DATA RETRIEVAL FOR CLINICAL PROJECTS IN THE EVOLVING HEALTHCARE SYSTEM: PAST, PRESENT, AND FUTURE

Data retrieval underpins the success of clinical audits, quality improvement initiatives, and research. Over recent decades, healthcare systems have transitioned from manual chart review to electronic patient record (EPR) systems, informatics-driven queries, and now artificial intelligence (AI)-assisted methods. Each stage has brought improvements in scale and efficiency but has also introduced new challenges in accuracy, coding reliability, and access to clinically meaningful detail. This review traces the evolution of data sourcing methods, from manual extraction to digital workflows, examines the balance between structured and unstructured data, and highlights the emerging role of prompt engineering and natural language processing (NLP). By combining insights from literature and clinical practice, it outlines the limitations of current systems and the opportunities that future innovations may provide.