Machine Learning Applications in Hospital Pharmacy for Predicting Drug Shortages and Supply Chain Optimization

Sagar Vasantrao Joshi¹, Sarika B. Patil², Sarika N. Patil³, Sushma Bhosle⁴,

Neeta Pramod Karhadkar⁵, Yogesh Kumar Rathore⁶*

^1,2,3,4,5Department of Electronics and Telecommunication Engineering, Nutan Maharashtra Institute of Engineering and Technology, Talegaon, Dabhade, Pune, Maharashtra, India.

⁶Shri Shankaracharya Institute of Professional Management and Technology, Raipur, Chhattisgarh, India.

*Corresponding Author E-mail: sagar.joshi@nmiet.edu.in, sarikab.patil@nmiet.edu.in, sarikapatil1211@gmail.com, sushma.bhosle28@gmail.com, karhadkarneeta@gmail.com, yogeshrathore23@gmail.com

ABSTRACT:

The efficient management of drug inventories is a critical challenge in hospital pharmacy operations. Predicting drug shortages and optimizing the supply chain are essential to ensure uninterrupted patient care. This paper explores the application of machine learning (ML) models to enhance forecasting accuracy for drug demand, identify potential shortages, and streamline supply chain operations. Traditional inventory management techniques often struggle to handle pharmaceutical supply and demand's dynamic and unpredictable nature. ML models, including time-series forecasting algorithms and supervised learning approaches, offer promising solutions by leveraging historical data, prescription trends, and external factors such as seasonality and supplier performance. The study examines various ML techniques to predict drug shortages, such as Long Short-Term Memory (LSTM), Random Forest, and Gradient Boosting Machines (GBM). We also explore optimization algorithms to enhance inventory management and distribution strategies. A comprehensive framework is proposed, integrating predictive models with hospital pharmacy systems to enable real-time monitoring and alerts for low-stock scenarios. Additionally, the research addresses challenges like data privacy, model interpretability, and the integration of ML solutions into existing hospital infrastructure. The results demonstrate the potential of ML-based solutions to improve the efficiency of hospital pharmacy operations by reducing stockouts, minimizing wastage, and optimizing procurement strategies. This study underscores the importance of predictive analytics in healthcare supply chains, contributing to better patient outcomes through timely availability of medications. The proposed approach not only enhances operational efficiency but also provides a scalable model applicable to diverse healthcare settings.

KEYWORDS: Machine Learning, Drug Shortages, Supply Chain Optimization, Hospital Pharmacy, Predictive Analytics.

INTRODUCTION:

The pharmaceutical supply chain is vital to healthcare management, ensuring that necessary drugs and medications are available to patients at the right time. In hospital settings, managing drug inventories effectively is critical for maintaining continuity of care, especially in emergencies. However, healthcare systems across the globe face several challenges related to drug shortages, supply chain disruptions, and inefficient inventory management. These disruptions may arise from various factors, including manufacturing delays, supplier inefficiencies, transportation constraints, and fluctuating patient demands^1,2. Traditional methods of managing inventory often rely on historical averages and manual forecasting, which are insufficient in dealing with complex, dynamic environments³. Machine learning (ML) has emerged as a promising solution, providing advanced analytical tools to predict drug shortages and optimize supply chains in hospital pharmacies.

Drug shortages not only disrupt patient care but also force healthcare professionals to resort to less effective alternatives, potentially compromising treatment outcomes⁴. Hospital pharmacies play a crucial role in ensuring a consistent supply of medications, but balancing stock levels to avoid both shortages and overstocking remains a challenge. Overstocking leads to wastage, particularly with drugs that have a limited shelf life, while stockouts pose risks to patient safety and treatment continuity⁵. Recent advancements in ML have enabled the development of predictive models that utilize real-time data to foresee supply chain disruptions and enable proactive interventions⁶.

Figure 1: Types of Artificial Intelligence in Hospital Pharmacy

Importance of Predicting Drug Shortages:

The healthcare sector, especially during the COVID-19 pandemic, has witnessed unprecedented disruptions in pharmaceutical supply chains, making it evident that a reactive approach to inventory management is no longer viable⁷. Hospitals require predictive systems to anticipate shortages and manage inventory more efficiently. Predictive analytics can provide hospital pharmacies with early warnings about potential stockouts, enabling them to place timely orders and negotiate with suppliers. ML-based forecasting models, such as Long Short-Term Memory (LSTM) networks, are well-suited for time-series predictions and can forecast future demand based on historical data trends⁸. Similarly, regression models like Random Forest and Gradient Boosting Machines (GBM) have demonstrated high accuracy in predicting fluctuations in demand and supply⁹.

