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CodeSpaceX Documentation

website·Indexed 30 days ago

https://copper3d-brids.github.io/codespacex/

A technical knowledge base covering EHR and FHIR implementation, including HAPI FHIR tutorials for Patient, Observation, and ImagingStudy resources, FHIR profiling, and mCODE Implementation Guides. It also includes DevOps guides for Elasticsearch installation and Azure 104 certification preparation, as well as micro-frontend API gateway routing for Vue plugins.

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What's inside CodeSpaceX

  1. Overview of Elasticsearch and the ELK Stack

    Elasticsearch is an open-source distributed search engine designed for document storage (such as product data or order messages), where data is serialized into JSON format. It is commonly used for search functionality, log statistics, analysis, and system monitoring.

    Elasticsearch is part of the Elastic Stack (ELK), which consists of:

    • Elasticsearch: The core engine used to search and store data.
    • Beats: Used for data collection.
    • Logstash: Used for data collection and processing.
    • Kibana: Used for data visualization.

  2. Overview of the HL7 mCODE Implementation Guide

    The minimal Common Oncology Data Elements (mCODE) Implementation Guide, published by HL7 International and the Clinical Interoperability Council, provides a standardized set of FHIR resources for oncology data. Version 3.0.0 utilizes approximately 30 FHIR resources to define the core data elements required for oncology clinical research and care.

  3. Components of the Rule Engine for clinical inference

    The Rule Engine architecture consists of the following specialized services:

    • FHIR Adapter: Converts FHIR data to internal Rule Engine format and supports invocation via the CDS Hooks specification.
    • Rule API: Handles internal routing and saves processed data.
    • Filter: Filters data to the actual state for logical output.
    • Rule Engine: Executes the logical inference mechanism based on rules.
    • Formatter: Formats the results of the logical inference.
    • Api.KnowledgeService: Manages CRUD operations for the graphical knowledge base (rules, artifacts, definitions).
    • Api.FactService: Preserves and provides facts during inference.
    • Api.JobStatusService: Manages task creation and status tracking.
    • Api.ErrorService: Reports and saves errors occurring during inference.
    • Api.ResultService: Saves and provides inference results.
    • TerminologyService: Stores and provides medical terminology.

  4. EHR Server and DigitalTWINS Platform Architecture

    The EHR server architecture is based on FHIR and integrates hospital data with the DigitalTWINS platform. The system utilizes a HAPI FHIR Server as the central hub for health data, backed by PostgreSQL. It integrates with an Orthanc Server for DICOM/PACS imaging studies and an OMOP Server for standardized observational data. The Backend/DigitalTWIN API acts as the orchestration layer, utilizing plugins such as fhiypy, sparc-me, and an OMOP python plugin to interact with these servers via RESTful APIs.
    [Orthanc Server] <--> [HAPI FHIR Server] <--> [PostgreSQL]
                                 ^ 
                                 | (RESTful API)
                                 v
                      [Backend/DigitalTWIN API]
                      (fhiypy, sparc-me, OMOP python plugin)

  5. Structure a FHIR Implementation Guide

    A comprehensive Implementation Guide should generally include the following sections:

    About the Implementation Guide

    • General introduction.
    • Instructions on how to use the guide and its scope.
    • Relevant contact information.

    Use Case(s)

    • Overview of systems and architecture.
    • Actor descriptions, including scenarios, triggers (sequence diagrams), and pre/post conditions.
    • Business rules and policies (e.g., "Systems must have a consent on file for the patient").

    Technical Implementation Guide

    • Detailed technical specifications and profile links.

  6. Connect Azure to ITSM tools via ITSMC

    The IT Service Management Connector (ITSMC) allows Azure services (like Azure Log Analytics and Azure Monitor) to integrate with supported ITSM products to manage work items related to detected issues. Supported tools include ServiceNow, System Center Service Manager, Provance, and Cherwell.

