# Machine Learning Models of Appropriate Medevac Utilization in Rural Alaska

> **NIH NIH K08** · STANFORD UNIVERSITY · 2023 · $166,320

## Abstract

PROJECT SUMMARY / ABSTRACT
The purpose of this award is to provide Dr. Brian Rice, Assistant Professor of Emergency Medicine at Stanford
University, the support necessary for his transition from a junior investigator into an independent clinician-
scientist using applied biomedical informatics to address health disparities. Dr. Rice is an emergency medicine
physician with an advanced degree in epidemiology and global health, and a background in computer
programming and artificial intelligence. His long-term goal is to utilize his interdisciplinary training to develop
and implement machine learning tools to empower precise, high-value clinical decision-making surrounding
emergency care and transport in historically disadvantaged populations. His training activities focus on
advancing his ability to apply biomedical informatics to address health disparities via these training objectives:
1) expanding his skills in data management and computational statistics 2) learning methods for community-
engaged and participatory approaches to health disparities research, and 3) acquiring new skills machine
learning and classification model building. The candidate has convened a mentorship team that includes Dr.
Tina Hernandez-Boussard, a biomedical artificial intelligence expert with a focus on improving transparency
and minimizing bias in machine learning models to make them more equitable and generalizable, and Dr.
Stacy Rasmus, a leading Alaska Native behavioral scientist with extensive experience successfully conducting
community-engaged qualitative research in rural Alaska. The research proposal builds off the candidate’s prior
work with air medical evacuation (medevacs) in rural Alaska which established the central hypothesis that
medevacs can be classified as appropriate or inappropriate by machine learning models built on outcome data
and enriched by qualitative methods. This central hypothesis will be tested by the following specific aims: 1)
define the burden and outcomes of medevacs in rural Alaska; 2) identify key context-specific contributors to
medevac utilization in rural Alaska; and 3) develop machine learning models to classify appropriateness of
medevac utilization in rural Alaska. The research proposed in this application is innovative because it employs
accepted methods of machine learning classification modelling and applies them to novel fields of medevac
and Alaska Native health disparities. The significance of the proposed training grant is it will provide the data
and the skills required for Dr. Rice to subsequently study the implementation of these models as a decision tool
in a future R01-level application. Ultimately, this continuum of research has the potential to decrease expenses
and improve safety by redirecting medevac resources towards patients whose time-sensitive conditions benefit
from medevacs and away from patients that incur risk and cost without benefit, both in Alaska Native
communities in rural Alaska and for all Ame...

## Key facts

- **NIH application ID:** 10653776
- **Project number:** 5K08MD016445-02
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Brian Travis Rice
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $166,320
- **Award type:** 5
- **Project period:** 2022-06-26 → 2027-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10653776

## Citation

> US National Institutes of Health, RePORTER application 10653776, Machine Learning Models of Appropriate Medevac Utilization in Rural Alaska (5K08MD016445-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10653776. Licensed CC0.

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