UC Tech News is excited to share the stories of the people and projects behind the winners of the 2026 UC Tech Awards.
Team Name: UCSF-UCLA Point-of-Care Randomization (POCR) Collaboration Team
Award Category: Yvonne Tevis UC Collaboration
Location: UCSF and UCLA
Team Members:
- Alina Goncharova, Technical Program Manager, Research Informatics, UCSF
- Andrew Robinson, Health IT, UCSF
- Jory Purvis, Technical Lead, Research Informatics, UCSF
- Mark Pletcher, Chair, Department of Epidemiology & Biostatistics, UCSF
- Catherine Sarkisian, MD, MSHS, UCLA
- Khalda Ibrahim, MD, UCLA
- Sitaram Vangala, MS, UCLA
- Chad Wes Villaflores, MS, UCLA
- Eric M. Cheng, MD, MS, UCLA
- William Turner, UCLA
- Richard K. Leuchter, MD, UCLA
- Amy Machado, UCLA
- Julia Tabar, UCLA
- Jade A. Verdeflor, MPH, UCLA
The UCSF-UCLA Point-of-Care Randomization (POCR) Collaboration Team won the Silver Yvonne Tevis UC Collaboration Award at the 2026 UC Tech Awards for developing and scaling the POCR engine, a first-of-its-kind capability that enables real-time randomized trials within the Electronic Health Record (EHR).
Project Summary
Randomized controlled trials (RCTs) are the gold standard for determining which interventions improve care, but operational, technical, and organizational barriers make them difficult to conduct as part of routine clinical workflows.
To address this challenge, UCSF created the POCR engine, which integrates randomized trials within the EHR. During a patient visit, the engine can identify eligible participants in real-time, randomly assign them to a study group, and automatically capture data into analytics platforms.
Building on this foundation, UCSF partnered with UCLA to implement the POCR engine within a separate health system. Together, the campuses worked through different technical infrastructure, implementation processes, and workflows to create a shared innovation capability.
The successful cross-campus collaboration established a scalable, repeatable model for embedding clinical research into everyday care. By making rigorous trials easier to conduct, health systems can continuously learn from routine patient encounters, test and refine interventions, and generate high-quality evidence about what works.
Alina Goncharova, the team representative, shared some of her thoughts on the project and its impact in the Q&A below.
How did collaboration across teams or campuses shape the project?
This project would not have succeeded without deep collaboration between UCSF and UCLA. The UCLA team came in highly organized, with clear pilot use cases and a strong commitment to making embedded randomization work in practice. UCSF provided implementation support through a recharge-based model, while UCLA invested time and effort into understanding the technical and operational requirements within their own Epic environment. That partnership helped us refine not only the technology itself, but also our documentation, onboarding materials, and implementation guides for future adopters. More broadly, the collaboration showed how much the UC system can accomplish when campuses share expertise rather than working in silos.
What did winning this UC Tech Award mean to your team personally or professionally?
Winning a UC Tech Award this year feels especially meaningful because so many teams across higher education and healthcare are operating under real budget and staffing pressures. Our team is proud that, even in that environment, we were able to build and scale something that advances the vision of a learning health system. Professionally, the recognition validates years of collaboration across informatics, research, operational, and clinical teams at both UCSF and UCLA. Personally, it reinforces that this work matters, not just as a technical achievement, but as infrastructure that can help health systems rigorously evaluate and improve patient care through embedded randomized quality improvement initiatives.
What’s next for this project?
We’re excited that additional organizations have already expressed interest in implementing the randomization engine, and we hope to continue supporting broader adoption across the UC system and beyond. One major next step is development of a Clinical Decisions Support (CDS) Hooks–based version of the engine (pocr.ucsf.edu), which would allow health systems to use a cloud-based randomization service without requiring the same level of local Epic customization and implementation effort. We’re also continuing to expand the broader framework for randomized quality improvement (RQI) projects, helping health systems evaluate interventions more rigorously and generate stronger evidence directly within clinical workflows. Our long-term goal is to make embedded randomized evaluation a routine part of healthcare improvement.
Contact
Alina Goncharova, Technical Program Manager, Research Informatics, UCSF
