loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

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

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

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Business Analytics ; Cardiovascular Technologies ; Computing and Telecommunications in Cardiology ; Data Engineering ; Data Mining ; Databases and Datawarehousing ; Databases and Information Systems Integration ; Decision Support Systems ; Decision Support Systems, Remote Data Analysis ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Health Information Systems ; Knowledge-Based Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems

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 are 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)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 54.87.17.177

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

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 (BIOSTEC 2018) - HEALTHINF; ISBN 978-989-758-281-3; ISSN 2184-4305, SciTePress, 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 (BIOSTEC 2018) - HEALTHINF},
year={2018},
pages={562-570},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006658905620570},
isbn={978-989-758-281-3},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - HEALTHINF
TI - Predicting Hospital Capacity and Efficiency
SN - 978-989-758-281-3
IS - 2184-4305
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
PB - SciTePress