Translational bioinformatics in the era of real-time biomedical, health care and wellness data streams

TLDR

Monitoring and modeling biomedical, health care, and wellness data from individuals and at the population level holds great promise for understanding the shift from healthy physiology to disease, yet the accessibility and clinical utility of such data remain limited, and translational bioinformatics methods and tools are central to realizing real‑time analytics in clinical settings. The article aims to design and discuss methods for streaming data capture, real‑time aggregation, machine learning, predictive analytics, and visualization that integrate patient‑generated wellness data with EMRs, enabling population‑scale data integration to stratify patients, understand asymptomatic cases, and support individualized diagnostics, prognosis, and interventions. The authors propose streaming data capture, real‑time aggregation, machine learning, predictive analytics, and visualization techniques that integrate patient‑generated wellness data with EMRs, personal health records, patient portals, and clinical repositories to support individualized diagnostics, prognosis, and interventions. These advances are poised to significantly influence clinical decision‑making and the implementation of data.

Abstract

Monitoring and modeling biomedical, health care and wellness data from individuals and converging data on a population scale have tremendous potential to improve understanding of the transition to the healthy state of human physiology to disease setting. Wellness monitoring devices and companion software applications capable of generating alerts and sharing data with health care providers or social networks are now available. The accessibility and clinical utility of such data for disease or wellness research are currently limited. Designing methods for streaming data capture, real-time data aggregation, machine learning, predictive analytics and visualization solutions to integrate wellness or health monitoring data elements with the electronic medical records (EMRs) maintained by health care providers permits better utilization. Integration of population-scale biomedical, health care and wellness data would help to stratify patients for active health management and to understand clinically asymptomatic patients and underlying illness trajectories. In this article, we discuss various health-monitoring devices, their ability to capture the unique state of health represented in a patient and their application in individualized diagnostics, prognosis, clinical or wellness intervention. We also discuss examples of translational bioinformatics approaches to integrating patient-generated data with existing EMRs, personal health records, patient portals and clinical data repositories. Briefly, translational bioinformatics methods, tools and resources are at the center of these advances in implementing real-time biomedical and health care analytics in the clinical setting. Furthermore, these advances are poised to play a significant role in clinical decision-making and implementation of data-driven medicine and wellness care.