "Audit our E/M coding distribution — we're billing 80% level 3. Is that normal for family medicine?"
- "Our days in A/R jumped from 32 to 48 in two months. What should we investigate?"

## Industry Context

Medical billing errors cost US healthcare $935 million per week. The average practice loses 5-10% of revenue to preventable billing issues. Denial management alone can recover 2-5% of net revenue when done right.

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Built by [AfrexAI](https://afrexai-cto.github.io/context-packs/) — AI agent context packs for regulated industries. Get the full Healthcare AI Context Pack with 50+ frameworks at our [storefront](https://afrexai-cto.github.io/context-packs/).