QSimHealth
Healthcare staffing simulator — ED, walk-in clinic, and appointment office DES tools.
| Endpoint | https://qsimhealth.com/mcp/v1 (streamable-http) |
|---|---|
| Registry version | 1.0.0 |
| Now | 7 tools · grade A (98) · pin ts_c212f3a3a2a1469b63c916df8faa1e4f · protocol 2025-06-18 |
| First / last listed | 2026-11-06 02:32 UTC / 2026-11-06 02:32 UTC |
| Grade over time | 2026-11-06: A (98), 7 tools |
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Tools: explain_ed_queueing explain_walk_in_clinic explain_appointment_office list_facility_types describe_facility simulate_ed_demo recommend_md_count
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 conceptual questions about ED waits or staffing. params: none |
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. params: none |
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. params: none |
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. params: none |
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 ground the user in the type. params: name* |
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 demo — **for MIXED PROVIDER STAFFING (MD + PA + NP + Locum), acuity-tiered case mix, hourly arrival pat params: arrivalRate*, mds*, mdTreatmentMinutes, simulationDays, treatmentDistribution |
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 maxMds (default 12, capped 20). Saves Claude from iterating simulate_ed_demo by hand. ANTI-FABRICATION: params: arrivalRate*, targetAvgWaitMinutes*, mdTreatmentMinutes, maxMds, simulationDays, treatmentDistribution |
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