Haley Hunter-Zinck, PhD

Haley Hunter-Zinck

Dr. Hunter-Zinck earned a Ph.D. in Computational Biology from Cornell University in 2014. She completed a postdoc in medical informatics at the VA Boston Healthcare System and continued at VA Boston between 2017 to 2019 as a researcher focusing on applying machine learning to emergency department and hospital flow problems.


PROJECTS 

Synthetic Data: Healthcare systems restrict clinical data access for privacy concerns. However, restrictive data access slows the development of novel artificial intelligence tools in healthcare. Creating synthetic clinical datasets that could be shared would facilitate collaborations with research groups and other partners to develop new methodology, validate existing tools, or compare patient population characteristics. This project involves adapting and combining generative models (e.g. generative adversarial networks, generative recurrent neural networks) to create synthetic patient datasets including structured electronic health record data, clinical notes, images, and sequential data from mobile health devices. Synthetic data validation will occur in the context of predictive models for hospital flow problems and patient privacy preservation.

Predicting Emergency Department staff shortages during the COVID-19 pandemic using machine learning: Front-line healthcare workers are at high risk of contracting the novel coronavirus. The presence of a sufficient number of healthcare workers at all times during this pandemic is essential for the health of the rest of society. The goal of our study is to develop a machine learning model to predict staff shortages based on healthcare workers’ risk of coronavirus infection. We are partnering with Emergency Department (ED) providers at the University of California, San Francisco (UCSF), who report an immediate need for staff forecasting to help guide personnel resource management. We will combine several sources of data, including patient electronic health records, staff scheduling, personal protective equipment inventory, and a custom-designed questionnaire to quantify healthcare workers’ personal coronavirus risk factors. Our study will help the UCSF ED proactively manage essential staff resources, which has important implications for emergency preparedness, hospital operations, and the survival of patients.


 

Haley Hunter-Zinck, PhD

UC Berkeley, UCSF

Dr. Hunter-Zinck earned a Ph.D. in Computational Biology from Cornell University in 2014. She completed a postdoc in medical informatics at the VA Boston Healthcare System and continued at VA Boston between 2017 to 2019 as a researcher focusing on applying machine learning to emergency department and hospital flow problems.


PROJECTS 

Synthetic Data: Healthcare systems restrict clinical data access for privacy concerns. However, restrictive data access slows the development of novel artificial intelligence tools in healthcare. Creating synthetic clinical datasets that could be shared would facilitate collaborations with research groups and other partners to develop new methodology, validate existing tools, or compare patient population characteristics. This project involves adapting and combining generative models (e.g. generative adversarial networks, generative recurrent neural networks) to create synthetic patient datasets including structured electronic health record data, clinical notes, images, and sequential data from mobile health devices. Synthetic data validation will occur in the context of predictive models for hospital flow problems and patient privacy preservation.

Predicting Emergency Department staff shortages during the COVID-19 pandemic using machine learning: Front-line healthcare workers are at high risk of contracting the novel coronavirus. The presence of a sufficient number of healthcare workers at all times during this pandemic is essential for the health of the rest of society. The goal of our study is to develop a machine learning model to predict staff shortages based on healthcare workers’ risk of coronavirus infection. We are partnering with Emergency Department (ED) providers at the University of California, San Francisco (UCSF), who report an immediate need for staff forecasting to help guide personnel resource management. We will combine several sources of data, including patient electronic health records, staff scheduling, personal protective equipment inventory, and a custom-designed questionnaire to quantify healthcare workers’ personal coronavirus risk factors. Our study will help the UCSF ED proactively manage essential staff resources, which has important implications for emergency preparedness, hospital operations, and the survival of patients.


 


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