Design and Development of Medical Databases

Since the Fourth Industrial Revolution was declared at the World Economic Forum in 2016, we have witnessed significant technological innovations across various fields. Artificial intelligence (AI) technology has developed alongside computing technology, algorithms, and platforms, but its most crucial foundation lies in data collection and management capabilities. This paper aims to share expertise gained through our experience in constructing medical databases, including the Hearing Big Data Center, Common Data Model implementation, and balance function test databases. We first explain the fundamental concepts of databases, distinguishing between structured and unstructured data, and introduce relational database models and data schemas particularly relevant to otolaryngology. We then describe practical strategies for designing and operating medical databases that can flexibly handle both structured and unstructured data, using hearing and vestibular function test data as representative examples. Additionally, we address important considerations for healthcare database construction, including privacy regulations, de-identification processes, and data combination techniques according to South Korean guidelines. Finally, we discuss how well-designed medical databases can be linked with AI and machine learning models, enabling multimodal analysis that integrates clinical data, imaging, and physiological signals. We argue that by understanding database design principles and proper data management approaches, medical researchers can more efficiently utilize vast medical data resources while maintaining patient privacy, ultimately advancing medical research and clinical practice in the era of AI.