Integration and analysis of diverse healthcare data sources: A novel solution

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Contemporary healthcare analytics requires informed decision-making through seamless integration, correlation, and curation of diverse data from sources like clinical trials, research publications, ubiquitous devices, and standard terminologies. Modern healthcare systems need to monitor temporal changes, manage key features, and deliver robust search capabilities, extending beyond electronic health records. However, existing systems lack readiness for comprehensive healthcare analytics tasks, necessitating a sophisticated solution. Our work introduces a groundbreaking comprehensive framework for managing, integrating, and processing continuously evolving healthcare data, with a focus on establishing an efficient architecture for data processing and ensuring interoperability and consistency. We incorporate a time dimension to capture critical changes for efficient data analysis and decision-making, extending from clinical trials to mapping clinical trial data to clinical research. Moreover, we curate disparate datasets, including trials, academic publications, standard medical terms, concepts, and ubiquitous device data. Employing highly efficient algorithms and methods, we optimize time and space complexity, validating the feasibility of our proposed solution. Our results demonstrate maximum linear change detection and update processing latency, showcasing efficiency compared to state-of-the-art methods. Additionally, our methods for profiling crucial entities in clinical trial data achieve consistent average accuracy, notably with the VSM model. This innovative approach significantly advances meeting dynamic requirements in contemporary healthcare analytics, particularly in clinical trials.