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QSimHealth

com.qsimhealth/public · v1.0.0

Measured by MCP Station

Description written by the publisher, not verified

Healthcare staffing simulator — ED, walk-in clinic, and appointment office DES tools.

Connect

claude mcp add --transport http public https://qsimhealth.com/mcp/v1

{
  "mcpServers": {
    "public": {
      "url": "https://qsimhealth.com/mcp/v1"
    }
  }
}

Tools as reported by the server

• explain_ed_queueing
  Return a textbook-level explanation of Emergency Department queueing dynamics — what plain M/M/c can't model (triage breaks FIFO, patients leave LWBS, peaks dominate), and what real ED staffing decisions need (acuity-tiered metrics, abandonment curves, hourly schedules). Use this when the user asks
• explain_walk_in_clinic
  Explain how walk-in clinics differ from EDs structurally — terminating systems, MD/PA mix as the primary lever, short patient patience, peak-hour coverage instead of 24-hour load. Use when the user describes a walk-in clinic, urgent care, or retail health setting.
• explain_appointment_office
  Explain appointment-based scheduling dynamics — no-show rates as the dominant variance, buffer time as the trade-off lever, double-booking strategy, treatment-time variance by visit type. Use for primary care, specialty clinics, dental, or any scheduled-arrival setting.
• list_facility_types
  List the four healthcare facility archetypes QSimHealth speaks to: ED, urgent care, walk-in clinic, appointment office. Returns one-line descriptions. Call describe_facility for detail on one type, or simulate_ed_demo to run a generic simulation.
• describe_facility
  Return detailed info on one facility type: typical arrival pattern, MD/PA mix, common pain points, what a sim with simulate_ed_demo would teach you about it, and what a CUSTOM facility model from ChiAha would add (your actual data, real schedules, abandonment curves). Use before simulate_ed_demo to
• simulate_ed_demo
  Run a 7-day MD-only demo simulation of an ED, urgent care, walk-in clinic, or appointment-office staffing scenario. Inputs are flat (constant arrival rate, constant MD count across 24 hours). Returns hourly metrics, average wait, total served, utilization. This is a single-provider-type TEACHING dem
• recommend_md_count
  INVERSE of simulate_ed_demo — given an arrival rate and a target average wait, returns the smallest MD count meeting the target. Use when the user asks 'how many MDs do I need to keep wait under N minutes?' or 'what's the minimum staffing that hits a service-level target?'. Linear scan from 1 to max

Repository · JSON · Text for agents · Registry updated 2026-05-27