Towards Secure Hospital Data Systems: Real-Time Anomaly Detection and Integrity Management with AI and Blockchain

Abstract

The research introduces a fresh hybrid architecture integrating Artificial Intelligence (AI) and Blockchain technology to achieve real-time anomaly detection and to ensure data integrity in medical scenarios. The LSTM-CNN motivated model returned results of 95% accuracy and 94% F1-score along with 93% recall, underscoring its enhanced capacity for spotting both illegal data access and suspicious medical dealings. The experimental setup led to an analysis of a synthetic hospital dataset comprising 26,000 items, which included patient admissions, billing transactions, and medical records, with 5.3 % of the data specifically designed to include anomalies to assess the model's performance. After deploying the Blockchain on Ethereum, we ensured that the data was immutable and secure, completely removing cases of data tampering and unauthorized alterations, which descended from 5 and 8 incidents before the installation to zero following it. Also, the solution showed that reductions in validation failures went from 7 to 1, illuminating how smart contracts automate data validation processes. Results from latency studies demonstrate that the system is capable of managing real-time transactions with an average delay of 3.2 seconds, establishing its suitability for fluid medical environments that demand fast access to data. The research points out that the proposed framework may considerably increase data security and operational efficiency within healthcare contexts. The study generates a firm framework for developing more secure and trustworthy hospital data management systems, while future efforts will concentrate on enhancing the framework for broader deployment and improving its scalability to deal with increasingly complex healthcare data scenarios.