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

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.

Keywords: clinical decision support; health information technology; health monitoring; individualized medicine; scientific wellness; wearables; wellcare.

Figure 1

Flowchart of individualome—a health care and wellcare data model for incorporating biomedical, health care and wellness monitoring information with EMRs. Various health data streams can be integrated into a consolidated data model we call individualome. Standards for health care data from Table 2 are indicated at points of implementation. The individualome data can be used for various applications including diagnostics, prognostics and personalized clinical trials. The findings from these applications can be used to generate actionable recommendations, sharing with consumers how to best improve aspects of their health and mitigate personalized disease risks. Current diagnostics and prognostics are based on standard clinical data; by adding multi-omic data and continuous data from environment and personal health repositories, we will be able to build precision models of human health and disease and identify indolent/subclinical stages of disease.

Figure 2

From health monitoring to predictive modeling of diseases: edges are different health monitoring data streams; nodes indicates disease areas where the health monitoring data can be used for prognostic, diagnostic, clinical, therapeutic or wellness interventions. 1: psychiatric and neurological disease, cerebrovascular disease, stress responses/autonomic reactivity, chronic pain; 2: cardiac arrest, myocardial infarction, coronary heart disease, anxiety, aerobic fitness levels; 3: chronic back pain, movement disorders (Parkinsonism), tremors, rehabilitation recovery, agility testing, dystonia, myalgia, chronic fatigue syndrome; 4: hypertension, orthostatic hypotension, chronic kidney disease, peripheral arterial disease, vasculitis (e.g. Lupus, Raynaud’s disease); 5: movement disorders, rehabilitation, epilepsy, myalgia; 6: chronic and acute lung diseases, obstructive sleep apnea, sleep disorders, narcolepsy, synucleopathies; 7: insulin level (Type 1 or Type 2 diabetes); 8: diabetes, cardiovascular disease, inflammatory bowel disease, irritable bowel syndrome, gluten sensitivity, eating disorders; 9: chronic and acute lung diseases; 10: hyper/hypothyroidism, female endocrinology, obstructive sleep apnea, narcolepsy, neurologic, psychiatric, chronic fatigue syndrome and developmental disease.

Figure 3

Visualizing biomedical, health care and wellness data streams. (A) A screenshot from EHDViz: a clinical data visualization dashboard combining provider generated clinical data with patient generated data. (B) Analytics dashboard implemented using Elastic and Kibana to analyze a large cohort of patients (n = 8517) with 2.91 million data points of laboratory measurements.