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AI & Analytics

The iEHR platform leverages advanced AI and analytics tools to transform healthcare data into actionable insights. Ensuring data quality and integrity is critical for generating meaningful, accurate reports and improving decision-making processes. Continuous monitoring of data quality plays a key role in maintaining these standards.

iEHR AI & Analytics


Key Components of Analytics

  1. FHIR® Datastore

    • Stores data using standard fields, with a focus on common resources such as Patient and Observation.
    • Ensures interoperability and consistency for analytics applications.
  2. Bots and Subscriptions

    • Maintain real-time data quality and correctness.
    • Example: Automatically tagging Encounter.type with the correct ontology and generating errors if incorrect.
  3. Access Policies

    • Provide secure and de-identified data pipelines for privacy-compliant analysis.
    • Protect sensitive patient and operational data during AI and analytics processes.
  4. Bots Integration

    • Synchronize with external tools, machine learning pipelines, and analytics workflows.
    • Facilitate seamless integration with industry-standard applications for advanced data processing.

3 Classes of AI & ML powering iEHR.ai Platform

iEHR MCP (Model Context Protocol) server allows AI models to interact with iEHR CDR and insignts. It essentially acts as a bridge between AI models and the FHIR datastore, enabling them to retrieve information and execute tasks within iEHR.ai platform.

iEHR AI & Machine Learning

  1. AI & Machine Learning for Data Transformation
    Leveraging advanced AI and machine learning techniques, unstructured data sources are processed and converted into a structured, FHIR-formatted datastore. This step ensures that raw, disparate data is standardized and organized for seamless interoperability and usability.

  2. AI & Machine Learning for Insight Generation
    By analyzing the FHIR-formatted datastore, AI and machine learning models extract meaningful insights and patterns. This enables a deeper understanding of the data, supporting data-driven decision-making and fostering innovation.

  3. Generative AI for Enhanced Utilization
    Building on the insights generated, generative AI models utilize the FHIR datastore as their foundation. These models create sophisticated outputs or applications by synthesizing information from the datastore, driving actionable results and expanding possibilities.


Applications in Healthcare Data Analytics

Healthcare analytics is broadly divided into two key areas of application:

Retrospective Analysis

  • Performance Metrics: Evaluate operational efficiency and quality metrics to identify areas of improvement.
  • Quality Assessment: Analyze historical data to measure adherence to clinical standards and optimize healthcare delivery.

Predictive Modeling

  • Clinical Decision Support (CDS): Encode evidence-based clinical guidelines into rules-based recommendations to guide clinicians.
  • Patient Patterns: Use machine learning to uncover commonalities across patients and develop personalized care plans.
  • Future Behavior Forecasting: Predict patient outcomes and optimize resource allocation based on data-driven insights.

Benefits of AI & Analytics in Healthcare

By leveraging AI and analytics, iEHR empowers healthcare organizations with:

  • Enhanced Decision-Making: Provide clinicians and administrators with actionable insights derived from real-time data.
  • Improved Operational Efficiency: Streamline workflows and resource allocation based on predictive models.
  • Personalized Patient Care: Tailor care plans to individual needs through pattern recognition and predictive analytics.
  • Data Security and Privacy: Ensure secure, compliant, and ethical usage of patient data throughout the analytics lifecycle.

For more detailed information or to explore how iEHR's AI & analytics can transform your healthcare operations, feel free to contact us.