AI-Engineered EHR Intelligence for Precision Care
DOI:
https://doi.org/10.5281/299k3v91Keywords:
Clinical Data Engineering; Clinical Data Lake and Data Warehouse; EHR Analytics; EHR as Data Source; Data Compression in EHR; Precision Healthcare Decision Systems; Dynamic Scalable Architecture; Markov Decision Process; Reinforcement Learning.Abstract
The growing availability of clinical data across Electronic Health Records (EHR) systems has created an urgent need for AI-driven data engineering solutions that enable scalable EHR analytics and precision healthcare decision-making. To address this need, this chapter presents a scalable, AI-driven clinical data engineering framework that automatically curates and prepares clinical data for a wide range of downstream applications.
The framework is built around a clinical data engine that uses deep learning models for natural language processing (NLP) of clinical text and Clinical Concept Extraction (CCE) to extract clinical concepts from notes and structured tables. This enables clinical data preparation for large-scale research that is efficient, accurate, and easy to implement. The engine integrates four recursive deep learning components, each addressing a distinct stage of the data preparation pipeline:
- Clinical Text Recommending System (CTRS) – supports small-data EHR analytics based on clinical text
- Clinical Data Preparation Engine (CDPE) – curates multi-source, multi-type clinical data for both large-scale analysis and exploratory research
- Automated AI-driven Chatbot for Answering Clinical Questions (AICACQ) – answers clinical questions efficiently and accurately
- Clinical Concept Recommendation System (CCRS) – supports CCE-related recommendation tasks
The framework’s novelty lies in two areas: the deep lifecycle relationships linking these components (and their extensibility to new applications), and its ability to drive clinical-data preparation according to analytic demand. By supporting a wide range of tasks across the clinical research pipeline, the engine scales flexibly with both the complexity of analytical demands and the diversity of clinical data sources.
Ultimately, by enabling task-recommendation-driven, data-engine-supported AI application development, this framework empowers both front-end designers and back-end developers of EHR analytics systems — bridging the gap between clinical analytics and the infrastructure that supports it.
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