JMIR Publications

ABSTRACT

Up to 95% of novel interventions demonstrating significant effects at the bench fail to translate to the bedside. In recent years, windfalls of “big data” have afforded investigators more substrate for research than ever before. However, issues with translation have persisted: countless biomarkers for diagnostic and therapeutic targeting have been proposed, yet few of these generalize effectively. We assert that inadequate heterogeneity in datasets used for discovery and validation causes their non-representativeness of the diversity observed in real-world patient populations. This non-representativeness is contrasted with advantages rendered by solicitation and utilization of data heterogeneity for multi-systemic disease modeling. Accordingly, we propose the potential benefits of models premised on heterogeneity to promote the Institute for Healthcare Improvement’s Triple Aim. In an era of personalized medicine, these models can confer higher quality clinical care for individuals, increased access to effective care across all populations, and lower costs for the healthcare system.