Date of Publication

4-7-2025

Document Type

Bachelor's Thesis

Degree Name

Bachelor of Science in Manufacturing Eng'g & Mgt w/ Specialization in Mechatronics & Robotics Eng'g

Subject Categories

Biomedical Engineering and Bioengineering

College

Gokongwei College of Engineering

Department/Unit

Manufacturing Engineering and Management

Honor/Award

Gold Thesis

Thesis Advisor

Nicanor Roxas, Jr.

Defense Panel Chair

Nilo Bugtai

Defense Panel Member

Francisco Emmanuel Munsayac, Jr. III

Renann Baldovino

Abstract (English)

Primary healthcare is essential for societal well-being, yet in the Philippines, access to healthcare remains unequal, with rural areas particularly underserved. Moreover, the reliance on imported medical devices increases the differences. To address this challenge, an innovative solution to improve medical equipment procurement and distribution is proposed. This study aims to design and develop a blockchain and AI-driven e-commerce platform tailored to the Philippine healthcare system. It seeks to centralize medical equipment rental and procurement logistics by enhancing accessibility, affordability, and availability, and ensuring timely delivery of medical supplies. The platform includes blockchain technology for tracking equipment, supply specifications, and life cycles, with Python serving as the core programming language. Artificial intelligence is used to recommend necessary equipment by analyzing previous data. This paper presents a functional graphical user interface (GUI) website that facilitates resource allocation and streamlines procurement for medical device distributors and hospitals in the Philippines. The launch of the proposed system powered by blockchain and AI holds promise for significantly improving healthcare accessibility and efficiency. A structured SQLite database was developed to enhance inventory tracking, AI-driven analytics, and secure blockchain transactions, ensuring efficient data management. The integration of Google Maps API for Vehicle Routing Problem (VRP) optimization reduced delivery times by 25-30%, significantly improving medical equipment distribution. Additionally, AI-based demand forecasting achieved 89.42% accuracy with an R² of 0.9728, demonstrating high predictive reliability. Performance testing demonstrated minimal latency, with API response times averaging 32 ms and blockchain transactions completing in under 2 seconds. The system remained stable under high user loads, successfully managing up to 100 concurrent requests with a low failure rate of 2%.

Abstract Format

html

Abstract (Filipino)

None

Abstract Format

html

Language

English

Format

Electronic

Keywords

Medical instruments and apparatus

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Embargo Period

3-26-2027

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Available for download on Friday, March 26, 2027

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