Diego A. Martínez
Assistant Professor of Industrial Engineering
Pontificia Universidad Católica de Valparaíso
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School of Industrial Engineering at PUCV
2241 Brasil Ave, Room 5-8 Valparaíso, Chile Tel: +56 (32) 227-3701 E-mail: diego.martinez.c@pucv.cl Websites: Google Scholar Profile PUCV School of Industrial Engineering Johns Hopkins Malone Center for Engineering in Healthcare Johns Hopkins Center for Data Science in Emergency Medicine |
Diego A. Martínez is currently serving as an Assistant Professor of Industrial Engineering at Pontificia Universidad Católica de Valparaíso, and holds courtesy positions in Emergency Medicine and Medicine at Johns Hopkins University. Previously, he held the position of Assistant Professor in Emergency Medicine and Medicine at the Johns Hopkins University School of Medicine. Dr. Martínez's research primarily focuses on the application of artificial intelligence in healthcare. He is dedicated to exploring how this technology can support leaders in the private and public sectors to enhance healthcare quality, affordability, and accessibility. His ongoing research initiatives encompass the following projects:
HEALTHCARE WORKER CAPACITY
INFECTIOUS DISEASES
EQUITY & HEALTH
TEACHING
- Connected Emergency Care Patient Safety Learning Lab (with Scott Levin): Integrate predictive software into electronic health records to minimize harm for ER patients with respiratory tract infections, both health-wise and financially. With the support of the AHRQ.
- Transforming Kidney Care in the Emergency Department using Artificial Intelligence Driven Clinical Decision Support (with Jeremiah Hinson): Novel algorithms to predict acute kidney injury onset in ER patients, informing patient management and medication administration.. With the support of the AHRQ.
- Leveraging Data-Driven Technologies to Minimize Waiting Lists in Resource-Constrained Chilean Health System (with Diana Prieto, Jose Zayas, and Felipe Feijoo): Measuring the impact of extended wait times in universal healthcare systems and creating data-driven systems to manage waitlists. With the support of the AHW.
INFECTIOUS DISEASES
- Multi-level Modeling for Control of Multidrug-resistant Organisms within Healthcare Networks (with Eili Klein): Investigating how infectious diseases spread among healthcare workers and patients through contact networks. Our goal is to develop decision support systems that minimize the risk of hospital-acquired infections. With the support of the CDC.
EQUITY & HEALTH
- Juntos (Together): A community led approach to enhance to Covid-19 testing among vulnerable Latinos (with Kathleen Page): Developing data and systems to monitor and mitigate Covid-19's impact on marginalized groups by improving testing and vaccination efforts. With the support of the NIH.
TEACHING
- EII 301 Probability (at PUCV): An elementary introduction to probability covers fundamental topics such as combinatorics, random variables, probability distributions, Bayesian inference, and Markov chains.
- EII7446 Applied Data Science (at PUCV): This course provides a theoretical and practical exploration of statistical learning, requiring a solid foundation in modest statistics and advanced linear algebra. It incorporates various design challenges to connect theory with real-world applications and features guest presentations by industry leaders. Topics covered include classification, clustering, model evaluation, and user-centered design.
Disclaimer: Any opinions, findings, conclusions, or recommendations expressed herein are those of the author and do not necessarily reflect the views of Johns Hopkins University or any other organization supporting our research.