BERT-Driven Natural Language Processing in Electronic Health Records for Improved Clinical Insights

In order to improve clinical insights, this study investigates the incorporation of BERT-driven natural language processing (NLP) in electronic health records (EHRs). Utilizing a descriptive design, interpretivism philosophy, as well as a deductive approach, this research demonstrates the adaptability of BERT in deciphering intricate medical literature. The results show enhanced data extraction efficiency and accuracy, which has a significant impact on medication extraction and disease recognition. Among the practical ramifications are enhanced clinical decision support platforms. Ethical aspects such as prejudices and privacy issues generate attention to the necessity of thorough regulations. Future research on improving interpretability, dealing with ethical issues, and investigating the model's adaptability across healthcare institutions is advised by the study. This study adds to our comprehension of BERT in healthcare while highlighting the critical role that practical and ethical considerations play in ensuring its ethical integration.