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QSimHealth

chiaha-ai/qsimhealth-site

This server gives you programmatic access to discrete event simulation models for healthcare staffing scenarios. It's built for analyzing patient flow and resource allocation across emergency departments, walk-in clinics, and appointment-based offices. You'd reach for this when you need to model queue dynamics, staffing levels, or patient wait times without building simulation infrastructure from scratch. The DES (discrete event simulation) tools let you run what-if scenarios on shift schedules, provider availability, and patient arrival patterns. Useful if you're optimizing clinic operations or need to justify staffing decisions with quantitative models rather than gut instinct.

tools
7
transport
HTTP
last updated
May 27, 2026
View on GitHub

Install

From registry metadata
$ npx add-mcp https://qsimhealth.com/mcp/v1 --name public

Run in your terminal. add-mcp asks which agents to add it to (Claude Code, Codex, Cursor and more). Add -g to install it for every project.

Review the command, arguments and environment values before installing. MCP servers run with your local permissions.

Other ways

Claude Code

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

Run in your terminal. Add --scope user to make it available in every project.

Tools

Verified live · 7 tools

Verified live against the running server on Nov 6, 2026.

explain_ed_queueingReturn 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 t...

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 t...

No parameters. Call it with no arguments.

explain_walk_in_clinicExplain 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 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.

No parameters. Call it with no arguments.

explain_appointment_officeExplain 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.

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.

No parameters. Call it with no arguments.

list_facility_typesList 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.

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.

No parameters. Call it with no arguments.

describe_facilityReturn 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 befo...1 params

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 befo...

Parameters* required
name*string
Facility archetype key from list_facility_types.one of ed · urgent_care · walk_in · appointment_officedefault: ed
simulate_ed_demoRun 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-pro...5 params

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-pro...

Parameters* required
arrivalRate*number
Mean patient arrivals per hour (λ). Range 0-500. For real-facility hourly arrival patterns from your data, sign up at https://qsimhealth.com or contact qsimhealth@chiaha.com.default: 8
mdTreatmentMinutesnumber
Mean treatment time per MD encounter, in minutes. Range 1-180.default: 15
mds*integer
MDs (or single-provider type) on duty per hour. Range 1-20. The public demo is MD-only — for MIXED PROVIDER staffing (MD + PA + NP + Locum), acuity-tiered case mix, and hourly schedules, sign up at...default: 3
simulationDaysinteger
Days to simulate. Range 1-7.default: 7
treatmentDistributionstring
Shape of treatment-time distribution. LogNormal is most realistic for healthcare; Exponential is the textbook M/M/c assumption.one of Exponential · LogNormal · Normal · Constantdefault: LogNormal
recommend_md_countINVERSE 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?'. Li...6 params

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?'. Li...

Parameters* required
arrivalRate*number
Mean patient arrivals per hour (λ). Range 0-500.default: 12
maxMdsinteger
Search ceiling for MD count. Range 1-20. For mixed-provider (MD + PA + NP + Locum) optimization, sign up at https://qsimhealth.com.default: 12
mdTreatmentMinutesnumber
Mean MD treatment time, in minutes. Range 1-180.default: 15
simulationDaysinteger
Days to simulate per candidate. Range 1-7; 3 is the default for faster search.default: 3
targetAvgWaitMinutes*number
Maximum acceptable average wait, in minutes. Search returns the smallest MD count meeting this.default: 15
treatmentDistributionstring
Shape of treatment-time distribution.one of Exponential · LogNormal · Normal · Constantdefault: LogNormal

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Readme

Details

Repository
github.com/chiaha-ai/qsimhealth-site
Categories
Mobile Development, Productivity & Office
Transport
HTTP
Registry
MCP registry, active
Tools verified
Nov 6, 2026

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