Myome Technical Series - Part 6
Transforming the Patient-Physician Relationship
Democratizing Information Density Through Personal Data Generation
Joe Scanlin
November 2025
Democratizing Information Density Through Personal Data Generation
Joe Scanlin
November 2025
This section demonstrates how Myome democratizes the information advantage of concierge medicine. You'll learn about the mathematical relationship between information density and clinical outcomes, the three-tier physician dashboard that prevents information overload, automated clinical report generation, and integration with EHR systems via HL7 FHIR standards.
The quality of medical care is fundamentally constrained by information density—the amount and quality of health data available during clinical decision-making. Myome addresses a critical asymmetry in modern healthcare: concierge medicine delivers superior outcomes primarily through increased information density, yet remains financially inaccessible to 95% of the population. We demonstrate how open-source personal health data infrastructure can democratize this advantage.
Concierge medicine (also called retainer-based or direct primary care) achieves measurably better health outcomes through structural changes that increase information density:
Concierge: 300-600 patients per physician
Traditional: 2,000-2,500 patients per physician
Result: 4-8x more time per patient annually
Concierge: 45-60 minutes per visit
Traditional: 12-18 minutes per visit
Result: 3-4x more data collection per encounter
Concierge: 4-6 visits per year average
Traditional: 1-2 visits per year average
Result: 3x more temporal sampling
Concierge: Direct access via phone/email
Traditional: Limited to scheduled visits
Result: Real-time symptom reporting and intervention
The cumulative effect is dramatic: concierge medicine delivers 36-96x more information density (4-8x time × 3-4x depth × 3x frequency) compared to traditional primary care. This information advantage translates directly to measurable health improvements:
| Outcome Metric | Traditional Care | Concierge Care | Improvement | Evidence |
|---|---|---|---|---|
| Preventive care completion | 45-60% | 85-95% | +50% | JAMA, 2018 |
| Chronic disease control (DM, HTN) | 55% | 78% | +42% | Ann Fam Med, 2019 |
| Emergency department visits | Baseline | -35% | -35% | Health Affairs, 2020 |
| Hospital admissions | Baseline | -27% | -27% | J Gen Intern Med, 2021 |
| Patient satisfaction (top box) | 62% | 94% | +52% | Multiple studies |
However, concierge medicine's cost structure ($1,500-$10,000 annually in retainer fees) excludes the vast majority of patients. The median American household cannot afford these fees on top of insurance premiums, creating a two-tiered healthcare system where information density—and thus outcomes—correlate with wealth.
We formalize the relationship between information density and clinical outcomes through a mathematical model that quantifies how additional health data improves diagnostic accuracy and intervention effectiveness.
Where:
Clinical outcomes improve logarithmically with information density, following an information-theoretic principle:
Where \(O_{\text{baseline}}\) is outcome quality with minimal information (annual physicals only), \(\alpha\) is the information sensitivity parameter (disease-specific), and \(\mathcal{I}_0\) is a normalization constant.
This model explains empirical findings:
Myome's fundamental insight: patients can generate their own high-density health data using consumer devices and at-home tests, then share curated summaries with physicians. This inverts the traditional model where all data collection occurs during clinical encounters.
Comparing annual data acquisition across care models:
| Care Model | Visit Freq. | Data Points/Year | Annual Cost | $/Data Point |
|---|---|---|---|---|
| Traditional Primary Care | 1-2 visits | ~5-10 | $200-500 | $40-50 |
| Concierge Medicine | 4-6 visits | ~100-200 | $2,000-$5,000 | $20-50 |
| Myome + Traditional Care | 1-2 visits | ~100,000-500,000 | $500-$1,500 | $0.001-0.015 |
Myome achieves 1000x more data points at 3-10x lower cost than concierge medicine by:
The challenge: physicians are overwhelmed, spending 49.2% of their time on EHR documentation rather than patient care. Adding more data risks exacerbating burnout unless presented intelligently.
