Data-Driven Intelligence in Electronic Health Records (EHRs)

Beginning with the development of the first EHRs, modern healthcare systems were mobilized and refined access to patient data, treatment, and diagnostics. However, modern EHR systems, despite their widespread adoption, have failed to provide timely, credible, and actionable support to clinical workflows due to issues with complex data, data interoperability, and the lack of intelligent systems. In this chapter, we examine how the incorporation of data-driven intelligent systems with AI, ML, and NLP EHRs can address these barriers to smarter healthcare delivery. We document how AI could augment EHRs functionally for disease prediction, clinical decision support, risk stratification, and personalized care recommendations. We also demonstrate how the structuring of the health records data, both structured and unstructured, can be driven by predictive analytics. The rest of the chapter uses the examples of sepsis and the prediction of post-discharge hospital readmission, situated outside the study location, to illustrate the scenarios for possible AI applications that can augment the quality of healthcare outcomes. The chapter weaves the fabric of AI use for health with privacy, data security, and ethical use of AI across the entire system which emerges as salient healthcare options. More precisely, this discussion has a straight line to the pioneering uses of technologies such as federated learning and blockchain as ways of overcoming privacy-preserving data sharing to collaboratively construct a model, which, pivots to implementation problems like the quality of data, algorithmic data bias, and the growing issues of explainable AI which is a prerequisite to clinician endorsement. This chapter, by illustrating the intersection of contemporary computational paradigms with smart healthcare systems, offers practitioners, developers, researchers, and even the medical workforce a technical blueprint to build comprehensive, intelligent, secure, and highly adaptive EHR systems able to perform real time, individualized care. When compared to other domains like agriculture and information technology, a system-level EHR AI transformation is sorely needed, which includes modular EHR systems with a privacy-performance optimization layer, clinician feedback module, and others, culminating in a simulated pilot run with the MIMIC-IV dataset to demonstrate feasibility and the clinical edge.