🏥 Cardiac Service Strategy Lab (v2.0)

An AI-powered decision support tool designed to resolve the disconnect between Theater Planning and Bed Management.

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🎯 The Problem This Solves

Many surgical services face a “Bed-Day Choke.” Increasing surgical slots to clear a backlog often leads to day-of-surgery cancellations because the ward cannot absorb the throughput.

This tool uses stochastic simulation to demonstrate that beds — not just theater time — are the primary lever for clearing 26-week clinical breaches.

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🌟 Key Features

1. The AI Strategy Solver

The model searches for the optimal solution using a heuristic optimization engine. It identifies the minimum viable combination of:

• Beds
• Theater Slots

required to achieve a Zero Breach state by a specified target week.

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2. Dual-Pathway Clinical Engine

Unlike static spreadsheets, this model treats patients as dynamic clinical entities:

• The 10% Legacy Clock
  10% of patients follow a “hard-breach” pathway, automatically escalating to Category 1 at 26 weeks.

• Stochastic Deterioration
  Remaining patients deteriorate according to a Poisson-distributed probability matrix.

• Outlier Logic
  Length of Stay (LOS) is generated using a Gamma Distribution, reproducing real-world bed-blocker behavior.

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3. “Bed-Choke” & Acuity Metrics

• Tracks cancellations caused by bed unavailability
• Flags High-Acuity Weeks where patient mix requires 1:1 nursing ratios

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4. Live Ward Digital Twin

The Operations Manager allows users to input the live status of every bed, transforming a strategic model into a tactical 7-day admission prescription tool.

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📊 Dashboard Modules

Tab	Purpose
Strategic Dashboard	Side-by-side comparison of Baseline, Proposed, and AI-Optimized scenarios
Operations Manager	Bed-level inputs producing a 7-day admission “Prescription”
Transparency Lab	Visual validation of Poisson deterioration and Gamma LOS distributions

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🚀 Getting Started

# 1. Clone the repository
git clone https://github.com/your-username/cardiac-strategy-lab.git
cd cardiac-strategy-lab

# 2. Install requirements
pip install -r requirements.txt

# 3. Launch the dashboard
streamlit run app.py

📝 Assumptions & Logic

Nursing Ratios
Staffing is calculated at 4.5 FTE per bed to maintain 24/7 safe coverage.

Priority Logic
Admissions are prioritized by Clinical Category (1–5) first, then by waitlist age.

Bed Buffers
Scenarios can be tested with safety buffers (beds held empty for emergency arrivals).

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📂 Project Structure

app.py
engine.py
visuals.py

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🛠️ Configuration Snippets

requirements.txt
streamlit
pandas
numpy
plotly

.gitignore
venv/
pycache/
.streamlit/
*.pyc
.DS_Store