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Patient Transport Network · AI Digital Twin

Hospital Network Patient Transport
powered by an AI
Digital Twin.

Most healthcare simulation stops at the hospital door. Patient transfers — the ambulance miles between facilities — get planned on instinct and quarterly reviews. Our patented Digital Twin Studio® replays an entire transport network continuously, testing dispatch placement, weather scenarios, and demand spikes against a year of validated transfer data. The result: faster transfers, smarter dispatch, and capital decisions made on evidence — not assumption.

Request a Live Demo →   See the Case Study
3,600+
Clients Worldwide
5,833
Transfers Validated
30+
Years of Innovation
~1 mo
Year-1 Payback

Patient Transport Wait· Avg.

Live · 6 Bases
Baseline 38 min
Optimized 27 min
Reduction −11 min
Across 5,833 inter-facility transfers per year that's ~1,070 reclaimed patient-hours — surfaced by the digital twin in days, not quarters.

Geo-Aware Dispatch

Live road network, road-type speeds, and dynamic weather modeling. Every dispatch decision tested against real geography before a rig rolls.

Validated AI Dispatch Logic

One year of historical transfers — 5,833 in this case — run against existing business rules, then sandboxed through demand spikes of +5%, +10%, +15%.

Capital Avoidance

Recommended expanding 2 existing dispatch facilities instead of building a new one — avoiding ~$2M in capex while still absorbing +15% projected demand.

Why Hospital Transport Networks Run Reactive

Most healthcare simulation stops at the hospital door.

Healthcare modeling has a blind spot: the ambulance miles between facilities. Most simulation optimizes the ED, the OR, or the ICU — but rarely the rig that connects them. The result is a transport network planned by anecdote and quarterly review, with $1M+ capital decisions made on hunches.

Static Transport Planning

Anecdotal dispatch. Untested capital. Reactive weather response.

  • ✕Dispatch placement decided by historical "feel" — no road, weather, or demand modeling
  • ✕"Build a new dispatch facility" defaults to ~$2M capex with no scenario comparison
  • ✕Weather impact on transport times unmodeled — handled with overtime and prayer
  • ✕Rig utilization invisible across the network; capacity decisions guess-based
  • ✕Patient wait times measured monthly, but never modeled against alternative business rules
Digital Twin Studio®

Live geo-network. Year-validated dispatch. Capex tested before it's spent.

  • ✓Geographic twin: hubs, partner facilities, road network, road-type speeds, weather variability
  • ✓Validated against 1 year of historical transfers — 5,833 inter-facility moves
  • ✓Demand stress-tested at +5%, +10%, +15% — capacity headroom proven before crisis
  • ✓Dispatch business rules, helicopter add, and rig deployment compared side-by-side
  • ✓Capital decisions made on evidence: expand vs. build, helicopter vs. ground, rig adds vs. rule changes
How Digital Twin Studio® Works

Six engines. One continuous transport-network loop.

Digital Twin Studio® combines patented simulation, geographic network modeling, dynamic weather scenarios, and dispatch business-rule sandboxing into a single closed-loop platform — purpose-built for hospital network transport planning.

— 01

Geo-Network Modeling

Hospitals, treatment centers, and dispatch bases placed on a map of the region. Roads, highways, and travel speeds calculated automatically by road type and scale.

— 02

Dynamic Weather Scenarios

Alter weather conditions while the simulation is running. See exactly how snow, ice, or thunderstorms cascade through dispatch, transport, and patient wait times.

— 03

Dispatch Placement Optimization

Add, move, or rebalance dispatch bases on the live map. Compare 6, 7, or 8 dispatch locations across the same year of historical demand. Test rig adds before signing a lease.

— 04

Demand Stress Testing

Increase transfer demand from any facility by +5%, +10%, +15%. Find the breaking point in the current network before patients do.

— 05

Business-Rule Sandbox

Rig assignment, proximity logic, helicopter-vs-ground rules, on-call escalation. Test new dispatch policies against the full historical record — without touching live operations.

— 06

On-The-Fly Modeling

Re-route, re-position, re-rule — while the simulation is running. No "stop, recompile, retry" cycles. Adjust to road construction, seasonal demand, or new partner facilities live.

Case Study · Regional Hospital Transport Network

Three states. Six dispatch bases. 5,833 transfers validated.

