Data Feed
The Data Feed is OmegaX’s central mechanism for collecting and standardizing every piece of health-related information. This includes wearable metrics, medical records, lab results, symptom journals, and more. By consolidating all of these data streams into a single, continuous feed, we can support real-time AI insights and proactive user engagement.
What is the Data Feed?
Think of the Data Feed as a living timeline of each user’s health. Whenever a new piece of information arrives—be it a blood pressure reading, a PDF lab report, or a symptom entry—the Data Feed integrates that update into a coherent, chronological record.
- Holistic Understanding: Fragmented data makes it impossible to see health trends in context (e.g., stress levels vs. heart rate changes).
- Real-Time AI: AI can only act proactively if it’s aware of all relevant signals the moment they come in.
- Medical Continuity: Providers who access a user’s OmegaX data get a single source of truth, rather than scattered bits from different apps or wearables.
Data Sources
OmegaX integrates multiple data streams into the feed:
OmegaX connects to Apple HealthKit, Google Fit, and smartwatches to pull in real-time health data.
- Tracks steps, active minutes, and workouts to monitor movement levels.
- Heart rate monitoring includes resting heart rate, variability, and stress indicators.
- Sleep tracking helps analyze sleep patterns and detect irregularities.
Data updates continuously or in short intervals, depending on the device. This helps detect trends in fitness, recovery, and overall health without users having to log anything manually.
Medical records are pulled from electronic health systems (EHRs), lab results, and imaging reports when available.
- Lab tests (bloodwork, cholesterol, glucose levels)
- Medical imaging (X-rays, MRIs, CT scans)
- Doctor updates for chronic conditions like diabetes or hypertension
Most hospitals use FHIR or HL7 formats, but PDFs are also supported if users upload their reports. Unlike fitness data, this information updates only when a healthcare provider submits new records—so it’s less frequent but highly valuable.
Not everything comes from devices. Users can manually log symptoms, mood, medications, or anything else they want to track.
- Symptom journals help track patterns (e.g., headaches, fatigue).
- Mental health check-ins allow mood tracking over time.
- Medication adherence logs help ensure users stay on track with prescriptions.
Some inputs can be added via voice prompts (e.g., “Log chest pain level 3 at 2 PM”), making tracking easier without typing. This data is stored alongside wearable and medical data for a complete picture.
Health isn’t just about the body—it’s also about surroundings. OmegaX can factor in location-based data to give smarter recommendations.
- GPS tracking detects activity changes (e.g., if someone’s altitude suddenly increases, it might suggest hydration).
- Weather and pollution data help connect symptoms like allergies or breathing issues to external factors.
- Circadian rhythm tracking aligns sleep advice with local time zones.
This kind of contextual data makes AI-powered recommendations more relevant to real-world conditions instead of just relying on raw health numbers.
Data Standardization
| Process | Purpose | Example |
|---|---|---|
| Unit Normalization | Standardizes vitals to avoid inconsistencies across devices. | Converts BP from kPa to mmHg, glucose from mmol/L to mg/dL. |
| Metadata Augmentation | Adds timestamps, time zones, and device IDs for better tracking. | Tags a heart rate reading as “post-exercise”, ensuring AI interprets context correctly. |
| Quality Checks | Detects outliers and eliminates duplicates. | Flags a 500 bpm heart rate as invalid or removes overlapping entries. |
Example Data Flow
Step 1: Data Capture
A smartwatch logs a 90 bpm heart rate at 09:00 and syncs it to OmegaX.
Step 2: Unit Normalization
OmegaX checks the measurement units (beats per minute is standard, so no conversion needed).
Step 3: Metadata Augmentation
- The reading is tagged as “post-breakfast walk”.
- A timestamp and time zone are added.
- The device ID is linked for traceability.
Step 4: Quality Checks
- OmegaX flags implausible readings (e.g., 500 bpm heart rate).
- If multiple devices report the same data, duplicates are merged or discarded.
Step 5: Data Integration
The cleaned and standardized entry is merged into the Data Feed for AI analysis.
Timeline & Visualization
The Data Feed is organized as a chronological sequence of entries:
Heart rate: 90 bpm (9:00 AM) → Blood Pressure: 130/80 (9:30 AM) → Symptom: Mild headache (10:00 AM)
Each event is accompanied by metadata (source, category) and can be filtered or grouped in the frontend. The AI Doctor regularly scans these entries to detect unusual patterns or significant changes.
Real-Time Updates
Once a new data point is added:
Step 1: Immediate Store
A new data point is captured and added to the Data Feed.
Step 2: AI Processing
The Omega AI Doctor evaluates the update in real-time and determines if any action is needed.
Step 3: User Notification
If flagged as important, the system triggers a push notification, voice call, or chat prompt to inform the user.
Step 4: Continuous Learning
The AI refines future responses based on data patterns, improving accuracy.
This continuous cycle ensures that no data—however small—goes unnoticed. By layering all input sources into one feed, OmegaX creates a dynamic, ever-evolving health portrait that positions users (and their providers) to anticipate and prevent problems, not just react to them.
Future Expansions
- Integration with Genetic Data: Enables long-term risk analysis (e.g., predisposition to cardiovascular conditions).
- Pharmacy & Prescription Tracking: Logs prescription refills directly into the feed for medication compliance.
- ML-Driven Cleansing: Automated anomaly detection for spurious readings, especially from consumer devices.
In essence, the Data Feed is the lifeblood of OmegaX—pulling together every relevant data point into a unified timeline that both users and the AI Doctor can act on, shifting healthcare toward a continuous, preventive model of well-being.