Stanford Emergency Medicine Department Seed Grants

Since 2020 Stanford University’s Department of Emergency Medicine has awarded seed grants that encourage early-career physician-scientists in the department to explore new territories in emergency medicine.

Announcing our newest seed grant recipients!

Diversity Characteristics Improve ACS Screening Predictions for Vulnerable Populations when Screening for STEMI

The study aims to develop a diversity-enhanced logistic regression model to improve the prediction of acute coronary syndrome in emergency departments, with a focus on reducing inequities in screening and improving performance across demographic subgroups, by collaborating with Vanderbilt University and Stanford University Quantitative Sciences Unit.

Gabrielle Bunney, MD, innovation and design fellow


Promoting Emergency Physician Retention: Strategies for Inclusion and Structural Transformation (PERSIST)

A qualitative study is proposed to understand the increased attrition, especially among women, in the emergency medicine workforce since the COVID-19 pandemic, by examining the key factors influencing the decision to leave, and how these factors vary by gender and practice setting, to develop strategies to retain a diverse workforce.

Serena Hua, MD, critical care fellow

Sally Mahmoud-Werthmann, MD, assistant professor of emergency medicine


Female Altitude Physiology - Exploring Progesterone’s Role in Acute Mountain Sickness

The study aims to understand how female physiology, specifically fluctuating progesterone levels, may affect the risk of developing acute mountain sickness in high-altitude athletes, by recruiting a large cohort of pre-menopausal females and correlating their serum progesterone levels with symptoms of acute mountain sickness. The results could provide valuable data for female athletes planning high-altitude excursions.

James Marvel, MD, assistant professor of emergency medicine

Patrick Burns, MD, clinical associate professor


CompassionNet: Universal Screening for Care Needs

The study aims to improve the identification of palliative care needs in the emergency department using machine learning and natural language processing, to provide timely and targeted interventions for high-risk patients, thereby enhancing patient experience and clinical precision medicine outcomes.

Nick Ashenburg, MD, assistant professor of emergency medicine

David Kim, MD, assistant professor of emergency medicine

A Pediatric Emergency Department-Based Program to Prevent Violence Against Youth

The study aims to address the pressing issue of youth violence in the United States. by convening an interdisciplinary panel to create the first expert recommendations for a comprehensive, equity-focused violence prevention program in pediatric emergency departments, with a focus on addressing structural racism and inequities.

Preeti Panda, MD, pediatric emergency medicine fellow

Linkage to Care of At-Risk Emergency Department Patients

The study aims to understand and improve the follow-up care for uninsured or Medi-Cal-covered patients discharged from Stanford Health Care’s emergency department, by collecting data on current follow-up rates, identifying barriers to follow-up care, and designing a linkage-to-care pathway in collaboration with local Federally Qualified Health Centers, to promote equity in care and improve the patient and provider experience.

Christianna Sim, MD, social emergency medicine fellow

Ayesha Khan, MD, associate professor of emergency medicine

Previous Recipients

The Impact of restrictive state abortion law on Emergency Physician clinical practice

To understand the challenges emergency medicine doctors face following the Dobbs v. Jackson Women's Health decision, the project team will collect, analyze, and share data that can be used to support changes in policies and practices to help doctors provide the right care to their patients.

Monica Saxena, MD, JD, assistant professor of emergency medicine

Development of a Pregnancy Disclosure and Options Counseling Curriculum

Improved physician training is essential to provide personalized care to pregnant patients while avoiding bias in emergency and acute care settings. The project team will assemble experts, develop a teaching program that will enable emergency doctors to have unbiased conversations with pregnant patients and customize care based on the patient’s beliefs, preferences, and needs.

Carl Preiksaitis, MD, Stanford emergency medicine medical education fellow

A Study to Evaluate the Impact of a Pilot Mentorship Mobile Application for UIM Emergency Medicine Residents

A physician mentorship app provides an innovative, practical alternative to traditional in-person mentorship for eligible emergency medicine residents and eliminates some barriers that have traditionally disadvantaged UIM trainees from finding optimal or effective mentorship.

Sally Mahmoud-Werthmann, MD, assistant professor of emergency medicine

Performance of Nasal Alar Pulse Oximetry Probe in Aeromedical Transport

In medical air transport, vibrations from the aircraft and turbulence from air currents can lead to inaccurate readings from physiological sensors. Examining the accuracy of nasal pulse-oximetry versus the standard finger pulse-oximetry probe, analyzing their accuracy for patients with different skin tones, and comparing these numbers to results from patients with arterial lines can improve care.

Alfredo Urdaneta, MD, associate professor of emergency medicine

Using Augmented Reality to Improve Learner Familiarity with Pediatric Resuscitation Carts

Pediatric resuscitations are complex and require treatment based on a child's weight. Dedicated equipment carts provide the correct equipment but because they are sealed and sterile, they can be expensive to train on. Instead, the department created a phone app using augmented reality (AR) to help doctors learn to use the carts. The team’s goal was to determine if the AR app helped doctors acquire knowledge easily and locate important equipment during a pediatric resuscitation more quickly.

Sara Krzyzaniak, MD, director of Stanford’s emergency medicine residency program

Real-Time Time-to-Event Analytics at the Emergency Department Bedside

The difficulty for most machine learning projects is obtaining high-quality clinical labels. The team set out to better train a machine learning model and refine a hardware solution by obtaining an echocardiogram at the bedside to provide information at the moment of care. Their goal was to develop a device that could plug into the video port of any ultrasound machine, instantly providing information about the heart’s ejection fraction as well as the video quality.

Dev Dash, MD, assistant professor of emergency medicine


A Novel Application of Motion Analysis Software to Assess the Current State of Social Distancing Practices Among Visitors to Yosemite National Park

At the start of the COVID-19 pandemic, James Marvel, MD set out to provide the National Park Service with precise, actionable data on visitor use patterns in the park to inform how best to operate during the pandemic. Marvel and team used artificial intelligence to track movement patterns in Yosemite National Park with a level of precision previously unobtainable.

James Marvel, MD, assistant professor of emergency medicine


Development of a Novel Deep Learning Algorithm for the Classification of Emergency Department Renal Point of Care Ultrasound

The accuracy of point-of-care ultrasound performance can be practitioner-dependent. Deep learning, enabling algorithms to learn to read images the way that human experts do, can help augment and standardize ultrasound interpretation. By teaching artificial intelligence to assess ultrasound images of kidneys to reliably classify and segment hydronephrosis, these new algorithms can increase renal ultrasound accuracy and improve patient care.

Ting Xu Tan, MD, emergency medicine ultrasound fellow

Youyou Duanmu, MD MPH, assistant professor of emergency medicine