Open Platforms for Health - Computational Precision Health

Open Platforms for Health:
Transforming health data collection, research, and care

Ida Sim, MD, PhD Co-PI, Open Platforms for Health
Co-Director, Computational Precision Health
Professor of Medicine & Physician, UCSF
Chief Research Informatics Officer, UCSF

Fernando Pérez, PhD Co-PI, Open Platforms for Health
Faculty Director, Berkeley Institute for Data Science
Associate Professor of Statistics, UC Berkeley CDSS
Co-Founder, Project Jupyter

The project: Led by Ida Sim, MD, PhD, co-director of the Joint Program in Computational Precision Health (CPH), and Fernando Pérez, PhD, incoming faculty director of the Berkeley Institute for Data Science (BIDS), Open Platforms for Health is using the power of open science to transform collection and interpretation of health data in the treatment of metabolic disorders. More than 38 million Americans have diabetes, with African Americans and other people of color disproportionately affected. The inaugural effort of Open Platforms for Powering Science and Society initiative at the UC Berkeley College of Computing, Data Science, and Society, Open Platforms for Health will pilot a new approach to the development of algorithms and clinical decision support using data from wearable devices, such as blood pressure cuffs and continuous glucose monitoring, to be deployed first at UCSF’s General Internal Medicine, Diabetes, and Weight Management clinics.

The approach: Data from wearables and algorithms used to interpret them are generally closely guarded by device makers and siloed by disease. By contrast, Open Platforms for Health will be powered by JupyterHealth – a groundbreaking, open platform that facilitates the secure collection of health data from sensors and medical records, and supports the development, sharing and testing of digital biomarker algorithms for clinical use. JupyterHealth will bring the Project Jupyter ethos – open source software, shareable notebooks and code to support reproducibility, and vendor-agnostic infrastructure – to enable new research collaborations for public benefit and transform health algorithm development. The platform will also allow patients and physicians new ways to personalize diabetes management, providing real-time insights into blood sugar fluctuations. Initially focused on metabolic disorders, JupyterHealth aims to expand to enable data collection, development, and testing of algorithms across various conditions, revolutionizing data-driven care for the patients with multiple chronic diseases who constitute nearly 40% of the U.S. population.

Open Platforms for Health is made possible by an anonymous philanthropic investment from a visionary donor.

The partners: Experts at Berkeley and UCSF are bringing together communities of practice, clinicians, computer scientists, researchers and innovators in patient privacy and health information management to build JupyterHealth. Team members include developers from 2i2c, the organization that manages the design, configuration and linkage between Jupyter datahubs now used by more than 10 million researchers and educators worldwide; The Commons Project Foundation, a global tech nonprofit pioneering approaches to patient-centered, privacy-protecting management of health information; and the Duke University Big IDEAs Lab, specializing in the analysis of multimodal data and development of biomarkers to predict health risk.
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For inquiries and further information, please reach out to us at JupyterHealth@berkeley.edu