Browsing by Author "Luvanda, Anthony"

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Architectural Health Data Standards and Semantic Interoperability: A Comprehensive Review in the Context of Integrating Medical Data into Big Data Analytics
(International Journal of Engineering Applied Sciences and Technology, 2023-03-26) Tsinale, Harriet Loice; Mbugua, Samuel Mungai; Luvanda, Anthony
The integration of medical data into Big Data analytics holds significant potential for advancing healthcare practices and research. However, achieving semantics interoperability, wherein data is exchanged and interpreted accurately among diverse systems, is a critical challenge. This study explores the impact of existing architectures on semantics interoperability in the context of integrating medical data into Big Data analytics. The study highlights the complexities involved in integrating medical data from various sources, each using different formats, data models, and vocabularies. Without a strong emphasis on semantic interoperability, data integration efforts can result in misinterpretations, inconsistencies, and errors, adversely affecting patient care and research outcomes. The significance of data standards and ontologies in establishing a common vocabulary and structure for medical data integration is underscored. Additionally, the importance of data mapping and transformation is discussed, as data discrepancies can lead to data loss and incorrect analysis results. The success of integrating medical data into Big Data analytics is heavily reliant on existing architectures that prioritize semantics interoperability. A well- designed architecture addresses data heterogeneity, promotes semantic consistency, and supports data standardization, unlocking the transformative capabilities of medical data analysis for improved healthcare outcomes.
Effective approaches for enhancing data management and semantic interoperability within the healthcare sector
(International Journal of Applied Research, 2023-08-04) Tsinale, Harriet Loice; Mbugua, Samuel Mungai; Luvanda, Anthony
In a variety of contexts, Big Data is widely used. In healthcare Big Data has its own characteristics, including heterogeneity, incompleteness, timeliness and durability, privacy, and ownership. To enhance health-related science, these features present a number of challenges for data storage, mining, and sharing. Big Data helps to understand electronic health records, gather demographic and medical data such as clinical data, medical conditions and diagnosis, allow doctors to provide a wide variety of patients with quality health care and also to make informative decisions. The large volume of data also provides researchers in the fields of medicine and healthcare with the ability to use tools and techniques to unlock hidden solutions. This study sought to investigate the effects that the existing approaches have on enhancing data management and semantic interoperability in healthcare sector. 180 respondents who took part of the survey were chosen from the Kenyatta University Teaching, Referral & Research Hospital. It was discovered that implementing health standards and tools can help healthcare companies in a number of ways by eliminating compatibility concerns and assuring accurate data representation. Consequently, encouraging successful adoption boosts decision-making processes, encourages sustainability, improves data quality, allows for interoperability, and makes it easier to comply with regulations. These implications help improve patient care, healthcare systems, and overall health outcomes