How is the unstructured data typically characterized in healthcare?

Unstructured data in healthcare is characterized as raw data that requires further analysis because it does not conform to a predefined data model or structure. This type of data includes various forms of information such as free-text notes, clinical narratives, audio recordings, images, and social media interactions. Unlike structured data, which is neatly organized in rows and columns, unstructured data is often ambiguous and heterogeneous, making it challenging to analyze without applying data processing and analysis techniques. Analysts typically need to employ natural language processing, machine learning, or other analytical methods to extract useful information from this raw data, thereby enhancing its utility for clinical decision-making, research, and operational efficiencies. Understanding this characteristic helps in appreciating the significant effort needed to convert unstructured data into meaningful insights, which is crucial for improving healthcare outcomes.