Interview Questions with PMWC 2025 Luminary Honoree: Ida Sim, UCSF
1. Your work with JupyterHealth is pioneering the secure collection and computation of health data from various sources. Could you share how JupyterHealth is currently being implemented in real-world healthcare settings, and what impact you anticipate it having on patient outcomes?
Answer:
JupyterHealth is currently in early development with use cases drawn from real-world primary and subspecialty care from UCSF and Weill Cornell. Our participatory design process has included frontline clinicians and lay users of wearable sensors.
JupyterHealth is a technology platform that will accelerate the process of sensor data acquisition, digital biomarker development, and integration of digital health solutions into clinical workflows. JupyterHealth is disease agnostic. As such, we anticipate broad impact on patient outcomes across multiple disease and prevention domains. For example, we anticipate JupyterHealth accelerating companies that draw on multiple sensor streams and electronic health record data to generate individualized metabolic profiles and personalized decision support for patients to optimize their own metabolic health. While such companies currently exist, lowering the technical barrier to entry and the backend operating costs while creating an open community for sharing and co-creating new computational approaches should result in more robust digital health markets that will then impact patient outcomes.
2. As Co-Founder of Vivli, you’ve revolutionized data sharing for clinical trials. How do you see the role of large-scale data sharing evolving in the next five years, particularly in the context of integrating data from diverse sources like sensors and medical records?
Answer:
Vivli has been an inspiring success story for how understanding of technology, research culture, and industry priorities can be harnessed together to create a new way forward towards open science. With the NIH’s data sharing mandate (in which funded studies must make their data available for reuse), scientific data sharing will increase in scale and scope in the coming years. Major expansions to sharing of imaging and genomics data are likely.
Medical records– in the form of the US Core Data for Interoperability (USCDI) dataset – can already be accessed for individual patients and in bulk through federally mandated APIs. JupyterHealth will further unleash the ability to securely access sensor data from individuals, integrated with USCDI access. The value of data sharing comes only if the shared data are used, however. It is not enough to bring standardized data together. Fostering large-scale data reuse requires scalable analytic platforms that not only bring sensor and medical record data together but will connect them to reusable analytic pipelines that can easily be packaged into implementable solutions. Building on the proven success of Project Jupyter, JupyterHealth uniquely addresses both large-scale data acquisition and data access and large-scale data use and reuse in digital health. Like Vivli, JupyterHealth reflects a deep understanding of enabling technology, digital health scientific needs, and industry dynamics to build a new pathway for digital health impact.
3. With your dual role in academic research and clinical practice, how do you envision the integration of advanced computational tools into everyday clinical workflows, and what are the biggest challenges you foresee in achieving this integration?
Answer:
Currently, clinical workflows are anchored in the electronic health record (EHR) system. Integration of computational tools into clinical workflows are therefore constrained by EHRs, which often limit the ability of 3rd party computational tools to access the care context (what task is the clinician trying to complete? What are the latest relevant test results?) or to write back into the official medical record (which limits the utility and potentially billing of services provided).
FHIR and SMART-on-FHIR standards are important mechanisms for enabling greater workflow integration but are not by themselves sufficient to ensure large-scale effective integration into everyday care.
Moreover, the heavy lift for integrating advanced computational tools is far more than a technical problem. Workflow re-engineering requires organizational change capabilities that ideally include the ability to rapidly prototype and adapt tools to local practice styles. JupyterHealth’s support for iterative “dev-ops” aims to facilitate such rapid prototyping.
Lastly, with recent AI advances, health systems need to establish governance processes to ensure appropriate AI usage and to conduct ongoing monitoring for performance bias and drift. These processes require clinical AI expertise, which is not yet widely available.
