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API Reference

Integrate medical intelligence into your platform

https://api.conceptualhealth.ai/v1

Authentication

All API requests require an HCC token obtained through your DataVault account. Include your token as a Bearer token in the Authorization header.

Authorization: Bearer <your_hcc_token>

Endpoints

POST/v1/clinical

Clinical Intelligence

Clinical intelligence queries including drug interactions, treatment suggestions, and diagnosis reasoning across the 8-axis model.

Example Request

{
  "query": "Patient on metformin + lisinopril with declining NM axis. Suggest intervention.",
  "axes": ["PO", "NM", "ER"],
  "time_range": "6_months",
  "response_format": "structured"
}

Example Response

{
  "reasoning_chain": [
    "NM axis decline correlated with medication adherence drop at month 3",
    "Metformin GI side effects flagged in 23% of similar profiles",
    "Lisinopril interaction: no contraindication, but potassium monitoring recommended"
  ],
  "suggestions": [
    "Switch to extended-release metformin to reduce GI burden",
    "Add weekly NM axis check-in via NexusOrb",
    "Schedule nutritionist consult within 14 days"
  ],
  "confidence": 0.87,
  "contributing_data": {
    "encounters": 12847,
    "axis_correlations": {
      "NM_PO": 0.72,
      "NM_ER": 0.41
    }
  },
  "hcc_cost": 2.5
}
POST/v1/drug-outcomes

Drug Outcome Analysis

Analyze drug efficacy, adverse events, and adherence patterns segmented by 8-axis wellness profile.

Example Request

{
  "drug": "metformin",
  "outcome_type": "efficacy",
  "axes_filter": ["NM", "PO"],
  "population_size": "minimum_500"
}

Example Response

{
  "drug": "metformin",
  "outcome_type": "efficacy",
  "cohort_size": 3241,
  "efficacy_score": 0.78,
  "adverse_event_rate": 0.12,
  "axis_breakdown": {
    "NM": { "correlation": 0.81, "trend": "positive" },
    "PO": { "correlation": 0.64, "trend": "stable" }
  },
  "hcc_cost": 3.0
}
POST/v1/risk-model

Patient Risk Modeling

Predict hospitalization, readmission, and health decline risk using multi-axis temporal data.

Example Request

{
  "patient_profile": {
    "axes_snapshot": { "PO": 6.2, "NM": 4.1, "ER": 7.8 },
    "age_range": "55-64",
    "conditions": ["type_2_diabetes", "hypertension"]
  },
  "risk_type": "hospitalization",
  "horizon": "90_days"
}

Example Response

{
  "risk_type": "hospitalization",
  "probability": 0.23,
  "contributing_factors": [
    { "factor": "NM axis below threshold", "weight": 0.42 },
    { "factor": "Dual chronic condition", "weight": 0.31 }
  ],
  "recommended_interventions": 3,
  "hcc_cost": 5.0
}
POST/v1/device-outcomes

Medical Device Real-World Evidence

Access real-world evidence for medical devices including efficacy data, complication rates, and patient-reported outcomes.

Example Request

{
  "device_category": "continuous_glucose_monitor",
  "outcome_metrics": ["accuracy", "patient_satisfaction", "complication_rate"],
  "time_period": "12_months"
}

Example Response

{
  "device_category": "continuous_glucose_monitor",
  "studies_analyzed": 47,
  "outcomes": {
    "accuracy": 0.92,
    "patient_satisfaction": 0.85,
    "complication_rate": 0.03
  },
  "sample_size": 8920,
  "hcc_cost": 3.0
}
POST/v1/population

Population Health Analytics

Run cohort analysis and population-level health analytics across demographic and axis-based segments.

Example Request

{
  "cohort_definition": {
    "age_range": "40-65",
    "axes_threshold": { "PO": { "min": 3.0 } },
    "conditions": ["metabolic_syndrome"]
  },
  "analysis_type": "trend",
  "granularity": "quarterly"
}

Example Response

{
  "cohort_size": 14203,
  "analysis_type": "trend",
  "periods": [
    { "quarter": "Q1_2026", "avg_PO": 4.8, "avg_NM": 5.1 },
    { "quarter": "Q4_2025", "avg_PO": 4.5, "avg_NM": 4.9 }
  ],
  "statistical_significance": 0.003,
  "hcc_cost": 10.0
}
POST/v1/one-health

One Health Intelligence

Cross-species analytics combining human and veterinary health data for zoonotic detection and comparative medicine.

Example Request

{
  "query_type": "cross_species_correlation",
  "human_condition": "respiratory_infection",
  "animal_species": "canine",
  "geographic_scope": "southeast_us"
}

Example Response

{
  "correlation_found": true,
  "correlation_strength": 0.67,
  "temporal_lag": "14_days",
  "alert_level": "moderate",
  "zoonotic_risk": "low",
  "recommendation": "Monitor regional veterinary respiratory cases as leading indicator",
  "hcc_cost": 5.0
}
POST/v1/temporal

Temporal Decay Analysis

Analyze how health behaviors decay over time and predict future trajectories using the 28 temporal decay rules.

Example Request

{
  "patient_axes": {
    "PO": [7.2, 6.8, 6.1, 5.5],
    "NM": [8.0, 7.5, 7.0, 6.2]
  },
  "interval": "monthly",
  "predict_ahead": 6
}

Example Response

{
  "decay_model": "exponential_plateau",
  "predicted_trajectory": {
    "PO": [5.1, 4.8, 4.6, 4.5, 4.5, 4.5],
    "NM": [5.8, 5.4, 5.1, 4.9, 4.7, 4.6]
  },
  "intervention_urgency": "high",
  "decay_rules_triggered": ["rule_7_exercise_plateau", "rule_12_nutrition_drift"],
  "hcc_cost": 7.5
}
GET/v1/catalog

Data Catalog

Retrieve available data domains, axes definitions, query types, and supported parameters.

Example Request

GET https://api.conceptualhealth.ai/v1/catalog
Authorization: Bearer <your_token>

Example Response

{
  "axes": ["PO", "NM", "ER", "SC", "RS", "ES", "TA", "PV"],
  "domains": [
    "clinical", "drug-outcomes", "risk-model",
    "device-outcomes", "population", "one-health", "temporal"
  ],
  "query_types": ["structured", "natural_language", "sql_like"],
  "supported_time_ranges": ["30_days", "90_days", "6_months", "1_year", "all"],
  "version": "1.0.0",
  "hcc_cost": 0
}

Rate Limits

TierDaily LimitBurst Rate
Starter100 queries/day10/min
Professional1,000 queries/day50/min
Enterprise10,000 queries/day200/min
UnlimitedUnlimitedCustom

SDKs

Python

Available
pip install conceptualhealth

Swift

Available
SPM: conceptualhealth-swift

JavaScript

Coming Soon
npm install @conceptualhealth/sdk

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