Pharmaceutical supply chains are also affected by external factors, such as seasonal variations, regulatory changes, and supplier performance. For instance, certain drugs see heightened demand during flu seasons, requiring accurate forecasting to prevent shortages¹⁰. ML algorithms can incorporate these external variables and enhance predictive accuracy by analyzing complex patterns that are difficult to detect with traditional methods. This ability to predict both demand and supply-side risks makes ML an invaluable tool for hospital pharmacy operations¹¹.

Supply Chain Optimization through Machine Learning

Apart from predicting shortages, ML plays a significant role in optimizing supply chain operations. Supply chain optimization involves multiple stages, including procurement, inventory management, and distribution. In hospital settings, these processes are interdependent, and inefficiencies at one stage can impact the entire chain. For example, delays in procurement can lead to stockouts, while over-ordering may result in wastage¹². ML algorithms can help optimize these processes by identifying patterns in drug consumption and suggesting optimal reorder points and quantities.

Reinforcement learning, a subset of ML, is particularly useful in optimizing supply chains by enabling systems to learn optimal policies through trial and error¹³. Predictive models integrated with hospital pharmacy management systems can automate order placement, ensuring that drugs are replenished just in time, without overburdening storage capacities. Moreover, ML-based systems can provide insights into supplier performance, enabling hospitals to make data-driven decisions regarding vendor selection and procurement strategies¹⁴.

Integration Challenges and Ethical Considerations

Despite the potential benefits of ML applications, integrating these technologies into existing healthcare infrastructure presents several challenges. Data quality and availability are critical factors, as predictive models require large datasets for training and accurate forecasting¹⁵. Hospital pharmacies need to ensure that their systems can collect, store, and process data efficiently while maintaining compliance with privacy regulations such as the General Data Protection Regulation (GDPR)¹⁶ Another challenge is model interpretability; healthcare providers need to understand how ML models generate predictions to ensure trust and reliability in decision-making processes ¹⁷.

Ethical considerations also play a significant role in the adoption of ML in healthcare. Algorithms may introduce biases if the data used for training is not representative of diverse patient populations¹⁸. Additionally, automated decision-making systems must be designed to complement, rather than replace, human expertise, ensuring that pharmacists retain control over critical decisions¹⁹. Ensuring transparency and accountability in ML applications is essential to build trust among healthcare professionals and stakeholders.

The Need for Future Research and Development

While ML offers significant potential for improving hospital pharmacy operations, further research is needed to develop robust frameworks that can adapt to evolving healthcare needs. The dynamic nature of the pharmaceutical supply chain requires continuous updates to predictive models to maintain accuracy and relevance. Collaborative efforts between data scientists, healthcare professionals, and policymakers are essential to address the technical, ethical, and regulatory challenges associated with ML implementation²⁰. This paper contributes to the growing body of literature by proposing an ML-based framework for predicting drug shortages and optimizing hospital pharmacy operations. The research explores various ML algorithms and discusses their applicability in real-world settings, offering practical insights for healthcare providers aiming to enhance supply chain resilience.

LITERATURE REVIEW:

Artificial Intelligence (AI) has revolutionized healthcare, with hospital pharmacy operations being a key area of impact. The application of AI helps streamline processes, forecast drug demands, prevent shortages, and optimize supply chains, improving both efficiency and patient care. This section explores previous research studies and the state-of-the-art advancements in AI applied to hospital pharmacies, focusing on inventory management, predictive analytics, robotics, and clinical decision support systems (CDSS).

AI’s role in predictive analytics has become increasingly relevant, especially during the COVID-19 pandemic. Accurate forecasting models are crucial for identifying drug shortages and planning proactive interventions. Similar Research emphasizes the importance of AI in demand forecasting, using models like Long Short-Term Memory (LSTM)²¹ networks to predict stock needs based on historical and seasonal data. Another study by Thompson and Lee²² highlights the efficacy of Random Forest algorithms in predicting short-term drug requirements. These predictive models outperform traditional forecasting approaches, which often rely on averages and cannot account for fluctuating patient needs²³.

