Projects

NIH Funded

RESCU-ER

This project works towards reducing rural-urban disparity in pediatric mortality from life-threatening emergencies. We have conducted an in-depth investigation on the epidemiology of rural child outcomes and developed an app that consists of a cognitive aid intervention for pediatric cardiac arrest and infant resuscitation. Our experienced team aims to identify modifiable factors and enhance EMS performance to improve survival and outcomes for rural children.

rescuer-app-example

ml

NIH Funded

Machine Learning for Detecting Adverse Safety Events

Our team has designed and continues to develop an ontology-driven NLP tool to identify adverse safety events from children EMS text charts patient. Future work aims to demonstrate the scalability of this automated screening system on additional datasets to understand and describe the incidence of and contributors to preventable safety events.

Peer-reviewed work

Publications & Talks

  1. Trujillo Jimenez, D., Wang, D., Bahr, N., Hsieh T.YJ., Cho, B., Meckler, G., Hansen, M., Eriksson, C., Kim, K.S., Bedrick, S., Jiang, S., and Guise, JM., 2025. A Medically–Grounded LLM Agent–based Decision Tool to Detect Patient Safety Events in Medical Records. (To be submitted)
  2. Lee, S.A., Sanjeevi, J., Trujillo Jimenez, D., Hansen, M., Meckler, G., Bahr, N., Huynh, T., Newgard, C., Guise, JM. RESCUER mobile app to support pediatric resuscitation: protocol for a randomised control trial. Contemporary Clinical Trials Communications.
  3. Trujillo Jimenez, D., Wang, D., Bahr, N., Hansen, M., Meckler, G., Eriksson, C., Jiang, S., and Guise, JM., 2025. Machine Learning and AI Enabling the EMS System to Deliver Learning Health System Care​. NYU LHS Conference.
  4. Trujillo Jimenez, D., Wang, D., Bahr, N., Hansen, M., Meckler, G., Eriksson, C., Jiang, S., and Guise, JM., 2025. SAFE-AI: A medically grounded AI method to identify patient safety events in healthcare. BIDMC ML & AI Conference.
  5. Trujillo Jimenez, D., Hsieh T.YJ., Cho, B., Bahr, N., Eriksson, C., Hansen, M., Meckler, G., Jiang, S., and Guise, JM., 2024. Large Language Models and their Promise in Clinical Practice: A Case Study in Automated Chart Review​. BIDMC Annual Meeting.
  6. Trujillo Jimenez, D., 2024. Using GPT to Accelerate Progress Bridging the Quality Chasm of Medication Errors. Academy Health Annual Research Meeting.

collaboration-invitation