AI-HCOS: AI-Powered HealthCare Optimization Suite

Welcome to AI-HCOS

AI-HCOS is a modular, interactive, and scalable web application that implements a suite of mathematical optimization models derived from Chin-I Lin's academic research. It provides intelligent tools for healthcare facilities to plan bed capacity, optimize clinical team staffing, allocate hospital beds across various medical units, and determine optimal location and sizing of emergency room facilities.

This suite integrates artificial intelligence technologies such as machine learning, natural language processing, and generative models to provide demand forecasting, system simulation, intelligent explanation, and automated reporting. Explore the modules using the navigation bar above.

Intelligent Planning

Leverage AI to optimize bed capacity and staffing, ensuring efficient resource utilization.

Bilingual Interface

Seamlessly switch between English and Arabic for a user-friendly experience.

Comprehensive Reporting

Generate detailed reports and visualize key metrics through interactive dashboards.

M1: Aggregate Hospital Bed Capacity Planning (AHBCP)

This module aims to determine the optimal number of hospital beds needed over a planning horizon. It balances patient waiting times, capital investment, and operational costs using dynamic decision-making models.

Inputs

Patient Arrival Rate (λ_t, e.g., 100,110,120):

Service Rate (µ, 1 / ALOS):

Max Acceptable Waiting Time (α_t):

Initial Capacity (c₀):

Capacity Adjustment Step Size (B):

Number of Increments (n):

Budget Constraint per period (γ_t):

Cost Functions (Simplified):

Waiting Cost per patient-hour (f(x_t)):

Transition Cost per bed change (g(x_t-1,x_t)):

Operating Cost per bed (h(x_t)):

Results

Period	Capacity	Waiting Cost	Transition Cost	Operating Cost	Total Period Cost	Compliance
Overall Total Cost:

AI Explanation:

AI-generated explanation will appear here.

M2: Health Care Team Capacity Planning (HCTCP)

This module develops optimized staffing configurations for health care teams across patient classes. It ensures that patient flow performance indicators are maintained while minimizing staffing costs and service delays.

Inputs

Patient Class 1 Arrival Rate (λ₁):

Patient Class 1 Max Waiting Time (hours):

Patient Class 2 Arrival Rate (λ₂):

Patient Class 2 Max Waiting Time (hours):

Team A Configuration:

Service Rate (µ) for Class 1:

Service Rate (µ) for Class 2:

Cost per Team A:

Team B Configuration:

Service Rate (µ) for Class 1:

Service Rate (µ) for Class 2:

Cost per Team B:

Max Teams A to consider:

Max Teams B to consider:

Results

Teams A	Teams B	Total Cost	Waiting Time PC1	Waiting Time PC2	Compliance
Optimal Teams:

AI Explanation:

AI-generated explanation for HCTCP will appear here.

M3: Hospital Bed Allocation Across Medical Units (HBA)

This module allocates a fixed total number of hospital beds across multiple Medical Care Units (MCUs) to minimize rejection rates and balance unit loads while considering local constraints.

Inputs

Results

MCU	Allocated Beds	Rejection Rate	Rejection Cost
Total Rejection Cost:

AI Explanation:

AI-generated explanation for HBA will appear here.

M4: Emergency Room Services Facility Location and Capacity Planning (ERSFLCP)

This module optimizes the location and capacity of emergency room facilities by minimizing cost and ensuring coverage, accessibility, and low diversion risk.

Detailed input forms, calculations, visualizations, and AI tasks for ERSFLCP will be implemented in future updates. This module involves complex facility location problems, M/M/c/c models, and Lagrangian Relaxation, which are challenging for client-side implementation.

AI Explanation:

AI-generated explanation for ERSFLCP will appear here.

M5: AI-Guided Practice and Learning (Educational Toolkit)

This module serves as an educational toolkit for hands-on practice using real-world problems and model applications from the book, including an AI tutor for adaptive learning.

Due to the purely client-side nature of this application, the full capabilities of an AI tutor for dynamic step validation, custom problem generation, and adaptive difficulty scaling would require persistent backend services and more advanced AI model integration. This module will be a conceptual placeholder for now.

AI Explanation:

AI-generated explanation for the Learning Module will appear here.

Dashboard

This dashboard provides an overview of key performance indicators and optimization results across various modules.

Welcome to AI-HCOS

Intelligent Planning

Bilingual Interface

Comprehensive Reporting

M1: Aggregate Hospital Bed Capacity Planning (AHBCP)

Inputs

Cost Functions (Simplified):

Results

AI Explanation:

M2: Health Care Team Capacity Planning (HCTCP)

Inputs

Team A Configuration:

Team B Configuration:

Results

AI Explanation:

M3: Hospital Bed Allocation Across Medical Units (HBA)

Inputs

Results

AI Explanation:

M4: Emergency Room Services Facility Location and Capacity Planning (ERSFLCP)

AI Explanation:

M5: AI-Guided Practice and Learning (Educational Toolkit)

AI Explanation:

Dashboard

Reports