Lifeline Analytics: A Predictive Approach to Blood Demand Management

Abstract

Lifeline Analytics addresses the critical challenges in blood supply management by combining advanced data analysis with predictive modeling to optimize both demand forecasting and donor engagement [1],[3]. Drawing from historical utilization records, inventory trends, and behavioral indicators, the platform enables proactive decision-making that minimizes shortages and wastage[4]. Its intuitive, role-specific dashboard unites administrators, clinicians, and donor coordinators through real-time insights, interactive visualizations, and automated alerts [5]. Preliminary deployments have demonstrated measurable improvements in inventory stability and donor retention. Built on a modular, secure architecture, Lifeline Analytics integrates seamlessly with hospital systems while adhering to healthcare data standards. Scalable microservices support ongoing data expansion and rapid analytical updates. Embedded feedback loops ensure model refinement based on user interaction and regional variations. This robust, adaptive framework provides a forward-looking, resilient, and patient-centric approach to blood supply management.

Introduction

Maintaining a stable and equitable blood supply is crucial but challenging due to fluctuating demand, seasonal variations, and inconsistent donor participation. Traditional blood inventory management methods, which rely on static forecasts and manual adjustments, often lead to shortages or overstocking, impacting patient care.

Lifeline Analytics offers an advanced, machine learning–driven platform that proactively manages blood supply by analyzing historical transfusion data, inventory trends, donor demographics, and engagement metrics. Its hybrid predictive engine integrates Random Forest regression, ARIMA time-series models, and Artificial Neural Networks to capture both short-term and long-term demand patterns. The platform also includes explainable AI and donor segmentation to improve transparency and optimize personalized donor outreach.

Delivered via a modular dashboard, Lifeline Analytics supports real-time monitoring, automated alerts, and tailored communication for clinicians, administrators, and donors. This enables a shift from reactive inventory management to strategic, data-informed decision-making that enhances supply-demand alignment, reduces waste, and improves donor retention.

The system collects and preprocesses comprehensive data from hospital EHRs and blood banks, applying ensemble modeling to maximize forecasting accuracy. Donor response is predicted through classification models like logistic regression, decision trees, and XGBoost, focusing on behavioral and demographic factors to boost engagement.

Evaluations demonstrate that the hybrid forecasting models outperform single approaches by combining strengths across algorithms, achieving high prediction accuracy. The donor classification models effectively identify likely responders to enable personalized outreach. Users find the dashboard intuitive and responsive, with automated alerts improving communication efficiency.

Overall, Lifeline Analytics exemplifies a scalable, integrated approach to blood supply management that leverages machine learning, real-time data, and user-friendly tools to build a resilient, efficient, and ethical healthcare resource system.

Conclusion

Lifeline Analytics introduces a comprehensive framework for transforming blood supply management through the integration of predictive modeling and data-informed donor engagement. By leveraging historical usage patterns, behavioral insights, and advanced forecasting techniques, the system enables blood banks to shift from reactive operations to proactive planning. The platform’s modular, API-ready architecture ensures compatibility with existing healthcare infrastructures, supporting real-time adaptability and seamless integration. The incorporation of explainable AI fosters transparency in model outputs, while the automated outreach module enhances donor responsiveness and operational coordination. Beyond minimizing inventory imbalances and mitigating shortages, the solution provides strategic value to both administrative and clinical stakeholders. Designed with scalability and resilience in mind, Lifeline Analytics establishes a foundation for future developments in healthcare analytics, supporting improved resource planning under routine conditions and during emergency scenarios.

References

[1] Y. Tian et al.., “Simulating the blood transfusion system in Kenya: Modelling methods and exploratory analyses,” Preprint, Oct. 2024. [2] S. Kaur, P. Singh, and A. Sharma, “Blood demand forecasting using hybrid ARIMA–LSTM model: A case study in Indian blood banks,” Computers in Biology and Medicine, vol. 154, p. 106506, 2023. [3] M. Elsayad and H. A. Hefny, “Machine learning-based prediction of blood donors’ response behavior,” Journal of Biomedical Informatics, vol. 139, p. 104328, 2023. [4] J. G. Almeida, T. Silva, and R. Henriques, “A demand prediction model for healthcare logistics: The case of blood products,” Health Systems, vol. 12, no. 2, pp. 113–127, 2023. [5] A. Anwar, K. Nazir, and S. Rizwan, “Smart blood bank system using machine learning and real-time dashboards,” in Proc. IEEE Int. Conf. on e-Health and Bioengineering (EHB), Ia?i, Romania, Nov. 2023, pp. 1–6. [6] A. B. Khan, M. N. Qureshi, and S. S. Pathan, “A predictive analytics framework for managing blood supply chains using machine learning techniques,” Journal of Ambient Intelligence and Humanized Computing, vol. 14, no. 1, pp. 221–235, 2023. [7] L. Zhang, Y. Li, and M. Zhao, “Deep learning-based demand forecasting of blood products in clinical settings,” Expert Systems with Applications, vol. 214, p. 119063, 2023. [8] D. Prasanna, R. Chatterjee, and V. Venkatesh, “Artificial intelligence for smart blood supply chain management: A comprehensive review,” IEEE Access, vol. 10, pp. 87645–87658, 2022.

Copyright

Copyright © 2025 Prof. Sharvani V, Dr. Girish Kumar D, Mrs. Tunkarapalli Swetha, Prof. Subhashree D C, Prof. M M Harshitha. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.