🔒Data Flow and Security

Data Sources and Stakeholders

• Patient: Provide initial data through questionnaires, self-reported metrics, and wearable devices.

• Healthcare Providers: Share data from EHRs, diagnostic reports, and treatment plans via ABDM integration.

• Diagnostic Labs: Upload biomarker testing and advanced diagnostic results securely.

• Medical Devices: Integrate with body composition analyzers, CGM systems, blood pressure monitors, and other devices.

• Wearable and IoT Devices: Collect real-time data on activity levels, sleep patterns, heart rate, and other health metrics.

• Mapping and Measuring Tools: Incorporate data from food intake tracking apps, GPS tracking apps, and environmental sensors.

Data Ingestion Flow

1. Data Collection:

   • Patient-Entered Data

   • Healthcare Provider Data

   • Diagnostic Lab Data

   • Medical Device Data

   • Wearable and IoT Device Data

   • Mapping and Measuring Tool Data.

2. Data Reception and Parsing:

   • Parse data into structured format

   • Decode device-specific data formats

   • Use FHIR or OpenEHR standards to harmonize data.

3. Data Validation and Cleaning:

   • Implement SodaCL checks for data quality

   • Validate data against predefined rules and constraints

   • Flag and correct or discard inconsistencies and errors.

4. Data Transformation and Enrichment:

   • Transform data into suitable format for analysis and modeling

   • Enrich data with additional information from external sources.

5. Data Storage:

   • Securely store standardized and validated data

   • Implement data privacy and security measures.

6. Data Analysis and Modeling:

   • Use Apache Airflow to orchestrate data processing and analysis workflows

   • Apply AI/ML algorithms to generate insights and personalized recommendations

   • Develop predictive models to assess risk factors, predict treatment outcomes, and personalize interventions.

7. Data Visualization and Reporting:

   • Present data in user-friendly visualizations and reports

   • Enable patients and healthcare providers to track progress, monitor health metrics, and make informed decisions.

Technical Considerations

• API Integrations: Integrate with APIs from ABDM, healthcare providers, diagnostic labs, and third-party apps and tools.

• Data Streaming: Implement a streaming data architecture for efficient processing and analysis of real-time data from wearable and IoT devices.

• Scalability: Design the platform to handle large volumes of data and scale horizontally as needed.

• Security: Implement robust security measures, including encryption, access control, and data anonymization, to protect sensitive patient information.