Improving personalized healthcare with automated longitudinal EHR analysis

Traditional Electronic Health Record (EHR) data analysis at King's College Hospital relies on extensive manual effort, from data extraction to reporting, limiting efficiency and scalability. This study presents an automated framework for longitudinal EHR data analysis to enhance personalized healthcare insights.

Central to the framework is the integration of Markov Chains with Survival Analysis (SA) and Latent Growth Modeling, enhancing the modeling of patient trajectories and capturing variances in growth patterns over time. Expectation-Maximization with Gaussian Mixture Models, extended with Latent Class Analysis, identifies clinically meaningful patient subgroups for tailored interventions. The framework addresses data uncertainty, enabling precise event forecasts and trajectory predictions. The system employs Apache NiFi for data ingestion, Elasticsearch for indexing, and Splunk and Kibana for real-time visualization and reporting. Natural Language Processing (NLP) techniques extract structured insights from unstructured clinical notes, enriching datasets with context. The automation significantly reduces manual processing while ensuring data integrity and enhancing predictive capabilities.

Implementation demonstrated a 15% increase in detecting major depression cases, an 18% improvement in predicting patient decisions, a 25% reduction in growth trajectory prediction variance, and a 10% increase in event prediction accuracy. The framework enhances data-driven decision-making, supporting personalized healthcare interventions through real-time insights.

This automated framework integrates predictive modeling, NLP techniques, and real-time data processing, improving the efficiency and accuracy of longitudinal EHR analysis. Providing robust, actionable insights enables personalized healthcare delivery, enhances clinical decision-making, and optimizes patient outcomes.