Architecture

Architecture

A Clinical Decision Support System (CDSS) is a software application designed to assist healthcare providers in making clinical decisions. It provides evidence-based information and recommendations to improve patient care.

Key Architectural Components of a CDSS

A CDSS typically consists of the following architectural components:

  • User Interface:

    • Web-based interface: Accessible through web browsers.
    • Mobile app: For on-the-go access.

  • Integrated into EHR: Embedded within the electronic health record system.

  • Knowledge Base: Stores clinical guidelines, evidence-based medicine, and other relevant medical knowledge. Can be structured or unstructured. May use ontologies and knowledge graphs to represent complex relationships between concepts.

  • Inference Engine: Processes patient data and applies the knowledge base to generate recommendations. Uses various reasoning techniques like rule-based, case-based, or probabilistic reasoning. Can incorporate machine learning algorithms for predictive analytics and personalized recommendations.

  • Data Source: Collects patient data from various sources like EHRs, laboratory systems, and wearable devices. Can integrate with real-time data streams for timely decision support.

  • Alert and Notification System: Sends timely alerts and notifications to healthcare providers. Can be customized based on user preferences and urgency of the alert.

Architectural Considerations

  • Interoperability: The CDSS should seamlessly integrate with existing healthcare systems, including EHRs and laboratory systems.
  • Usability: The user interface should be intuitive and easy to use, especially for busy healthcare providers.
  • Security and Privacy: Strict security measures must be in place to protect patient data and privacy.
  • Scalability: The system should be able to handle increasing workloads and data volumes.
  • Maintainability: The architecture should be modular and well-documented for easy maintenance and updates.
  • Performance: The system should provide timely responses and avoid performance bottlenecks.
  • Flexibility: The system should be adaptable to changing clinical guidelines and emerging technologies.

The DAECOS platform is under heavy development at present and the above mentioned architectural considerations have been taken into account while designing the platform.

Introduction