Background: Blood Supply Chain Management (BSCM) faces the dual, critical challenges of ensuring product availability and safeguarding sensitive donor and patient data. Inefficiencies in demand forecasting lead to costly wastage or life-threatening shortages, while the increasing digitization of healthcare logistics exposes the system to significant security vulnerabilities. Although machine learning (ML) and data encryption have been addressed as separate solutions in healthcare, there is a notable absence of integrated frameworks that concurrently tackle both predictive accuracy and data integrity within the unique constraints of the BSCM.

Methods: This study proposes and evaluates a novel, integrated framework to address this gap. We developed a Long Short-Term Memory (LSTM) network, a deep learning model, to forecast the demand for blood products using a time-series dataset. For data security, we designed and implemented a hybrid encryption protocol combining the Advanced Encryption Standard (AES-256) for bulk data encryption with the Rivest-Shamir-Adleman (RSA-2048) algorithm for secure key exchange. The performance of the integrated system was evaluated based on the ML model's forecasting accuracy (Mean Absolute Percentage Error - MAPE), and the computational overhead (latency) of the encryption scheme.

Results: The LSTM forecasting model demonstrated high accuracy, achieving a MAPE of 6.8% on the test dataset, significantly outperforming traditional baseline models. The hybrid encryption protocol proved to be highly efficient, introducing an average computational overhead of only 45 milliseconds per transaction for a standard data packet. This minimal latency confirms the framework's viability for real-time deployment without compromising system responsiveness.

Conclusion: The integrated framework provides a robust and feasible solution for creating a more intelligent, secure, and efficient blood supply chain. By synergistically combining predictive analytics with strong cryptographic protections, this research offers a practical blueprint for modernizing critical healthcare logistics systems, ultimately leading to improved resource management and patient outcomes.

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