Automating Medication Supply Chain in Hospitals through Automated Drug Dispensing Machines
According to the World Health Organization, medication errors cost around $42 billion annually worldwide, posing a significant concern for hospitals due to their potential impact on patient safety and the quality of care provided. Traditional methods often fall short in addressing the complexities associated with diverse medication inventories and fluctuating demand patterns.
The healthcare industry is currently undergoing big digital transformations, with one example being the increasing adoption of Automated Dispensing Machines (ADMs) in hospitals. These machines play a crucial role in enhancing medication management and patient care. While ample information exists on setting up and organizing these machines, no study has yet provided hospitals with a comprehensive approach to doing so.
This VIP project focuses on optimizing the use of digital solutions in healthcare facilities, with initial emphasis on integrating ADMs to ensure optimal and safe medication management. Specifically, the project aims to leverage ADM capabilities to optimize the drug supply chain within medical facilities by investigating optimal integration strategies across different levels of a hospital infrastructure.
Effective utilization and efficient programming of ADMs present numerous challenges. The project entails extensive data collection on medication prescriptions, ordering procedures, and storage policies to identify demand patterns and usage trends for various medications within the hospital. Additionally, it involves developing algorithms for clustering drugs based on demand and usage patterns, as well as devising optimal ordering, storage, and layout strategies to facilitate error-free drug storage and retrieval while optimizing machine utilization and healthcare costs.
Desired Disciplines
- Graduate, senior and Junior Industrial Engineering Students especially students who have taken courses in OR, Production Systems, Supply Chain, and Simulation
- Industrial engineering students following the PreMed Track
- Senior and junior computer Science or computer engineering students with good coding skills
- Pharmacy, nursing, and medical students
- Social science and marketing students.
Team Leader