The Reality of Applying Self-learning Databases in East Jeddah General Hospital as Model

Abstract

The study addressed the latest scientific developments in the field of databases and their application at East Jeddah General Hospital. It focused on self-learning databases based on artificial intelligence, which operate without any human intervention or guidance. The study discussed various aspects, including concepts, importance, components, and application models of these databases. It also categorized types of databases based on the level of human intervention, referred to as levels of autonomy.

The study identified the research problem in the main question: What is the level of autonomy of the database management system in East Jeddah General Hospital? Its objectives centered on determining the level of autonomy of the database management system in the hospital and proposing a model for building a self-learning database. To achieve these objectives, the study utilized descriptive content analysis and case study methodologies, employing a questionnaire as a tool to gather information from the Information Technology department employees at East Jeddah General Hospital, totaling 10 participants.

Among the study's key findings were that the current database system implemented in East Jeddah General Hospital is at the second level (mixed level), and the hospital possesses the necessary resources to enhance the current database and transform it into a self-learning database, considering factors such as experience, age, specialization, and scientific qualification of employees. The study also highlighted the employees' awareness of the importance of implementing self-learning databases, with 100% believing in their contribution to improving work and supporting decision-making.

The study recommended that East Jeddah General Hospital should keep pace with the developments in the field of databases, transitioning from the mixed level to the level of self-learning databases. Additionally, it emphasized the need to shed light on the capabilities offered by self-learning databases, recognizing it as a modern and extensive field for further research and studies.