Modules Overview¶
HealthData.ai is a web-based platform for clinical data management and medical research.
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Clinical Documentation
Structured SOAP-based records and reports.
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Data Collection
Consistent multicentre data capture and validation.
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Medical Research
Study-ready datasets with governed access.
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Biobanking
End-to-end sample lifecycle management.
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Medical Imaging
PACS integration and structured imaging workflows.
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Machine Learning
Model development and reproducible evaluation.
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Patient Access
Digital touchpoints for questionnaires and follow-up.
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Data Model
Flexible EAV-based structures for clinical and research data.
Internationalization and User Interface¶
The user interface can be provided in multiple languages and automatically follows the visitor's browser language settings. The web application is built on open standards (HTTP(S), HTML, CSS, JavaScript) and requires only a modern browser on the client side.
Dashboards and Analytics¶
The platform provides various dashboards for data visualization and analysis. These range from patient-specific views to aggregated analytics at institution and network level.
Typical capabilities include:
- patient dashboards with a complete case overview
- institution and network dashboards for operational and clinical steering
- real-time analytics based on current data
- filter and analysis functions for different user groups
These tools help teams make clinical and organizational decisions based on consistent, up-to-date information.
Performance, Scalability, and Extensibility¶
The architecture follows a proven web application model with centralized data storage and scalable web servers. As demand grows, the platform can scale horizontally across multiple servers to improve performance and availability.
The flexible data model allows fields and metadata to be added or adjusted without changes to core application logic. Configurable questionnaires and protocols support domain-specific extensions during ongoing operations.
EAV Data Model¶
HealthData.ai uses an Entity-Attribute-Value (EAV) model, in which attributes are stored as records rather than fixed columns. This enables new attributes to be added without schema migrations and supports historization through metadata such as author and capture time.
For research-oriented analysis, data is periodically transformed into tabular structures and provided as prepared exports, for example in CSV format.
Standards and Interfaces (FHIR)¶
The integrated REST API is based on the HL7 FHIR standard and enables structured, secure data exchange with external systems such as ERP, archive, or MPI. Its resource-based model is particularly suitable for interoperable, including mobile, use cases.