JupyterHealth
// Open Software Platform for Health Data Ingestion and Management Empowering Researchers, Clinicians and Patients.
JupyterHealth
JupyterHealth is a modular, open-source platform designed to transform how health data flows from wearable devices, electronic health records, and other sources into tools for research, clinical insight, and patient-centered care. By combining robust standards compliance (e.g., FHIR, Open mHealth), scalable cloud deployment, and interactive computational tools like JupyterHub, JupyterHealth enables secure ingestion, analysis, and visualization of diverse health data — democratizing access to powerful health analytics for researchers, clinicians, and technology developers alike. JuypterHealth is co‑led by mDOT Center Founding Co‑Investigator Dr. Ida Sim and supported by mDOT Center CP Lead Dr. Jessilyn Dunn, whose expertise in health data interoperability and multimodal biomedical data integration guides the platform’s development and application in research and clinical workflows.
What It Is: JupyterHealth is open-source software that extends the popular Jupyter ecosystem into healthcare. It provides a standards-aligned, flexible architecture for collecting, storing, transforming, analyzing, and presenting health data — with modules that can be used independently or together. Its design supports interoperability across healthcare systems and accelerates reproducible research and care workflows.
Why It Matters: Traditional health data systems often keep patient-generated and clinical data siloed, accessible mostly by large organizations. JupyterHealth champions open science and accessibility, leveling the technical playing field so researchers, clinicians, and innovators of all sizes can integrate real-world data into research, algorithm development, and clinical decision-making. It supports transparency, reproducibility, and extensible computational workflows that drive insights and accelerate advances in health.
Details & Specifications
IN USE BY:
Category:
Technologies, Datasets, Models, Modules, Frameworks
Tags:
Interoperability
Health Data
Analytics
HL7 FHIR
Wearable Data
EHR Integration
Health Data
Analytics
HL7 FHIR
Wearable Data
EHR Integration
Modular Data Ingestion & Standardization
- Pulls data from wearables, mobile apps (e.g., Apple HealthKit, CommonHealth), and certified EHRs
Standardizes inputs to FHIR and Open mHealth formats for seamless interoperability
Secure Storage & Access Control
- Supports secure storage of sensitive health data (including PHI)
Includes authentication layers (SMART on FHIR, OAuth/OIDC) and connects with common identity systems
Interactive Analysis Environment
- Uses JupyterHub for collaborative notebooks and workflows, reducing IT overhead
Supports data exploration, algorithm development, and computational reproducibility
Presentation & Visualization Tools
- Enables deployment of dashboards and clinical views using tools like Voila
Aims to integrate interactive visualizations into existing health workflows via SMART Launch apps
Standards Compliance & Interoperability
- Built around HL7 FHIR, Open mHealth, SMART on FHIR, and other widely accepted protocols
Designed for both research use and scalable clinical deployment
Digital health research: Combine wearable signals and clinical records for novel biomarker discovery.
Population health analytics: Standardize diverse data sources for large-scale epidemiologic studies.
Clinical decision support: Build and deploy interactive tools that help clinicians visualize real-time data trends.
Teaching & reproducible science: Use shared notebooks and environments for collaborative health data science.
“Berkeley Launches Agile Metabolic Health and Open Platforms Initiative” – UC Berkeley research office article describing JupyterHealth’s role in an open platforms initiative to transform health data collection and analysis.
The Commons Project: Agile Metabolic Health and JupyterHealth – News release from The Commons Project about how JupyterHealth supports open health data collection for metabolic disorders research.
Open Platforms for Health – Computational Precision Health (UC Berkeley & UCSF) – Project page describing the Open Platforms for Health initiative powered by JupyterHealth.
Website: https://jupyterhealth.org
Primary Contact: JupyterHealth@berkeley.edu
Location: Berkeley, CA, USA