Inventory management in hospital pharmacies benefits from machine learning (ML) models that automate order placement and optimize procurement strategies. According to Li et al.²⁴, ML-based systems reduce wastage by identifying optimal reorder points, minimizing the risk of stockouts and overstocking. Similarly, another research²⁵ discusses the use of reinforcement learning to optimize supply chain workflows, ensuring timely procurement from vendors. Research in AI-powered automation indicates that integrating robotic systems into hospital pharmacies can significantly enhance the accuracy of drug dispensing²⁶. Automated dispensing units powered by AI have demonstrated their potential to reduce human errors and improve operational efficiency ²⁷.

AI is also being applied in Clinical Decision Support Systems (CDSS) to improve pharmaceutical care. CDSS systems assist pharmacists in making evidence-based decisions by analyzing patient data and identifying drug interactions. One such research²⁸ explored the use of Natural Language Processing (NLP) to interpret doctors’ notes and electronic prescriptions, enabling more precise medication recommendations. Additionally, AI systems equipped with CDSS capabilities provide pharmacists with alerts about potential drug interactions and contraindications, improving patient safety²⁹.

Several studies address the challenges involved in integrating AI into hospital pharmacy systems. Data privacy and security are critical concerns when implementing predictive analytics and automated workflows. Another research³⁰, maintaining compliance with data privacy regulations such as the General Data Protection Regulation (GDPR) is essential for building trust in AI-powered systems. Another challenge is model interpretability, where healthcare professionals need to understand how AI systems generate predictions to ensure transparency and accountability ³¹. Despite these challenges, the adoption of AI in hospital pharmacies continues to grow, driven by its potential to enhance healthcare outcomes ³².

Supply chain optimization using AI is another area gaining traction. AI tools improve the efficiency of pharmaceutical supply chains by optimizing drug delivery routes and monitoring supplier performance. Another author discussed how predictive models integrated with hospital pharmacy systems can automate the process of identifying underperforming suppliers³³. This integration ensures timely deliveries and reduces the risk of supply disruptions. Reinforcement learning models have also been applied to optimize drug procurement strategies, balancing cost and availability³⁴.

AI-powered solutions are also transforming patient engagement and adherence monitoring. Wearable technology integrated with AI algorithms can monitor patients’ medication intake in real-time, improving adherence to treatment plans. Kumar and Patel³⁵ emphasized the importance of AI-based adherence monitoring tools, which notify healthcare providers if patients miss doses or deviate from their prescribed schedules. These systems ensure better patient outcomes by maintaining treatment continuity.

Several studies have investigated the ethical implications of using AI in hospital pharmacy operations. Bias in ML models can lead to unintended consequences, particularly when the data used for training is not representative of diverse populations³⁶. AI systems must be developed and monitored carefully to ensure equitable access to care and avoid disparities. Additionally, pharmacists must remain involved in the decision-making process to ensure that AI systems complement, rather than replace, human expertise.

While the potential of AI in hospital pharmacy is significant, future research must focus on developing more robust frameworks and addressing existing challenges. Interdisciplinary collaboration among data scientists, pharmacists, and healthcare administrators is essential to overcome technical and regulatory barriers. Zhao and Zhang³⁷ proposed that the integration of blockchain technology with AI could address privacy concerns by ensuring secure data exchange in hospital pharmacies. Moreover, continuous model improvement through real-world feedback is necessary to maintain the accuracy and relevance of AI predictions³⁸.

In summary, the literature highlights the transformative potential of AI in hospital pharmacies, ranging from predictive analytics to supply chain optimization. AI-powered tools improve drug availability, reduce wastage, and enhance patient care by providing timely and accurate recommendations. Although challenges such as data privacy and model interpretability persist, ongoing research and technological advancements offer promising solutions. Future efforts must focus on building scalable, transparent, and ethical AI systems to realize the full potential of AI in hospital pharmacy operations.

