Información Clínica en Español: Cómo Aprovecharla sin Poner en Riesgo la Privacidad

Abstract

The growth of unstructured clinical information, such as medical records, clinical notes, and narrative reports, has created new challenges for its organization, analysis, and use in the healthcare sector. In this context, natural language processing (NLP) and language models have become key tools for extracting, structuring, and protecting the relevant information contained in medical texts. This popular science article was developed through a descriptive, qualitative literature review of fifty academic, technical, and regulatory sources, with the aim of explaining the importance of unstructured data in healthcare, describing the main applications of NLP in this field, and analyzing its most significant challenges, such as model accuracy, patient privacy, and the de-identification of sensitive information. The findings show that much of the most valuable clinical information resides in free text, that transformer-based techniques and language models can extract it with growing accuracy also in Spanish and that de-identification and interoperability are indispensable conditions for its ethical use. It is concluded that unstructured data, when handled securely, represents a real opportunity to strengthen research, support clinical decision-making, and modernize health information systems.