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

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

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PubMed Full text at PMC Publisher

In one paragraph

Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Vasiliki BikiaStanford University, Department of Biomedical Data Science, Stanford, CA, USA. bikia@stanford.edu.ORCID http://orcid.org/0000-0002-4660-1560

Paul SchmiedmayerStanford University, Stanford Mussallem Center for Biodesign, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-8607-9148

Aydin ZahedivashStanford University, Lucile Packard Children's Hospital, Department of Pediatrics, Pediatric Cardiology, Stanford, CA, USA.ORCID http://orcid.org/0000-0001-6835-1139

Lauren AalamiStanford University, Stanford Mussallem Center for Biodesign, Stanford, CA, USA.ORCID http://orcid.org/0009-0007-7132-5362

Adrit RaoStanford University, Stanford Mussallem Center for Biodesign, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-0780-033X

Vishnu RaviStanford University, Stanford Mussallem Center for Biodesign, Stanford, CA, USA.ORCID http://orcid.org/0000-0003-0359-1275

Matthew TurkStanford University, Stanford Mussallem Center for Biodesign, Stanford, CA, USA.

Scott R CeresnakStanford University, Lucile Packard Children's Hospital, Department of Pediatrics, Pediatric Cardiology, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-9473-0105

Oliver AalamiStanford University, Stanford Mussallem Center for Biodesign, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-7799-2429

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Identifiers

PMID42120671

DOI10.1038/s41746-026-02666-7

PMCPMC13623967

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.