TY - GEN
T1 - High-performance computing framework for predictive simulation of healthcare delivery innovation
AU - Park, Byung H.
AU - Ozmen, Ozgur
AU - Weigand, Gil
AU - Shankar, Mallikarjun
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/4/18
Y1 - 2016/4/18
N2 - In a complex healthcare delivery environment there are unforeseen risks and uncertainties that may unfold when new policies are applied. In silico predictive simulation approaches allow exploration of potential responses of a system to new policy and rule implementations. The validity of such computational models comes into question unless they operate with realistic representations which require significant modeling detail over a large-scale, and with high accuracy. This necessitates a large amount of computing capacity and data management. To address these needs we propose a high-performance computing (HPC) agent-based framework for healthcare system predictive simulations. The framework is designed to emulate a healthcare system modeled at high fidelity and with high resolution data, evaluate its performance in response to different user defined policies, and find polices that maximize outcome measures and system efficiency. The paper details our data preparation procedures, and describes how the framework is implemented and run on a supercomputer to model a healthcare system at an appropriately large scale.
AB - In a complex healthcare delivery environment there are unforeseen risks and uncertainties that may unfold when new policies are applied. In silico predictive simulation approaches allow exploration of potential responses of a system to new policy and rule implementations. The validity of such computational models comes into question unless they operate with realistic representations which require significant modeling detail over a large-scale, and with high accuracy. This necessitates a large amount of computing capacity and data management. To address these needs we propose a high-performance computing (HPC) agent-based framework for healthcare system predictive simulations. The framework is designed to emulate a healthcare system modeled at high fidelity and with high resolution data, evaluate its performance in response to different user defined policies, and find polices that maximize outcome measures and system efficiency. The paper details our data preparation procedures, and describes how the framework is implemented and run on a supercomputer to model a healthcare system at an appropriately large scale.
UR - http://www.scopus.com/inward/record.url?scp=84968548069&partnerID=8YFLogxK
U2 - 10.1109/BHI.2016.7455948
DO - 10.1109/BHI.2016.7455948
M3 - Conference contribution
AN - SCOPUS:84968548069
T3 - 3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016
SP - 517
EP - 520
BT - 3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016
Y2 - 24 February 2016 through 27 February 2016
ER -