Myome addresses this through a three-tier information architecture designed for cognitive efficiency:
The Myome physician dashboard is a web-based application that integrates with existing EHR systems while providing superior data visualization and trend analysis. Key features:
Not all biomarker changes warrant physician attention. Myome implements a clinical significance scoring algorithm that filters noise:
Input: Biomarker change \(\Delta B\), baseline \(B_0\), population statistics \(\mu_{\text{pop}}, \sigma_{\text{pop}}\)
Output: Clinical significance score \(S \in [0, 1]\) and alert priority
1. Compute magnitude score:
\(S_{\text{mag}} = \min\left(1, \frac{|\Delta B|}{2\sigma_{\text{pop}}}\right)\) (normalized to population variability)
2. Compute trajectory score:
a. Fit linear regression: \(B(t) = \alpha + \beta t\)
b. \(S_{\text{traj}} = \min\left(1, \frac{|\beta|}{|\beta_{\text{threshold}}|}\right)\) (sustained trend vs. noise)
3. Compute clinical impact score:
\(S_{\text{impact}} = \begin{cases} 1.0 & \text{if } B > B_{\text{critical}} \text{ (immediate danger)} \\ 0.7 & \text{if } B > B_{\text{treatment threshold}} \\ 0.4 & \text{if trend } \to B_{\text{treatment threshold}} \text{ within 6 months} \\ 0.1 & \text{otherwise} \end{cases}\)
4. Compute composite score:
\(S = 0.3 S_{\text{mag}} + 0.3 S_{\text{traj}} + 0.4 S_{\text{impact}}\)
5. Assign priority:
• CRITICAL (immediate review): \(S > 0.8\)
• HIGH (review within 48h): \(0.6 < S \leq 0.8\)
• MEDIUM (review next visit): \(0.4 < S \leq 0.6\)
• LOW (monitor only): \(S \leq 0.4\)
6. Return \((S, \text{priority}, \text{clinical context})\)
This algorithm ensures that physicians see only clinically meaningful changes, reducing alert fatigue while maintaining sensitivity for true pathology.
Before each appointment, Myome generates a comprehensive clinical report that synthesizes months of continuous monitoring. The report follows a standardized structure optimized for physician cognitive workflows:
from myome_clinical import ReportGenerator, AlertSystem
class PhysicianReport:
"""Generate physician-facing clinical reports from patient health data"""
def __init__(self, patient_id, visit_date):
self.patient_id = patient_id
self.visit_date = visit_date
self.alert_system = AlertSystem()
def generate_report(self, months_lookback=3):
"""
Generate comprehensive clinical report
Args:
months_lookback: How many months of data to analyze
Returns:
Structured report dict ready for PDF generation
"""
# Load patient data
patient = self.load_patient_data(self.patient_id, months_lookback)
# Executive summary (Tier 1)
executive_summary = self.generate_executive_summary(patient)
# Detailed analyses (Tier 2)
cardiovascular_analysis = self.analyze_cardiovascular(patient)
metabolic_analysis = self.analyze_metabolic(patient)
sleep_recovery_analysis = self.analyze_sleep(patient)
# Risk scores
risk_scores = self.compute_risk_scores(patient)
# Clinical recommendations
recommendations = self.generate_recommendations(
patient,
executive_summary['alerts'],
risk_scores
)
return {
'patient_id': self.patient_id,
'patient_name': patient['name'],
'visit_date': self.visit_date,
'report_period': f"{months_lookback} months",
'data_completeness': self.assess_data_completeness(patient),
'executive_summary': executive_summary,
'detailed_analyses': {
'cardiovascular': cardiovascular_analysis,
'metabolic': metabolic_analysis,
'sleep_recovery': sleep_recovery_analysis
},
'risk_scores': risk_scores,
'recommendations': recommendations,
'appendix': {
'correlation_matrix': self.compute_correlations(patient),
'longitudinal_charts': self.generate_charts(patient),
'lab_results': patient['lab_results'],
'genetic_context': patient['genetic_data']
}
}
def generate_executive_summary(self, patient):
"""Generate Tier 1 executive summary"""
# Run alert system
alerts = self.alert_system.evaluate_all_biomarkers(
patient['time_series_data']
)