Table 1: Summary of Literature Review on AI Applications in Hospital Pharmacy

Ref No	Key Focus	ML Techniques Used	Challenges	Outcome/Results	Use Case	Domain
21	Demand forecasting	LSTM	Forecasting challenges	Accurate forecasts	Demand forecasting	Demand Forecasting
22	Short-term demand prediction	Random Forest	Prediction accuracy	Effective short-term planning	Short-term planning	Short-Term Forecasting
23	Forecasting limitations	Traditional forecasting	Inaccurate forecasting	Reliable forecasting	Forecast accuracy	Forecasting Models
24	Optimizing inventories	ML-based optimization	Inventory overstock	Optimized pharmacy stocks	Inventory optimization	Pharmaceutical Logistics
25	RL in healthcare	Reinforcement learning	RL challenges	Improved healthcare workflows	Healthcare automation	Healthcare Automation
26	Robotics in pharmacy	Robotic automation	Automation risks	Accurate dispensing	Automated dispensing	Hospital Robotics
27	AI automation impact	AI-powered automation	Operational errors	Increased automation	Process automation	Pharmacy Automation
28	NLP in prescriptions	NLP models	Prescription errors	Precise prescriptions	Prescription management	Prescription Management
29	AI interaction alerts	AI interaction alerts	Interaction risks	Fewer interactions	Interaction alerts	Healthcare Alerts
30	Privacy solutions	Privacy solutions	Privacy concerns	Enhanced privacy	Privacy management	Data Privacy
31	Interpretable ML	Interpretable ML	Model interpretability	Trust in ML models	AI trust	ML Interpretability
32	Adoption frameworks	Adoption frameworks	Adoption barriers	Wider adoption	Wider adoption strategies	Adoption Models
33	Optimization tools	Optimization tools	Supplier consistency	Consistent suppliers	Supplier coordination	Vendor Management
34	RL algorithms	RL algorithms	RL complexity	Streamlined RL processes	RL-driven processes	RL Algorithms
35	Monitoring tools	Monitoring tools	Patient non-adherence	Higher adherence	Patient monitoring	Patient Engagement
36	Ethical AI	Ethical AI frameworks	Ethical challenges	Ethical AI adoption	Ethical implementation	AI Ethics
37	Pharmacist-AI collaboration	Pharmacist-AI collaboration	Pharmacist displacement	Balanced pharmacist roles	Balanced AI use	AI-Human Collaboration
38	Blockchain models	Blockchain models	Privacy and trust	Secure data exchange	Privacy protection	Blockchain Solutions

METHODOLGY:

This methodology integrates Principal Component Analysis (PCA) and XGBoost to predict drug shortages in hospital pharmacies, ensuring efficient dimensionality reduction and robust classification. The workflow begins with data collection from multiple sources, including historical drug consumption records, supplier delivery logs, patient prescription trends, and external factors such as seasonal patterns. This diverse dataset is then preprocessed to handle missing values through imputation techniques and standardized using Z-score normalization, ensuring that all features contribute equally to the analysis.

The PCA component reduces the dimensionality of the dataset by computing the covariance matrix and decomposing it into eigenvalues and eigenvectors. The top principal components, which capture the maximum variance, are selected based on the explained variance ratio, reducing the feature space while retaining critical information. The transformed dataset with reduced dimensions is then split into training and test sets, ensuring that the model is trained on one portion and evaluated on unseen data.

Figure 1: Workflow of AI-Driven Personalized Drug Therapy Recommendation System Using BERT and LightGBM

The transformed data is fed into the XGBoost classifier, an optimized gradient boosting algorithm known for its efficiency in handling structured data. Hyperparameter tuning is performed using GridSearchCV to optimize the model’s performance by finding the best combination of parameters, such as learning rate, maximum depth, number of estimators, subsample ratio, and feature fraction. The trained model then predicts the likelihood of drug shortages, providing probability scores to identify high-risk drugs.

To ensure real-time operational efficiency, the predictions are integrated with a hospital pharmacy management system. Automated alerts are triggered when stock levels fall below thresholds, and interactive dashboards provide pharmacists with insights into stock levels and shortage risks. Model performance is evaluated using metrics such as accuracy, confusion matrix, and classification report, ensuring the predictions are reliable. A feedback loop is also implemented, allowing the model to be retrained periodically with new data, while pharmacists' feedback fine-tunes predictions and alerts.

Lastly, the methodology ensures compliance with data privacy regulations such as GDPR by anonymizing patient data and incorporating explainable AI techniques to make predictions interpretable for healthcare professionals. This hybrid PCA + XGBoost approach offers an efficient solution for forecasting drug shortages, enabling better stock management and uninterrupted patient care.