A regional hospital network — three hub hospitals, transfers to and from 40+ partner facilities across three northeast states, mixed rural and metropolitan service area — came to CreateASoft facing a familiar choice: build a new dispatch facility (~$2M) or find a smarter way to absorb projected demand. They needed evidence, not instinct.

Hub Hospitals
3 in network
Partner Facilities
40+ across 3 states
Dispatch Bases
6 bases · 12 rigs
Annual Transfers
5,833 validated

— The Challenge

Six bases. Rising demand. A $2M build decision on the table.

  • 01Existing 6 dispatch locations under increasing transfer demand — constraints unclear
  • 02Weather impact on transport times across 3-state region difficult to plan around
  • 03"Build a new dispatch facility" being considered at ~$2M capex — no quantified alternative
  • 04Helicopter transport capability under evaluation — high cost, unclear utilization
  • 05No evidence base for dispatch business-rule changes; every rule change a live experiment

— The Solution

A geographic twin. A year of transfers. Every option tested side-by-side.

  • 01Built a digital twin of the 3-state network with origin/destination facilities, road network, and road-type travel speeds
  • 02Loaded 5,833 historical transfers with existing business rules — collected wait times, rig utilization, and request-to-completion data
  • 03Ran scenarios: new dispatch positions, repositioned bases, additional rigs, helicopter capability, and business-rule variations
  • 04Stress-tested transfer demand at +5%, +10%, +15% against the same network
  • 05Generated spaghetti diagrams of high-traffic roadways for ongoing road-construction planning
The Headline Result

Two existing bases expanded. One new facility — and ~$2M — avoided.

The simulation tested both options against the same demand stress at +15%. Building a new dispatch facility would have cost ~$2.0M in capex plus ~$600K in first-year operating overhead. Expanding two existing bases — one extra rig at each — absorbed the same demand at a fraction of the cost.

Build New Facility
Year-
Rejected
Expand 2 Existing
Year-1
−91%
~$2.0M

Avoided capital expenditure — new dispatch facility deferred. Two existing bases expanded with one additional rig each.

+15%

Transfer demand absorbed by the recommended configuration without a single new facility — validated by stress testing.

5,833

Historical transfers loaded for validation. One full year of operations modeled — no synthetic distributions.

Beyond the Capital Decision

Faster transfers. Smarter dispatch. Capital decisions on evidence.

The recommendation to expand two existing facilities was the headline, but the digital twin surfaced compounding wins across patient experience, fleet utilization, and ongoing operational planning — each one tested against a year of real network data.

— Avoided Capital Expenditure
~$2.0M

New dispatch facility deferred

Recommended expanding 2 existing bases instead of building one new
— Patient Transport Wait Time
−29%

From 38 → 27 min average

Faster bed turnover at sending facilities
— Demand Absorbed
+15%

Without a new dispatch facility

Stress-tested at +5/+10/+15% transfer demand
— Rig Utilization
+8%

Across 12-rig fleet

Smarter dispatch logic, fewer empty miles
— Validated Transfers
5,833

One full year of operations

Loaded historical data, not synthetic distributions
— Network Coverage
40+

Partner facilities, 3 states

Rural & metropolitan service area modeled end-to-end
First-Year ROI · Average Network Profile

A real-time transport-planning program that pays for itself in weeks.

Modeled on the case-study network's profile — 3 hub hospitals, 6 dispatch bases (~12 rigs), 40+ partner facilities, ~5,833 annual inter-facility transfers — here's what the simulation's recommendations translate to in dollars over the first year. Capital avoidance dominates Year-1; recurring savings compound from there.

~$3.6M

Estimated Year-1 financial impact across capital expenditure avoidance, fleet utilization, faster bed turnover, and reduced overtime. Recurring annual savings of ~$1.6M/yr thereafter.

Year-1 impact · Average regional hospital network
~$2.0M/yr

Capital Expenditure Avoidance

One-time avoided cost of building a new dispatch facility — land, construction, equipment. Recommended path: expand 2 existing bases (~$200K combined).

~$720K/yr

Annual Operating Savings

~$600K avoided new-facility operating overhead (crew, dispatch, utilities) plus ~$120K reduction in dispatch overtime from pre-tested business rules.

~$890K/yr

Fleet & Capacity Gains

+8% rig fleet utilization across 12 rigs (~$480K), faster bed turnover at sending facilities (~$290K), and reduced weather-event surge overtime (~$120K).

