Modeling and Informatics Advances in Medicine and HealthCare -- basic research, clinical decision making, and efficient delivery

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Title: Modeling and Informatics Advances in Medicine and HealthCare -- basic research, clinical decision making, and efficient delivery
Author: Lee, Eva K.
Abstract: We present systems approaches for engineering medical and the healthcare delivery processes. Part 1 of the talk focuses on medical care processes. Specifically, we discuss our innovative technological advances in health and (bio)medical informatics that assist in health risk prediction, early disease detection and intervention, diagnosis, drug discovery, optimal treatment design, and outcome prediction. For health risk prediction and early disease diagnosis, three applications are described: a) identifying the cognitive status of individuals and early detection of Alzheimer’s Disease, b) predicting metabolite concentrations in humans. c) genomic analysis and early detection of human cancer. For medical treatment design, we include three areas: a) optimal cancer treatment design, b) predicting the immunity effectiveness of a vaccine, and c) systems modeling of the entire disease diagnosis and treatment timeline that allows clinicians the capability to understand the clinical care flow and identify potential deficiencies and opportunities for improvement during the care process. Part II focuses on advances in healthcare delivery. We provide examples of some of our ongoing projects: a) Optimal care delivery model which focuses on improving capability and efficiency, decreasing practice variability, and enhancing quality of care. b) Medication error reduction to capture process flows and reduce high-alert errors occurring during the administering process. c) Optimizing EMR usage "beyond adoption" which focuses on alert management. Part III focuses on public health and population health monitoring. We focus on city readiness and emergency response for biodefense and infectious disease outbreaks. The challenge here involves multi-level strategic and operational planning of the public health infrastructure. Protection of a regional population involves large-scale dispensing of prophylaxic medication that presents many challenges (strategic stockpiles, medical supply distribution, locations of dispensing facilities, optimal facility staffing and resource allocation, routing of the population, and various logistics, transportation, and dispensing modalities). We describe our effort developing RealOpt© -- a modeling and enterprise decision support system for operations planning and logistics, clinic design, resource allocation, time-motion study, disease propagation analysis, and real-time decision-making for large-scale public health and medical preparedness.
Description: Eva K. Lee is an Associate professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology, and Director of the Center for Operations Research in Medicine and HealthCare.
Type: Presentation
Video
URI: http://hdl.handle.net/1853/31256
Date: 2009-06-05
Contributor: Georgia Institute of Technology. School of Industrial and Systems Engineering
Georgia Institute of Technology. Center for Operations Research in Medicine and HealthCare
Publisher: Georgia Institute of Technology
Subject: Systems modeling
Information technology
Machine learning
Health risk prediction
Early disease detection
Optimal treatment design and drug delivery
Outcome prediction
Public health and medical preparedness
Large-scale informatics
Clinical decision support
Efficient delivery

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