Natural Language Processing in Electronic Health Records: Extracting Actionable Insights from Unstructured Data

Abstract

One of the biggest data sources in today's healthcare environment, electronic health records (EHRs), remain underutilized for analytical purposes. Physician notes, discharge summaries, radiology, and operative notes are examples of free-text narrative that contain a large portion, perhaps 80%, of clinically relevant information. The need for structured, computable knowledge has led to the development of a leading computational technique: natural language processing (NLP). This paper reviews and provides empirical analysis of NLP applications in EHR contexts, including clinical named entity recognition, relation extraction, automated ICD coding, de-identification and patient-cohort phenotyping. Five benchmark tasks are used to compare transformer-based architectures—ClinicalBERT, BioBERT, and GatorTron—with two traditional rule-based and two machine-learning baselines. Our comparative experiments show that the domain-adapted models are generally at least 4–8 percentage points better, in terms of F1-score. We also explore the adoption history of NLP approaches spanning 2017–2023, and observe a quick evolution from rule-based systems to deep-learning systems, driven by the introduction of contextual embeddings. Ethical issues such as privacy in data collection (HIPAA and GDPR), fairness in algorithms, and the need for interpretability for clinical use are discussed. The paper ends with a forward-looking discussion of open challenges, including federated learning, low-resource clinical languages, and LLMs that will drive the future of EHR intelligence platforms..