  7. Structure a FHIR Implementation Guide

    A comprehensive FHIR Implementation Guide should include the following sections to ensure clarity for developers and implementers:

    • Use Case/Transaction Details: Introduction, scope, boundaries, relationships (used by/uses), involved actors, and functional requirements (e.g., sender/receiver responsibilities).
    • RESTful Exchange Details: Examples of HTTP calls, interactions, operations, and search details.
    • Profiles: A list of defined profiles including an introduction, the canonical URL identifying the profile, a rendering of the data structure, and explanations for special handling or exceptions.
    • Other Conformance Resources: Descriptions and renderings of artifacts like ValueSets or CapabilityStatements.
    • Examples: At least one example instance per profile. When multiple resource types are used, examples should include references to each other.
    • Appendix & Help: General guidance, feedback channels, and supplementary information.

  8. Architecture of an FHIR-based CDSS microservice platform

    The platform is designed as a set of distributed microservices that communicate asynchronously via REST. Each service operates through public contracts in FHIR JSON format and maintains an independent release cycle with versioned interfaces.

    Key service components include:

    • Business API: Handles service-specific requests (e.g., retrieving artifacts or terminology).
    • Event API: Allows external services to send event requests to track platform status.
    • Health Check API: Monitors service health, including infrastructure connections, host status (e.g., disk space), and application logic.
    • Business Logic: Implements the core feature (e.g., loading facts into an inference engine).
    • Event/Business Logic Stores: Manage and persist data related to service processes.
    • Other Service Client: Manages active communication and result transmission between services.

  9. Implement a universal UMD plugin architecture for dynamic CDN deployment

    When building complex applications (React, WebGL, WASM) as UMD plugins for third-party integration, follow these three golden rules to ensure the plugin can resolve its own assets regardless of where it is hosted:

    1. Entry Hijacking: At the very top of the entry file, detect the script's source URL using document.currentScript.src and store the host base path in a global variable (e.g., window.__MY_APP_BASE__).
    2. Loader Hijacking: Redirect the build tool's resource loader to use the captured base path.
      • For Vite: Use experimental.renderBuiltUrl in the config.
      • For Webpack: Set the magic variable __webpack_public_path__ = window.__MY_APP_BASE__ at the top of the entry file.
    3. Eliminate Hardcoded Paths: Replace all relative paths (e.g., new Worker('/assets/xxx.js')) with the dynamic base path: new Worker(window.__MY_APP_BASE__ + 'assets/xxx.js').

  10. Implement OMOP on FHIR architecture

    The architecture for implementing OMOP on FHIR typically follows this flow:

    OHDSI Environment (WebAPI) $\rightarrow$ OMOP on FHIR (HAPI FHIR Server + OMOP v5 data repository + FHIR DSTU2/v3) $\rightarrow$ SMART on FHIR.

    Implementation Constraints:

    • Data Mapping: Because FHIR contains more comprehensive information than the OMOP CDM, elements that cannot be mapped to OMOP are typically ignored unless specifically required by the FHIR version.
    • Missing Elements: Certain FHIR elements, such as specific patient demographic information, may not have direct equivalents in the OMOP CDM.

  11. Use the ImagingStudy FHIR resource for DICOM imaging data

    The ImagingStudy resource represents the content produced in a DICOM imaging study, comprising a set of series (each of a single modality like X-ray, MR, CT) and Service-Object Pair Instances (SOP Instances). It provides metadata and instructions on how to retrieve the actual DICOM data from a storage mechanism, such as a DICOM WADO-RS server, as the DICOM instances themselves are not stored within the FHIR resource.

  12. EHR Server Architecture Overview

    The EHR server architecture is designed to integrate hospital, study (RedCap), and remote monitoring data into a FHIR-based system, which then feeds into the DigitalTWINS platform. The data flow follows a pipeline of export, FHIR resource bundling, de-identification, and storage in a Study Data Set (SDS) before being accessed by the 12L Portal APP or other applications via an Authentication Layer.