Transforming Healthcare with Electronic Health Records: AI Integration, Evolution, and Future

Electronic Health Records (EHRs) are key components of healthcare system. It contains patient data such as demographics, history of medical diagnosis, medications, clinical reports, medical imaging. This information is indispensable for effective treatment, patient management, and decision-making. Despite largely adopting EHRs, a potential obstacle is fully exploiting their potential, particularly when using structured and unstructured data for research and clinical practice. One can efficiently process structured data, such as laboratory results and medication information, and unstructured data, which constitute most of EHR content (e.g., imaging reports, discharge summaries, etc.), are underutilized in clinical decision support systems. With artificial intelligence (AI) and deep learning (DL) techniques, understanding the underlying patterns of EHRs can improve the utility of the data. This chapter presents the application of AI in extracting, analyzing, and utilizing EHRs and its impact on the healthcare system. AI can change how healthcare systems work, improving patient care, optimizing workflows, and opening up new dimensions in the treatment process. With the rapid number of patients, manual collection and analysis of large amounts of data is tedious, costly, and unfeasible. In this context, natural language processing (NLP) and deep learning (DL) techniques enable the rapid extraction and analysis of critical information from unstructured data, such as medical records, images, and texts. NLP processes language to understand context; DL models learn complex patterns, enhancing efficiency and accuracy in data extraction. Recently, many AI techniques have been helping to detect early diagnosis of diseases such as diabetes, heart disease although large-scale adoption of such methods is significantly challenging. It requires financial capabilities, specific technology transfer schemes, and collaboration with multidisciplinary teams. Similarly, maintaining data privacy, handling complex and heterogeneous health data, developing accurate and complete AI models, mitigate bias, and ensure the robustness and fairness of the model is also challenging. This chapter also discusses emerging trends such as the integration of genomic data, real-time analysis, interpretability, personalized medicine, and dialogue management, all of which will facilitate significant changes in the functionality and efficiency of the healthcare system. Finally, this chapter shows how AI models improve efficiency in clinical healthcare, patient health, and efficient operational procedures, and it also identifies key future research directions.