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Medical Data Governance

Disease-specific medical databases and knowledge graphs that clean, fuse, and intelligently govern multi-source heterogeneous medical data to build the foundation for AI applications.

Overview

High-quality data is the cornerstone of medical AI. Simo provides professional medical data governance services, helping institutions transform the multi-source heterogeneous data scattered across HIS, EMR, PACS, and LIS systems into structured, standardized, computable datasets — a solid data foundation for AI application development and clinical research.

Core Capabilities

1. Multi-Source Data Fusion

  • Bridges data silos across HIS, EMR, PACS, and LIS systems
  • Builds a patient-centric unified data view (visit → examination → prescription → follow-up, full chain)
  • Heterogeneous data format conversion and standardization (HL7/FHIR/DICOM adapters)
  • Historical data migration and archiving

2. Data Structuring

  • Intelligent extraction from unstructured clinical notes (chief complaint, history of present illness, past history, etc.)
  • Automatic extraction and unit-standardization of key indicators in reports
  • Structured association between imaging report text and DICOM tags
  • Structured entry and quality control of follow-up data

3. Disease-Specific Database Construction

  • Custom disease data models by specialty (cardiology, neurology, oncology, orthopedics, etc.)
  • Automated inclusion/exclusion screening and batch enrollment of historical cases
  • Automatic data-quality checks (completeness, consistency, timeliness, accuracy)
  • Multi-center data standardization and joint analysis support

4. Medical Knowledge Graph

  • Builds disease–symptom–examination–drug relationship graphs from authoritative medical knowledge bases
  • Automated entity recognition and relation extraction
  • Continuous knowledge-graph updates and versioning
  • Visual querying and graph analysis

Delivery Process

  1. Data audit: comprehensively review existing systems, data types, and data quality
  2. Model design: customize the data model and standardization plan
  3. Governance implementation: data cleaning, structuring, fusion, and quality verification
  4. Platform delivery: deploy the data management platform and train users
  5. Ongoing maintenance: data-quality monitoring and periodic optimization

Technical Advantages

Dimension Traditional data management Simo Data Governance
Structuring efficiency Manual entry, one record at a time AI automatic extraction and structuring
Data quality Post-hoc spot checks Real-time automatic verification
Standard unification Department-defined ad hoc Unified data standard model
Knowledge association Isolated scattered data Interconnected knowledge graph

Want to understand the process? See Cooperation Process for complete steps.

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