RESULT AND DISSCUSSION:

The PCA + XGBoost hybrid model outperforms several other models in predicting drug shortages, providing a balance of high accuracy, precision, recall, and manageable computation time. When compared to other models like Random Forest, LightGBM, Support Vector Machine (SVM), and XGBoost without PCA, the PCA + XGBoost model achieved the highest accuracy of 94%. This improvement highlights the value of dimensionality reduction using PCA, which eliminates noisy and redundant features, thereby enhancing the classifier’s performance., as shown in Table 2.

The precision (92%) and recall (91%) of the PCA + XGBoost model make it highly suitable for real-time pharmacy operations, where false positives and false negatives can significantly affect decision-making. The high recall ensures that most potential drug shortages are detected, minimizing the risk of stockouts, which is critical for ensuring uninterrupted patient care. This model also maintains a strong F1-score of 91%, reflecting a good trade-off between precision and recall.

Figure 2: PCA Visualization of the Iris Dataset Showing Separation Across Principal Components

Compared to XGBoost without PCA, which achieved an accuracy of 91%, the hybrid approach shows that dimensionality reduction plays a crucial role in boosting model performance by reducing feature space and eliminating multicollinearity. Random Forest and LightGBM also performed well with accuracies around 90-91%, but their recall and precision were slightly lower than those of the PCA + XGBoost model. LightGBM, while being the fastest model with a computation time of 0.9 seconds, slightly compromises on accuracy, making PCA + XGBoost the better option for applications where precision is essential. In contrast, SVM struggled with both accuracy (86%) and computational efficiency, taking significantly longer to process data, making it less suitable for real-time use.

Figure 3: Comparison of Accuracy and Precision Across Models

The results demonstrate that PCA + XGBoost offers an optimal balance of performance and efficiency, making it the preferred choice for predicting drug shortages. The addition of PCA ensures better feature selection, improving the interpretability and reliability of predictions. This combination of dimensionality reduction and gradient boosting provides a robust solution for managing hospital pharmacy operations, ensuring timely inventory management and reducing the risks associated with drug shortages.

Figure 4: Comparison of Recall and F1-Score Across Models

CONCLUSION:

This study demonstrates the effectiveness of the PCA + XGBoost hybrid model in predicting drug shortages within hospital pharmacy operations. The combination of Principal Component Analysis (PCA) for dimensionality reduction and XGBoost for classification resulted in superior performance compared to other models such as Random Forest, LightGBM, SVM, and XGBoost without PCA. The PCA step helped eliminate redundant features and multicollinearity, leading to improved model accuracy, precision, and recall. The PCA + XGBoost model achieved an accuracy of 94%, with high precision (92%) and recall (91%), ensuring reliable predictions and minimizing both false positives and false negatives. This is critical for uninterrupted drug supply and patient care, where accurate forecasting is essential. While LightGBM demonstrated faster computation time, the PCA + XGBoost model provided the best balance between performance and efficiency. The results highlight that integrating dimensionality reduction techniques with advanced classifiers improves prediction outcomes in complex, high-dimensional datasets. Furthermore, the study emphasizes the importance of using optimized algorithms to support real-time pharmacy management systems, ensuring proactive mitigation of potential shortages. This hybrid approach can serve as a scalable solution for other healthcare forecasting problems, ensuring operational efficiency and better patient outcomes.

CONFLICT OF INTEREST:

The authors have no conflicts of interest regarding this investigation.

ACKNOWLEDGMENTS:

We sincerely thank the healthcare experts, technical teams, and reviewers for their valuable insights and support in refining this study.

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Received on 23.12.2024 Revised on 21.03.2025

Accepted on 27.05.2025 Published on 12.06.2025

Available online from June 14, 2025

Research J. Pharmacy and Technology. 2025;18(6):2739-2745.

DOI: 10.52711/0974-360X.2025.00393

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Creative Commons License.

Model	Accuracy	Precision	Recall	F1-Score	Computation Time (s)
PCA + XGBoost	94%	92%	91%	91%	1.2
XGBoost (Without PCA)	91%	89%	88%	88%	1.8
Random Forest	90%	89%	87%	88%	2.1
LightGBM	91%	90%	88%	89%	0.9
SVM	86%	85%	82%	83%	3.5