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Authors: James P. McGlothlin 1 ; Sriveni Vedire 1 ; Hari Srinivasan 1 ; Amar Madugula 1 ; Srinivasan Rajagopalan 1 and Latifur Khan 2

Affiliations: 1 Fusion Consulting Inc, United States ; 2 University of Texas at Dallas, United States

ISBN: 978-989-758-281-3

Keyword(s): Predictive Analytics, Data Warehousing, Patient Movement, Discrete Event Simulation.

Abstract: Hospitals and healthcare systems are challenged to service the growing healthcare needs of the population with limited resources and tightly restrained finances. The best healthcare organizations constantly seek performance improvement by adjusting both resources and processes. However, there are endless options and possibilities for how to invest and adapt, and it is a formidable challenge to choose the right ones. The challenge is that each potential change can have far reaching effects. This challenge is exacerbated even further because it can be very expensive for a hospital to experience logjams in patient movement. Each and every change has a “ripple” effect across the system and traditional analytics cannot calculate all the ramifications and opportunities associated with such changes. This project uses historical records of patient treatment plans in combination with a virtual discrete event simulation model to evaluate and predict capacity and efficiency when resources a re added, reduced or reallocated. The model assigns assets as needed to execute the treatment plan, and calculates resulting volumes, length of stay, wait times, cost. This provides a valuable resource to operations management and allows the hospital to invest and allocate resources in ways that maximize financial benefit and quality of patient care. (More)

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Paper citation in several formats:
McGlothlin, J.; Vedire, S.; Srinivasan, H.; Madugula, A.; Rajagopalan, S. and Khan, L. (2018). Predicting Hospital Capacity and Efficiency.In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, ISBN 978-989-758-281-3, pages 562-570. DOI: 10.5220/0006658905620570

@conference{healthinf18,
author={James P. McGlothlin. and Sriveni Vedire. and Hari Srinivasan. and Amar Madugula. and Srinivasan Rajagopalan. and Latifur Khan.},
title={Predicting Hospital Capacity and Efficiency},
booktitle={Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF,},
year={2018},
pages={562-570},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006658905620570},
isbn={978-989-758-281-3},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF,
TI - Predicting Hospital Capacity and Efficiency
SN - 978-989-758-281-3
AU - McGlothlin, J.
AU - Vedire, S.
AU - Srinivasan, H.
AU - Madugula, A.
AU - Rajagopalan, S.
AU - Khan, L.
PY - 2018
SP - 562
EP - 570
DO - 10.5220/0006658905620570

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