Myome Technical Series - Part 1
Introduction
The Healthcare Information Crisis and the 'Ome' Framework
Joe Scanlin
November 2025
The Healthcare Information Crisis and the 'Ome' Framework
Joe Scanlin
November 2025
This section introduces the fundamental healthcare information problem that Myome aims to solve. You'll learn why the U.S. healthcare system wastes $690 billion annually despite spending 18% of GDP, how episodic care fails to capture the continuous nature of health, and why we need a paradigm shift toward preventive monitoring. The section also introduces the seven 'ome' framework—a biological taxonomy that organizes personal health data across complementary domains.
The American healthcare system is fundamentally broken, not through irreversible structural failures, but through remediable inefficiencies rooted in information asymmetry and administrative dysfunction. The United States expends 18% of its GDP on healthcare—approximately $4.3 trillion annually—yet achieves health outcomes inferior to nations spending half as much per capita. This paradox stems from systematic waste across five critical domains:
At the heart of this crisis lies an information problem. Physicians spend an estimated 49.2% of their time on electronic health records and desk work, leaving mere minutes for actual patient care. Meanwhile, patients report feeling unheard, their concerns dismissed, their health trajectories poorly understood. The fundamental issue is that current healthcare systems operate on sparse, episodic data snapshots—a blood panel once a year, a physical examination during acute illness, retrospective patient recall of symptoms.
This sparse sampling approach fails to capture the continuous, dynamic nature of human health. Chronic diseases develop over years through subtle physiological shifts invisible to quarterly checkups. Metabolic dysfunction, cardiovascular decline, cognitive deterioration—these pathologies announce themselves through measurable biomarker changes long before clinical diagnosis, yet our healthcare system remains blind to these early signals.
The solution to healthcare's information crisis is not merely incremental improvement in existing electronic health record (EHR) systems. Instead, we require a fundamental paradigm shift: from episodic reactive care to continuous preventive monitoring. This transition demands that individuals become active participants in their health data generation, creating comprehensive longitudinal records that vastly exceed what any healthcare system could collect through intermittent clinical encounters.
Recent advances in consumer health technology have made this vision achievable. Wearable biosensors can continuously monitor cardiovascular function, sleep architecture, physical activity, and metabolic markers. At-home testing kits provide access to genomic sequencing, microbiome analysis, epigenetic aging biomarkers, and comprehensive blood panels. Environmental sensors track exposure to pollutants, allergens, and pathogens. Together, these technologies enable individuals to generate rich, multi-dimensional health datasets that dwarf the information available in traditional medical records.
The clinical evidence for this approach is compelling. Studies demonstrate that engaged, activated patients achieve superior outcomes across virtually all chronic conditions—hypertension, diabetes, obesity, multiple sclerosis, hyperlipidemia, and mental health disorders all show marked improvement when patients actively monitor and manage their health data. The challenge lies not in demonstrating the value of comprehensive health monitoring, but in creating systems that make such monitoring practical, scientifically rigorous, and minimally burdensome.
To organize the vast landscape of personal health data, we adopt the biological "ome" framework—a taxonomy that partitions human health into seven complementary domains, each capturing distinct but interrelated aspects of physiological function:
Environmental exposures including air quality (PM2.5, PM10, VOCs, CO₂), radiation levels, pollen counts, temperature, humidity, and pathogen risk.
Chemical modifications to DNA that regulate gene expression, measured through DNA methylation patterns that correlate with biological aging and disease risk.
Microbial communities inhabiting the gut, skin, and other body sites, assessed through metagenomic sequencing revealing bacterial, viral, and fungal populations.
Small molecules (metabolites) and proteins circulating in blood, reflecting real-time metabolic state and cellular function.
Complete DNA sequence including variants associated with disease risk, drug metabolism, and inherited traits.
Structural anatomy assessed through medical imaging (MRI, CT, DEXA scans) revealing tissue composition and organ morphology.
Continuous physiological measurements including heart rate variability, blood pressure, glucose dynamics, oxygen saturation, body composition, sleep stages, and physical performance metrics.
These seven domains are not independent—they interact in complex, dynamic ways that determine health trajectories. For example, gut microbiome composition (microbiome) influences glucose metabolism (physiome), which can trigger epigenetic changes (epigenome) that alter disease risk encoded in the genome. Environmental exposures (exposome) can shift microbiome populations, leading to systemic inflammation detectable in blood proteins (proteome). Understanding health requires integrating data across all these domains, identifying correlations, and predicting future states.
Myome provides the technical infrastructure to make this integration practical, scientifically valid, and personally actionable.