PRODUCT & CLINICAL USE CASES
Clinical Intelligence That
Solves Real Problems
See how Ardia's AI-driven platform transforms chronic care management from reactive alerting to proactive problem-solving across 20+ conditions.
Our Product: Clinical Intelligence Engine
Beyond alerts. Beyond chatbots. We deliver reasoned solutions with clinical context.
Multi-Signal Correlation
We don't look at one data point. We correlate voice, environmental, device, EHR, and patient-reported data to understand why something is happening.
Cause Determination
Our AI identifies the root cause: allergic trigger, medication non-adherence, environmental factor, or pattern match to historical episodes.
Solution Generation
We provide actionable recommendations with clinical reasoning: "Start nasal steroid today because..." not just "SpO2 is 92%".
Core Principle
❌ Bad (Typical AI):
"Your SpO2 is 92%"
No context. No action. Just noise.
✅ Good (Ardia):
"Start nasal steroid today — pollen is 3x your trigger threshold, pattern matches your March episode."
Context. Action. Reasoning. Solution.
Clinical Use Cases
Real-world examples of how Ardia transforms patient care across chronic conditions.
Asthma & COPD Management
Proactive respiratory care with environmental trigger detection
How It Works:
- •Multi-signal correlation: Voice analysis + Peak flow + Pollen data + Medication adherence
- •Pattern recognition: Identifies seasonal triggers and historical episode patterns
- •Proactive interventions: 'Start nasal steroid today - pollen 3x threshold' instead of 'SpO2 is 92%'
- •Reduces exacerbations by 40-60% through early intervention
Example Scenario:
Scenario: Patient with asthma, high pollen season
Trigger: Voice analysis detects nasal congestion + Pollen count 450 (threshold: 150)
Action: System recommends: Start nasal corticosteroid spray today, limit outdoor exposure
Reasoning: Pattern matches March 2024 episode when early intervention prevented exacerbation
Outcome: Patient avoids ER visit, maintains control
Congestive Heart Failure (CHF)
Early detection of fluid retention and decompensation
How It Works:
- •Weight monitoring: Detects +4lbs/3days pattern indicating fluid retention
- •Multi-signal correlation: Weight + SpO2 + Edema reports + Medication adherence
- •Cause determination: Identifies likely cause (medication non-adherence, dietary, infection)
- •Actionable solutions: 'Increase Lasix 40-80mg, consider admit' with clinical reasoning
Example Scenario:
Scenario: CHF patient showing signs of decompensation
Trigger: Weight +6lbs/3days, SpO2 trending down, edema reported
Action: System recommends: Increase Lasix 40-80mg, consider admission
Reasoning: Pattern matches November 2023 decompensation. Early intervention prevents hospitalization.
Outcome: Provider takes action within 24hrs, prevents ER visit
Type 2 Diabetes
Glucose pattern analysis and medication optimization
How It Works:
- •CGM integration: Continuous glucose monitoring data analysis
- •Pattern detection: Dawn phenomenon, post-prandial spikes, medication timing issues
- •Cause determination: Hepatic glucose production, medication non-adherence, dietary triggers
- •Personalized recommendations: Bedtime insulin adjustment, meal timing optimization
Example Scenario:
Scenario: Diabetes patient with dawn phenomenon
Trigger: Dawn phenomenon detected + A1C rising trend
Action: System recommends: Adjust bedtime insulin, monitor glucose patterns
Reasoning: Hepatic glucose production pattern identified. Medication timing optimization needed.
Outcome: Improved glucose control, reduced A1C
Atrial Fibrillation
Rhythm pattern analysis and trigger identification
How It Works:
- •ECG analysis: Smartwatch ECG data integration
- •Trigger correlation: Alcohol, stress, sleep patterns, medication timing
- •Pattern recognition: Holiday heart syndrome, stress-induced episodes
- •Lifestyle interventions: 'Limit alcohol, improve sleep hygiene' with reasoning
Example Scenario:
Scenario: AFib patient with irregular rhythm episodes
Trigger: Irregular rhythm detected + Alcohol consumption pattern
Action: System recommends: Lifestyle modification, reduce alcohol intake
Reasoning: Pattern matches holiday heart syndrome. Alcohol is primary trigger.
Outcome: Reduced AFib burden, improved quality of life
High-Risk Pregnancy
Specialty-trained OB/GYN reasoning engine
How It Works:
- •Vital monitoring: Blood pressure, weight, fetal movement patterns
- •Risk stratification: Pre-eclampsia detection, gestational diabetes management
- •Specialty protocols: OB/GYN-specific clinical guidelines and emergency protocols
- •Proactive care: Early intervention for pregnancy complications
Example Scenario:
Scenario: High-risk pregnancy with pre-eclampsia risk
Trigger: BP trending up + Proteinuria pattern + Weight gain pattern
Action: System recommends: Increase monitoring frequency, consider medication adjustment
Reasoning: Pre-eclampsia risk factors detected. Early intervention critical for maternal and fetal health.
Outcome: Managed at home, prevented complications
Ready to Transform Your Chronic Care Management?
See how Ardia's Clinical Intelligence Engine can reduce alert fatigue and improve patient outcomes in your practice.