REMA PADMAN
The H. John Heinz III College of Information Systems and Public Policy, Carnegie Mellon University
Healthcare Informatics and Analytics
The significant advances in clinical and consumer health technologies combined with the rapid developments in advanced analytics of high dimensional, high volume, and complex healthcare data is powering a transformation of healthcare delivery worldwide. Innovative analytical techniques are being developed to support a range of decisions that include predicting responses to different treatment regimens, individual and population level risk assessments, detecting anomalies, and preventing deterioration in the health status of the patient. Supporting patient – provider communication and shared decision making via intelligent reminders, notifications and informed guidance, and providing smart healthcare delivery operations to increase satisfaction, efficiency and quality of care are further capabilities being architected using informatics tools. Learning current and potential best practices from data using quantitative methodologies, such as statistical machine learning and operations research, and translating the new evidence to the frontlines of care via efficient software implementations and institutional deployments, offer both major challenges and opportunities for researchers and practitioners alike. This tutorial will highlight some of the methods and tools to address these issues with illustrative examples drawn from clinical, consumer self-care and public health domains.
The significant advances in clinical and consumer health technologies combined with the rapid developments in advanced analytics of high dimensional, high volume, and complex healthcare data is powering a transformation of healthcare delivery worldwide. Innovative analytical techniques are being developed to support a range of decisions that include predicting responses to different treatment regimens, individual and population level risk assessments, detecting anomalies, and preventing deterioration in the health status of the patient. Supporting patient – provider communication and shared decision making via intelligent reminders, notifications and informed guidance, and providing smart healthcare delivery operations to increase satisfaction, efficiency and quality of care are further capabilities being architected using informatics tools. Learning current and potential best practices from data using quantitative methodologies, such as statistical machine learning and operations research, and translating the new evidence to the frontlines of care via efficient software implementations and institutional deployments, offer both major challenges and opportunities for researchers and practitioners alike. This tutorial will highlight some of the methods and tools to address these issues with illustrative examples drawn from clinical, consumer self-care and public health domains.