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<p>The increasing adoption of digital health technologies has amplified the need for robust, interoperable solutions to manage complex healthcare data. We present the Spezi Data Pipeline, an open-source Python toolkit designed to streamline the analysis of digital health data, from secure access and retrieval through processing, visualization, and export. The Pipeline is integrated into the larger Stanford Spezi open-source ecosystem for developing research and translational digital health software systems. Leveraging Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR)-based data representations, the Pipeline enables standardized handling of diverse data types, including sensor-derived observations, electrocardiogram (ECG) recordings, and clinical questionnaires–across research and clinical environments. We detail the modular system architecture and demonstrate its application using real-world data from the Pediatric Apple Watch Study (PAWS) at Stanford University, in which the Pipeline facilitated efficient extraction, transformation, and clinician-driven review of Apple Watch ECG data, supporting annotation and comparative analysis alongside traditional monitors. By reducing the need for bespoke data engineering and enabling prospective, clinician-in-the-loop analysis within standardized workflows, the Spezi Data Pipeline supports reproducible and interoperable clinical research using routinely collected digital health data.</p>

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Spezi Data Pipeline: Streamlining FHIR-based interoperable digital dealth data workflows

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月12日

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  • Vasiliki Bikia,
  • Paul Schmiedmayer,
  • Aydin Zahedivash,
  • Lauren Aalami,
  • Adrit Rao,
  • Vishnu Ravi,
  • Matthew Turk,
  • Scott R. Ceresnak,
  • Oliver Aalami

摘要

The increasing adoption of digital health technologies has amplified the need for robust, interoperable solutions to manage complex healthcare data. We present the Spezi Data Pipeline, an open-source Python toolkit designed to streamline the analysis of digital health data, from secure access and retrieval through processing, visualization, and export. The Pipeline is integrated into the larger Stanford Spezi open-source ecosystem for developing research and translational digital health software systems. Leveraging Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR)-based data representations, the Pipeline enables standardized handling of diverse data types, including sensor-derived observations, electrocardiogram (ECG) recordings, and clinical questionnaires–across research and clinical environments. We detail the modular system architecture and demonstrate its application using real-world data from the Pediatric Apple Watch Study (PAWS) at Stanford University, in which the Pipeline facilitated efficient extraction, transformation, and clinician-driven review of Apple Watch ECG data, supporting annotation and comparative analysis alongside traditional monitors. By reducing the need for bespoke data engineering and enabling prospective, clinician-in-the-loop analysis within standardized workflows, the Spezi Data Pipeline supports reproducible and interoperable clinical research using routinely collected digital health data.