Lever
Calculation
Year-1
Avoided new dispatch facility
Build deferred — land + construction + equipment
~$2,000,000
Avoided new-facility operating cost
2 EMTs/24-7 + admin + utilities + dispatch overhead
~$600,000
Rig fleet utilization (+8%)
$6.0M annual fleet cost × 8% (12 rigs × ~$500K/yr)
~$480,000
Bed turnover / capacity unlocked
5,833 transfers × ~$50 avg sending-facility uplift
~$290,000
Dispatch business-rule overtime
$1.5M dispatch labor × 8% reactive-OT reduction
~$120,000
Weather-event surge overtime
Reduced reactive scrambles via pre-tested weather scenarios
~$120,000
Total Year-1 Impact
One-time capex avoidance + recurring annual savings
~$3.61M
Total Year-1 Impact
~$3.61M
Implementation Cost
~$300K
Net Year-1 Benefit
~$3.31M
Payback Period
~1 mo
* ROI based on a 3-hub regional hospital network with ~12 rigs across 6 dispatch bases serving 40+ partner facilities across 3 states with ~5,833 annual inter-facility transfers. Avoided new dispatch facility capex estimated at $2M (land, construction, equipment, initial outfitting); avoided new-facility operating cost at ~$600K/year (24/7 EMT crew, dispatch overhead, utilities, maintenance). Fleet operating cost assumed at ~$500K per rig per year (crew, vehicle, fuel, maintenance, supplies, equipment). Sending-facility bed-turnover uplift conservatively valued at $50 per transfer. Implementation includes Simcad Pro Health® / Digital Twin Studio® licensing, GIS & road-network integration, business-rule configuration, and a 30-day deployment. All operational improvements (dispatch optimization recommendation, weather scenario testing, demand stress absorption +15%, expand-vs-build cost-benefit) sourced directly from the case-study white paper. Year-1 impact includes one-time capex avoidance; recurring annual savings of ~$1.6M/yr thereafter.
Built On Digital Twin Studio®

Real-time transport planning isn't a feature. It's an entire platform.

CreateASoft has spent 30+ years building the engine that makes healthcare-grade transport simulation possible: 64-bit multi-threaded simulation, patented on-the-fly modeling, full geographic and historical data emulation, and integrated AI/ML.

Year-Long Validated Twin

Loaded against 1 full year of historical transfers — 5,833 in this case — using existing dispatch business rules. Patent-grade accuracy, not theoretical distributions.

Live Geo-Network Modeling

Hospitals and dispatch bases placed on real maps. Roads, highways, and travel speeds calculated by road type. Weather variability dialed live during the run.

On-The-Fly Modeling

Add bases, move dispatch points, change business rules — while the simulation runs. Re-route around road construction. Test new partner facilities without recompiling.

Per-Scenario KPI Dashboards

Patient wait time, rig utilization, request-to-completion time, spaghetti diagrams of high-traffic roadways — all measured per scenario, per shift, per weather condition.

Drag-and-Drop Network Builder

Build the model from existing GIS data, CAD layouts, and historical transfer logs. Add a new partner facility or dispatch base with a click — no recompiling, no re-validating.

AI Dispatch & Demand Engine

Proximity-aware dispatch logic, demand-driven rebalancing, helicopter-vs-ground evaluation, and weather-scenario contingency — all tested before deployment.

The CreateASoft Difference

Live geo-network twin vs. back-office spreadsheets.

Most healthcare simulation tools optimize what happens inside the hospital. Few model the rigs and roads between them. CreateASoft does both — at production-grade accuracy, on the actual map.

Capability
CreateASoft
Everyone Else
Modeling Foundation
✓ Live historical transfers
✕ Theoretical distributions
Geographic Modeling
✓ Native road & weather aware
✕ Generic flow only
Demand Stress Testing
✓ Multi-factor +5/+10/+15%
✕ Single-point analysis
Dispatch Logic Sandbox
✓ Live rule changes
✕ Recompile required
Weather Scenario Engine
✓ On-the-fly variable
✕ Fixed assumptions
Capital Comparison
✓ Build vs. expand vs. rule
✕ Single-option modeling
Engine Architecture
✓ Patented 64-bit multi-threaded
✕ 32-bit single-threaded
Optimize Today For A Better Tomorrow™

See your transport network before you build a new base.

Bring us your network map, your historical transfer log, and your toughest demand-growth scenario — and we'll show you exactly where to expand, where to rebalance, and where to defer capital. Powered by Digital Twin Studio®.

Request a Live Demo → More Case Studies
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