Effective approaches for enhancing data management and semantic interoperability within the healthcare sector

Abstract

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.