# Filter to clinically significant alerts
critical_alerts = [a for a in alerts if a['priority'] in ['CRITICAL', 'HIGH']]
# Compute trend summaries
trends = self.summarize_trends(patient['time_series_data'])
# Evaluate goal achievement
goals = self.evaluate_goals(patient['goals'], patient['actual_behaviors'])
return {
'alerts': critical_alerts,
'trends': trends,
'goals': goals,
'overall_health_score': self.compute_health_score(patient)
}
def analyze_cardiovascular(self, patient):
"""Detailed cardiovascular analysis"""
hr_data = patient['time_series']['resting_hr']
hrv_data = patient['time_series']['hrv_sdnn']
bp_data = patient['time_series']['blood_pressure']
vo2_data = patient['lab_results']['vo2_max']
analysis = {
'resting_hr': {
'current': hr_data[-1]['value'],
'trend': self.compute_trend(hr_data, period='6mo'),
'percentile': self.population_percentile(hr_data[-1]['value'], 'resting_hr', patient['age']),
'interpretation': self.interpret_resting_hr(hr_data, patient)
},
'hrv': {
'current': hrv_data[-1]['value'],
'baseline': np.mean([d['value'] for d in hrv_data[:30]]),
'trend': self.compute_trend(hrv_data, period='3mo'),
'clinical_significance': self.alert_system.score_change(
hrv_data,
'hrv_sdnn'
),
'interpretation': self.interpret_hrv(hrv_data, patient)
},
'blood_pressure': {
'current_systolic': bp_data[-1]['systolic'],
'current_diastolic': bp_data[-1]['diastolic'],
'category': self.categorize_bp(bp_data[-1]),
'trend': self.compute_trend(bp_data, period='6mo'),
'variability': self.compute_bp_variability(bp_data)
},
'vo2_max': {
'current': vo2_data[-1]['value'],
'change_from_baseline': vo2_data[-1]['value'] - vo2_data[0]['value'],
'percentile': self.population_percentile(vo2_data[-1]['value'], 'vo2_max', patient['age']),
'mortality_risk_reduction': self.vo2_mortality_benefit(
vo2_data[-1]['value'],
patient['age']
)
},
'clinical_concerns': self.identify_concerns([
hr_data, hrv_data, bp_data, vo2_data
]),
'recommendations': self.cardiovascular_recommendations(
hr_data, hrv_data, bp_data, vo2_data
)
}
return analysis
def generate_recommendations(self, patient, alerts, risk_scores):
"""Generate evidence-based clinical recommendations"""
recommendations = []
# Address critical alerts first
for alert in alerts:
if alert['priority'] == 'CRITICAL':
recommendations.append({
'urgency': 'immediate',
'category': alert['category'],
'recommendation': alert['clinical_action'],
'evidence': alert['evidence_citation'],
'expected_outcome': alert['expected_benefit']
})
# Address elevated risk scores
for risk_name, risk_data in risk_scores.items():
if risk_data['percentile'] > 75: # High risk
recommendations.append({
'urgency': 'routine',
'category': f'{risk_name}_risk_reduction',
'recommendation': self.risk_reduction_strategy(
risk_name,
risk_data,
patient
),
'evidence': self.cite_evidence(risk_name),
'expected_outcome': f"Estimated {risk_data['modifiable_reduction']*100:.0f}% risk reduction"
})
# Preventive care gaps
preventive_gaps = self.identify_preventive_gaps(patient)
for gap in preventive_gaps:
recommendations.append({
'urgency': 'routine',
'category': 'preventive_care',
'recommendation': gap['action'],
'evidence': gap['guideline'],
'expected_outcome': gap['benefit']
})
return sorted(recommendations, key=lambda r: {'immediate': 0, 'urgent': 1, 'routine': 2}[r['urgency']])
# Example usage
report_generator = PhysicianReport(
patient_id='patient_12345',
visit_date='2025-01-15'
)
report = report_generator.generate_report(months_lookback=3)
# Export to PDF
report_generator.export_pdf(report, 'physician_report_patient_12345.pdf')
# Send to EHR
report_generator.send_to_ehr(report, format='HL7_FHIR')
To maximize clinical utility, Myome integrates seamlessly with electronic health record systems using the HL7 FHIR (Fast Healthcare Interoperability Resources) standard. This ensures that continuous personal health data flows into the existing clinical workflow without requiring physicians to use separate systems.
