Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Analyze medical billing workflows, identify revenue leaks, optimize claims, reduce denials, and improve revenue cycle KPIs for healthcare practices and billi...
Analyze medical billing workflows, identify revenue leaks, optimize claims, reduce denials, and improve revenue cycle KPIs for healthcare practices and billi...
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
Analyze medical billing workflows, identify revenue leaks, optimize claim submissions, and reduce denial rates. Built for healthcare practices, billing companies, and revenue cycle teams.
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?
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
MetricTargetRed FlagDays in A/R<35>50Clean 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%
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
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)
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
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
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.
"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?"
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. Built by AfrexAI — AI agent context packs for regulated industries. Get the full Healthcare AI Context Pack with 50+ frameworks at our storefront.
Data access, storage, extraction, analysis, reporting, and insight generation.
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