Work

Natural Language Processing in Electronic Health Records: Progress, Challenges, and Future Directions

Year: 2025

Type: article

Abstract: Electronic health record systems transformed healthcare documentation by providing a system for storing and sharing extensive patient data. However, much of this information remains in the form of unstructured text, which limits its utility for computational analysis. Natural Language Processing (NLP) has emerged as a prominent approach to extract and structure information from free-text clinical narratives, offering the potential to unlock valuable insights for clinical care, research, and administration. This paper provides an overview of recent advances in NLP methods applied to EHRs, discusses open problems including data quality, privacy, and generalizability, and highlights potential future directions for the integration of NLP into clinical workflows. The conclusions point to the need for continued development of domain-specific language models, privacy-preserving techniques, and explainable AI methods to fully harness the power of NLP for healthcare transformation.

Source: Saudi Journal of Medicine

DOI: 10.36348/sjm.2025.v10i09.006

Authors: Faisal Mansour Alanazi, Meshari Ali Aljjedaee, Shalah Al Harbl, Ahmed Abdullah Alsharekh, Dheifallah Alrashidi

Institution: National Guard Health Affairs

Language: English

Topic: Biomedical Text Mining and Ontologies

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: diamond

OpenAlex ID: W4415019738