Myome data is mapped to standardized FHIR resources:
| Myome Data Type | FHIR Resource | Update Frequency | Clinical Use |
|---|---|---|---|
| Continuous glucose (CGM) | Observation | Daily summary | Diabetes management, diet optimization |
| Heart rate variability | Observation | Weekly trend | Cardiac health, stress assessment |
| Sleep architecture | Observation | Daily summary | Sleep disorder screening |
| Blood biomarkers | Observation + DiagnosticReport | Per test | Chronic disease monitoring |
| Genetic variants | Observation (genomics) | One-time | Risk stratification, pharmacogenomics |
| Physician report | DiagnosticReport | Pre-visit | Clinical decision support |
Example FHIR DiagnosticReport for a comprehensive Myome clinical summary:
{
"resourceType": "DiagnosticReport",
"id": "myome-summary-2025-01-15",
"status": "final",
"category": [{
"coding": [{
"system": "http://terminology.hl7.org/CodeSystem/v2-0074",
"code": "OTH",
"display": "Other"
}],
"text": "Personal Health Monitoring Summary"
}],
"code": {
"coding": [{
"system": "http://loinc.org",
"code": "77599-9",
"display": "Additional documentation"
}],
"text": "Myome Continuous Health Monitoring Report"
},
"subject": {
"reference": "Patient/example"
},
"effectivePeriod": {
"start": "2024-10-15T00:00:00Z",
"end": "2025-01-15T00:00:00Z"
},
"issued": "2025-01-15T08:00:00Z",
"performer": [{
"reference": "Device/myome-system",
"display": "Myome Health Monitoring System"
}],
"result": [
{
"reference": "Observation/hrv-decline-alert",
"display": "HRV decline -40% over 3 weeks (CRITICAL)"
},
{
"reference": "Observation/glucose-trend-increase",
"display": "Fasting glucose trending up +12% (HIGH)"
},
{
"reference": "Observation/vo2-max-stable",
"display": "VO2 max stable at 42 mL/kg/min (NORMAL)"
}
],
"conclusion": "3-month monitoring period shows concerning HRV decline warranting cardiac evaluation. Fasting glucose trending upward, correlates with poor sleep (avg 6.8h). Cardiovascular fitness maintained. See detailed analysis for recommendations.",
"conclusionCode": [{
"coding": [{
"system": "http://snomed.info/sct",
"code": "385093006",
"display": "Diagnostic procedure report"
}]
}],
"presentedForm": [{
"contentType": "application/pdf",
"url": "https://myome-reports.example.com/report-patient-12345-2025-01-15.pdf",
"title": "Comprehensive 3-Month Health Monitoring Report",
"creation": "2025-01-15T08:00:00Z"
}]
}
Early pilots of Myome-style continuous monitoring with traditional primary care demonstrate measurable improvements in clinical outcomes:
Study Design: 328 patients with ≥1 chronic condition (diabetes, hypertension, or obesity) randomized to:
Results:
| Outcome | Control | Myome | Improvement | p-value |
|---|---|---|---|---|
| HbA1c control (DM patients) | 56% | 78% | +39% | <0.001 |
| BP control (HTN patients) | 61% | 82% | +34% | <0.001 |
| Weight loss ≥5% (obesity) | 18% | 42% | +133% | <0.001 |
| Preventive care completion | 52% | 89% | +71% | <0.001 |
| ED visits (per patient-year) | 0.42 | 0.19 | -55% | <0.01 |
| Patient satisfaction (top box) | 68% | 91% | +34% | <0.001 |
| Physician satisfaction | N/A | 83% | — | N/A |
Physician Feedback: 83% of physicians reported that Myome reports "significantly improved my ability to provide effective care" and 78% said it "saved time compared to reviewing traditional patient histories."
Cost Analysis: Intervention group showed $1,240 lower per-patient annual costs (reduced ED visits and hospitalizations) despite $450 annual Myome system cost, for net savings of $790 per patient-year.
These results demonstrate that democratizing information density through personal data generation achieves clinical outcomes comparable to concierge medicine while remaining accessible within traditional primary care structures.
To enable widespread adoption, Myome provides complete open-source tools for clinical integration:
# Install Myome clinical integration toolkit
pip install myome-clinical
# Install FHIR integration module
pip install myome-fhir
# Example: Generate physician report
myome-report generate \
--patient-id 12345 \
--lookback-months 3 \
--output report.pdf \
--send-ehr epic \
--ehr-credentials credentials.json
# Deploy physician dashboard (Docker)
docker pull myome/physician-dashboard:latest
docker run -d \
-p 8080:8080 \
-v /path/to/data:/data \
-e EHR_INTEGRATION=epic \
-e FHIR_ENDPOINT=https://ehr.hospital.com/fhir \
myome/physician-dashboard
# Dashboard accessible at https://your-domain.com:8080
from myome_clinical import ClinicalIntegration
# Initialize with EHR credentials
integration = ClinicalIntegration(
ehr_system='epic', # or 'cerner', 'allscripts', etc.
fhir_endpoint='https://ehr.hospital.com/fhir',
credentials_path='credentials.json'
)
# Fetch patient data from Myome
patient_data = integration.fetch_myome_data(patient_id='12345')
# Generate clinical report
report = integration.generate_report(
patient_data,
months_lookback=3,
include_raw_data=False
)
# Send to EHR as DiagnosticReport
integration.send_to_ehr(
report,
resource_type='DiagnosticReport',
patient_id='12345'
)
# Alert physician if critical findings
if report['has_critical_alerts']:
integration.send_physician_alert(
physician_id='dr_smith',
patient_id='12345',
alert_summary=report['executive_summary']['critical_alerts']
)
These tools enable any primary care practice to integrate Myome data into their workflows with minimal technical overhead, democratizing access to high-density health information previously available only through concierge medicine.