Abstract

Efficient medical inventory management is vital for healthcare facilities worldwide. Inaccurate drug demand forecasting can lead to stockouts, overstocking, and increased costs, impacting patient care. This paper presents a data-driven solution using advanced machine learning models to optimize medical inventory management. The proposed system aims to improve stock availability, minimize bounce rates, enhance customer satisfaction, and reduce inventory cost waste. Outcome analysis demonstrates the solution’s efficacy, with the Gradient Boost model outperforming others. With a MAPE score below 5%, this approach offers exceptional reliability. The solution’s scalability and usefulness extend to various healthcare specialities, multi-facility implementation, and global applicability, fostering collaboration and best practice adoption among hospitals.

Introduction

In healthcare facilities worldwide, efficient medical inventory management is critical to providing high-quality patient care. The availability of essential medications and medical supplies is vital, but the challenge lies in maintaining optimal stock levels while minimizing waste and costs. Healthcare facilities increasingly turn to data-driven approaches to address this issue, leveraging advanced machine learning techniques to optimize their medical inventory management.

I. The Problem: Inefficient Medical Inventory Management

Inefficient medical inventory management can lead to many challenges, including stockouts, overstocking, and the inability to meet patient demands for essential medications. These issues result from a need for more accurate forecasting of drug demands, ultimately affecting patient care due to missed and delayed doses.

Suboptimal inventory management practices not only impact patient care but also lead to increased costs and resource waste. Overstocking, for example, increases holding costs, negatively affecting cash flow and can result in financial losses due to the increased percentage of expired medicines.

A. Objectives of the Solution

The proposed solution involves developing and implementing a comprehensive drug demand forecasting system that utilizes advanced machine learning models trained on historical medication sales data to predict drug demand patterns accurately. The key objectives of this solution are as follows:

• Optimize Stock Availability: Ensure essential medications are consistently available to meet patient demands.
• Minimize Bounce Rate: Reduce instances of stockouts and overstocking, ensuring a smoother flow of medical supplies.
• Enhance Customer Satisfaction: By meeting patient demands reliably and efficiently, healthcare facilities can enhance the overall patient experience.

Reduction of Inventory Cost Waste: Facilities can reduce wastage and financial losses associated with expired medicines by accurately forecasting drug demands.

B. Solution Overview

The implementation of this data-driven solution involves several key steps:

• Data Collection: Gather historical medication sales data, including drug categories, volumes, and sales dates.
• Data Preprocessing: Clean and prepare the data, handle missing values, and perform feature engineering to ensure data quality.
• Data Mining: Utilize advanced machine learning algorithms, including RNN, BI RNN, LSTM, BiLSTM, GRU, BiGRU, and ensemble models, to analyze and identify patterns in the data.
• Model Deployment: Integrate the predictive models with the medical inventory management system for real-time forecasting and optimization.

The solution’s effectiveness is measured using the Mean Absolute Percentage Error (MAPE) values for various models, which provide insights into the accuracy of demand forecasts.

C. Outcome Analysis

The analysis of outcomes reveals several critical insights:

The top 10 medicines constitute a significant portion of the total cost and quantity, highlighting their importance.

The Gradient Boost model outperforms other models with shallow MAPE values of 1.98% for training and 5.1% for testing, compared to other models with values ranging from 21.8% to 81.8%. This exceptional performance of the Gradient Boost model emphasizes its reliability and effectiveness in optimizing medicine.

Summary

Implementing advanced machine learning techniques for forecasting drug demand has proven to be a game-changer in medical inventory management. With a MAPE score of below 5%, this solution demonstrates exceptional reliability in ensuring the availability of essential medications, minimizing wastage, and reducing costs.

I. Scalability and Usefulness to Other Hospitals

The scalability and usefulness of this solution extend beyond a single healthcare facility:

A. Scalability of the Solution

• Flexibility Across Specialties: This solution suits various medical specialities and can adapt to varying medication demands in different departments.
• Multi-Facility Implementation: It enables seamless coordination across multiple healthcare facilities, offering centralized control and monitoring for regional healthcare networks.
• Global Applicability: The solution is scalable to international healthcare contexts and supports cross-border collaborations and pharmaceutical supply chain optimization.

B. Usefulness to Other Hospitals

• Resource Sharing: The solution facilitates collaboration among hospitals, enabling the sharing of demand insights and supply chain data for mutual benefit.
• Benchmarking and Best Practices: It allows hospitals to benchmark their inventory management against each other, promoting the adoption of best practices.
• Support for Healthcare Networks: The solution is ideal for healthcare networks and alliances, enhancing supply chain efficiency for multiple affiliated hospitals.

Summary

Implementing advanced machine learning techniques for medical inventory management is a significant step towards ensuring efficient healthcare operations and, most importantly, better patient care. This solution’s scalability and collaborative aspects make it a valuable asset to healthcare facilities worldwide.