Automating Patient Triage in Mayo Clinic's Multidisciplinary Dizziness Practice
Automating Patient Triage in Mayo Clinic's Multidisciplinary Dizziness Practice
Presented by:
Santiago Romero-Brufau, MD, PhD
Assistant Professor of ENT and Healthcare Systems Engineering, Mayo Clinic; Adjunct Assistant Professor, Department of Biostatistics, Harvard T.H. Chan School of Public Health
Abstract: Unnecessary medical appointments are a source of dissatisfaction for both patients and medical providers and add waste to the healthcare system. Determining the medical appointments that are necessary for a medical condition (patient triage) can be a time-consuming and workload-intensive task. In this presentation, Dr. Romero-Brufau will describe how the problem of patient triage was partially automated using a clinical informatics approach, resulting in a 75% reduction in the triage workload. This also serves as a use case for how to implement AI solutions into the clinical workflow. He will discuss model selection, data acquisition and workflow integration.
About the Speaker: Dr. Romero-Brufau, MD, PhD is a classically trained and non-practicing physician, and practicing data scientist and clinical informaticist. He is Assistant Professor of ENT and Healthcare Systems Engineering at Mayo Clinic, where he is the AI lead for the Department of Otolaryngology and Head/Neck Surgery (ENT). He is also Adjunct Assistant Professor of Biostatistics at the Harvard T.H. Chan School of Public Health, where he teaches in the Health Data Science Program. His work focuses on the development and implementation of data science and machine-learning solutions into the clinical workflow.
Thursday, January 26, 2023 12:00 pm – 1:00 pm (CST)
Originally recorded on Thursday, January 26, 2022, as part of CHOIR's
Automating Patient Triage in Mayo Clinic's Multidisciplinary Dizziness Practice
Presented by:
Santiago Romero-Brufau, MD, PhD
Assistant Professor of ENT and Healthcare Systems Engineering, Mayo Clinic; Adjunct Assistant Professor, Department of Biostatistics, Harvard T.H. Chan School of Public Health
Abstract: Unnecessary medical appointments are a source of dissatisfaction for both patients and medical providers and add waste to the healthcare system. Determining the medical appointments that are necessary for a medical condition (patient triage) can be a time-consuming and workload-intensive task. In this presentation, Dr. Romero-Brufau will describe how the problem of patient triage was partially automated using a clinical informatics approach, resulting in a 75% reduction in the triage workload. This also serves as a use case for how to implement AI solutions into the clinical workflow. He will discuss model selection, data acquisition and workflow integration.
About the Speaker: Dr. Romero-Brufau, MD, PhD is a classically trained and non-practicing physician, and practicing data scientist and clinical informaticist. He is Assistant Professor of ENT and Healthcare Systems Engineering at Mayo Clinic, where he is the AI lead for the Department of Otolaryngology and Head/Neck Surgery (ENT). He is also Adjunct Assistant Professor of Biostatistics at the Harvard T.H. Chan School of Public Health, where he teaches in the Health Data Science Program. His work focuses on the development and implementation of data science and machine-learning solutions into the clinical workflow.
Thursday, January 26, 2023 12:00 pm – 1:00 pm (CST)