Overview

HSLMS (Hospital Staff Leave Management System) is a full-stack leave-management platform for COMP60022-KN2 - Decision Analytics II. The project investigates operational challenges in Sri Lankan state hospitals through a Divisional Hospital case study, then delivers a data-engineered solution with MongoDB-backed analytics, React-based user interfaces, and an Express/Node.js API for complete staff leave workflows.

Features

HSLMS provides the following features:

1. Problem Investigation and Process Mapping

   • Analysed leave-management bottlenecks in Sri Lankan state hospitals using a Divisional Hospital case study
   • Documented misplaced forms, manual calculations, and legacy procedures requiring digitalisation

2. Data-Engineered Architecture

   • Proposed data pipelines for collection, validation, storage, retrieval, and analytics
   • Compared SQL and NoSQL options and selected MongoDB for semi-structured hospital data

3. MongoDB Schema and Data Validation

   • Designed collections for doctors, major staff, and minor staff
   • Validated collections with real cadre datasets and imported data via mongoimport, Shell, and Compass

4. Query Library and Analytics

   • Built a query library with operators, regex queries, and aggregation pipelines
   • Integrated MongoDB Atlas Charts for live dashboards and KPI reporting

5. Front-end Experience and UI Design

   • Produced complete wireframes for all HSLMS interfaces
   • Built the React front-end with structured components, forms, and data rendering

6. API Development and Testing

   • Implemented Express/Node.js CRUD APIs across all staff types
   • Verified API endpoints in Postman before UI integration

7. Administrative Export Support

   • Added CSV export functionality for offline administrative use

8. Evaluation and Reporting

   • Formulated analytical questions and hypotheses to study leave patterns
   • Presented workflow improvements and outcomes in a LaTeX-compiled academic report

Technologies Used

• MongoDB Atlas: Cloud database service (version managed by Atlas)
• MongoDB Atlas Charts: Live dashboarding (version managed by Atlas)
• Mongoose 8.6.3: ODM for MongoDB
• Express.js 4.21.0: REST API framework
• Node.js >=16.20.1: Backend runtime (minimum requirement from Mongoose 8.6.3)
• React 18.3.1: Front-end framework
• React DOM 18.3.1: React rendering layer
• Bootstrap 5.3.3: Core UI styling
• React Bootstrap 2.10.4: React UI components
• Axios 1.7.5: HTTP client
• CORS 2.8.5: Cross-origin resource support
• Multer 1.4.5-lts.1: File upload handling
• Validator 13.12.0: Input validation
• fast-csv 5.0.1: CSV import/export
• Moment.js 2.30.1: Date and time utilities

Project Specifications

• Course: COMP60022-KN2 - Decision Analytics II
• Application Type: Full-stack leave-management system with analytics dashboards
• API Port: 6010 (Express server)
• Data Handling: MongoDB Atlas with validated collections and query library
• Analytics: MongoDB Atlas Charts and hypothesis-driven evaluation

User Interfaces

UIs

UIs

UI

Getting Started

To run HSLMS locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/supunxiii/hslms.git

2. Navigate to the project directory:

   cd hslms

3. Install backend dependencies:

   cd server
   npm install

4. Create a .env file in server and set the MongoDB connection string:

   DATABASE=your_mongodb_atlas_connection_string

5. Start the API server:

   node app.js

6. In a new terminal, install client dependencies and start the React app:

   cd client
   npm install
   npm start

Project Structure

hslms/
├── client/                         # React front-end
├── data/                           # Data sets and imports
├── server/                         # Express API and MongoDB models
├── package.json                    # Root dependencies
├── package-lock.json               # Root lockfile
├── README.md                       # Project documentation
└── LICENSE                         # Licence

Developer

This project was developed by:

• Supun Wijesooriya - Developer

Contributing

Contributions are welcome. If you would like to contribute:

1. Fork the repository.

2. Create a new branch:

   git checkout -b feature/your-feature-name

3. Commit your changes:

   git commit -m "Add your commit message"

4. Push your branch:

   git push origin feature/your-feature-name

5. Open a pull request with a clear description of your changes.

License

This project is open-source and is licensed under the MIT License.

Contact

For any enquiries or feedback, please contact the developer:

• Supun Wijesooriya: GitHub Profile
• Project Repository: hslms

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Designed and developed in September 2024