Abstract

The healthcare industry is increasingly relying on data-driven insights for improving patient care and clinical decision-making. However, the complexity of healthcare data, particularly unstructured clinical notes and electronic health records (EHRs), presents significant challenges for traditional data processing systems. This paper introduces a novel approach that leverages custom Natural Language Processing (NLP) pipelines, integrated with FastAPI microservices, to enhance healthcare data intelligence. By utilizing domain-specific models like ClinicalBERT, the system efficiently processes unstructured text, extracting critical medical information such as diagnoses, symptoms, and treatments, and enabling real-time decision-making. The proposed architecture offers scalability, modularity, and low-latency performance, addressing the shortcomings of legacy systems. Furthermore, containerization using Docker and continuous integration/continuous deployment (CI/CD) pipelines ensure seamless deployment and system maintenance. Performance metrics, including latency, throughput, and NLP accuracy, are evaluated to demonstrate the efficacy of the system across various healthcare datasets. A real-world use case, such as clinical note triage, is presented to illustrate the practical impact of the system. Despite its promising results, limitations such as model generalizability and multilingual support are discussed, with directions for future research aimed at expanding the system’s applicability. This paper contributes to the field of healthcare data intelligence by offering a scalable, flexible solution that improves clinical decision support and patient outcomes.