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Medical Billing & Revenue Cycle Management
请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求,再添加到工作流。
SkillHub CLI
skillhub install 1kalin/afrexai-medical-billing打开来源仓库,查看说明和兼容环境后,按来源提供的命令或步骤安装。
将来源链接提供给你的 Agent,让它先读取说明、核对依赖,并向你说明将执行的安装步骤。
Analyze medical billing workflows, identify revenue leaks, optimize claim submissions, and reduce denial rates. Built for healthcare practices, billing companies, and revenue cycle teams.
| 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% |
这些信息来自收录时的来源分析,可能滞后;使用前请核对最新来源文件。
AI agent skill for medical billing analysis, claim denial management, revenue cycle optimization, and healthcare compliance.
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?
Get the complete Healthcare AI Context Pack — 50+ frameworks covering billing, compliance, clinical operations, and patient experience optimization.
Free AI Revenue Calculator → — Find out how much revenue your practice is losing to manual processes.
Built by AfrexAI
# 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...
# 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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