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A scalable and transparent data pipeline for AI-enabled health data ecosystems.

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Summary

We developed a formal data preparation pipeline for artificial intelligence (AI) to enhance transparency and traceability in AI model development. This system ensures reliable data extraction from electronic health records for trustworthy AI applications.

Keywords:
FHIRartificial intelligencedatasetharmonizationhealth data spacesinteroperabilitytransparency

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Area of Science:

  • Artificial Intelligence
  • Health Informatics
  • Data Science

Background:

  • Lack of transparency in AI data preparation hinders reproducibility, debugging, bias detection, and regulatory compliance.
  • Manual data extraction processes are error-prone and inefficient for developing reliable AI systems.
  • Trustworthy AI requires robust methods for data preparation and traceability.

Purpose of the Study:

  • To introduce a formal data preparation pipeline specification for AI applications.
  • To improve transparency and traceability in the AI data extraction process.
  • To address challenges in developing reliable and reproducible AI models from health data.

Main Methods:

  • Developed a declarative language for extracting AI-ready datasets from health data using HL7 Fast Healthcare Interoperability Resources (FHIR) profiles.
  • Created a common data model tailored for AI use cases, defining phenotypes and AI feature specifications.
  • Implemented a scalable feature repository for executing data preparation pipelines, converting complex EHR data into a structured format.

Main Results:

  • Successfully deployed and tested the data preparation pipeline in three research projects, including predicting complications after cardiac surgery.
  • The implemented software ensures reliable, fault-tolerant processing, producing AI-ready datasets with comprehensive metadata.
  • The framework demonstrated flexibility in defining diverse features with specific temporal and contextual criteria across pilot use cases.

Conclusions:

  • The proposed formal data preparation pipeline enhances transparency and traceability in AI development.
  • The framework supports the creation of reliable AI-ready datasets from complex health data.
  • This methodology facilitates the development of trustworthy AI systems in healthcare and other data-intensive domains.