A multi-method scheduling framework for medical staff

Wael Rashwan, Amr Arisha, John Fowler

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Hospital planning teams are always concerned with optimizing staffing and scheduling decisions in order to improve hospital performance, patient experience, and staff satisfaction. A multi-method approach including data analytics, modeling and simulation, machine learning, and optimization is proposed to provide a framework for smart and applicable solutions for staffing and shift scheduling. Factors regarding patients, staff, and hospitals are considered in the decision. This framework is piloted using the Emergency Department(ED) of a leading university hospital in Dublin. The optimized base staffing patterns and shift schedules actively contributed to solving ED overcrowding problem and reduced the average waiting time for patients by 43% compared to the current waiting time of discharged patients. The reduction was achieved by optimizing the staffing level and then determining the shift schedule that minimized the understaffing and overstaffing of the personnel need to meet patient demand.

Original languageEnglish (US)
Title of host publicationWSC 2018 - 2018 Winter Simulation Conference
Subtitle of host publicationSimulation for a Noble Cause
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1464-1475
Number of pages12
ISBN (Electronic)9781538665725
DOIs
StatePublished - Jan 31 2019
Externally publishedYes
Event2018 Winter Simulation Conference, WSC 2018 - Gothenburg, Sweden
Duration: Dec 9 2018Dec 12 2018

Publication series

NameProceedings - Winter Simulation Conference
Volume2018-December
ISSN (Print)0891-7736

Conference

Conference2018 Winter Simulation Conference, WSC 2018
CountrySweden
CityGothenburg
Period12/9/1812/12/18

Fingerprint

Scheduling
Waiting Time
Emergency
Schedule
Modeling and Simulation
Learning systems
Machine Learning
Planning
Personnel
Framework
Optimization

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Science Applications

Cite this

Rashwan, W., Arisha, A., & Fowler, J. (2019). A multi-method scheduling framework for medical staff. In WSC 2018 - 2018 Winter Simulation Conference: Simulation for a Noble Cause (pp. 1464-1475). [8632247] (Proceedings - Winter Simulation Conference; Vol. 2018-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WSC.2018.8632247

A multi-method scheduling framework for medical staff. / Rashwan, Wael; Arisha, Amr; Fowler, John.

WSC 2018 - 2018 Winter Simulation Conference: Simulation for a Noble Cause. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1464-1475 8632247 (Proceedings - Winter Simulation Conference; Vol. 2018-December).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Rashwan, W, Arisha, A & Fowler, J 2019, A multi-method scheduling framework for medical staff. in WSC 2018 - 2018 Winter Simulation Conference: Simulation for a Noble Cause., 8632247, Proceedings - Winter Simulation Conference, vol. 2018-December, Institute of Electrical and Electronics Engineers Inc., pp. 1464-1475, 2018 Winter Simulation Conference, WSC 2018, Gothenburg, Sweden, 12/9/18. https://doi.org/10.1109/WSC.2018.8632247
Rashwan W, Arisha A, Fowler J. A multi-method scheduling framework for medical staff. In WSC 2018 - 2018 Winter Simulation Conference: Simulation for a Noble Cause. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1464-1475. 8632247. (Proceedings - Winter Simulation Conference). https://doi.org/10.1109/WSC.2018.8632247
Rashwan, Wael ; Arisha, Amr ; Fowler, John. / A multi-method scheduling framework for medical staff. WSC 2018 - 2018 Winter Simulation Conference: Simulation for a Noble Cause. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1464-1475 (Proceedings - Winter Simulation Conference).
@inproceedings{9e368770fe034544b1d7d151ddb4a7a0,
title = "A multi-method scheduling framework for medical staff",
abstract = "Hospital planning teams are always concerned with optimizing staffing and scheduling decisions in order to improve hospital performance, patient experience, and staff satisfaction. A multi-method approach including data analytics, modeling and simulation, machine learning, and optimization is proposed to provide a framework for smart and applicable solutions for staffing and shift scheduling. Factors regarding patients, staff, and hospitals are considered in the decision. This framework is piloted using the Emergency Department(ED) of a leading university hospital in Dublin. The optimized base staffing patterns and shift schedules actively contributed to solving ED overcrowding problem and reduced the average waiting time for patients by 43{\%} compared to the current waiting time of discharged patients. The reduction was achieved by optimizing the staffing level and then determining the shift schedule that minimized the understaffing and overstaffing of the personnel need to meet patient demand.",
author = "Wael Rashwan and Amr Arisha and John Fowler",
year = "2019",
month = "1",
day = "31",
doi = "10.1109/WSC.2018.8632247",
language = "English (US)",
series = "Proceedings - Winter Simulation Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1464--1475",
booktitle = "WSC 2018 - 2018 Winter Simulation Conference",

}

TY - GEN

T1 - A multi-method scheduling framework for medical staff

AU - Rashwan, Wael

AU - Arisha, Amr

AU - Fowler, John

PY - 2019/1/31

Y1 - 2019/1/31

N2 - Hospital planning teams are always concerned with optimizing staffing and scheduling decisions in order to improve hospital performance, patient experience, and staff satisfaction. A multi-method approach including data analytics, modeling and simulation, machine learning, and optimization is proposed to provide a framework for smart and applicable solutions for staffing and shift scheduling. Factors regarding patients, staff, and hospitals are considered in the decision. This framework is piloted using the Emergency Department(ED) of a leading university hospital in Dublin. The optimized base staffing patterns and shift schedules actively contributed to solving ED overcrowding problem and reduced the average waiting time for patients by 43% compared to the current waiting time of discharged patients. The reduction was achieved by optimizing the staffing level and then determining the shift schedule that minimized the understaffing and overstaffing of the personnel need to meet patient demand.

AB - Hospital planning teams are always concerned with optimizing staffing and scheduling decisions in order to improve hospital performance, patient experience, and staff satisfaction. A multi-method approach including data analytics, modeling and simulation, machine learning, and optimization is proposed to provide a framework for smart and applicable solutions for staffing and shift scheduling. Factors regarding patients, staff, and hospitals are considered in the decision. This framework is piloted using the Emergency Department(ED) of a leading university hospital in Dublin. The optimized base staffing patterns and shift schedules actively contributed to solving ED overcrowding problem and reduced the average waiting time for patients by 43% compared to the current waiting time of discharged patients. The reduction was achieved by optimizing the staffing level and then determining the shift schedule that minimized the understaffing and overstaffing of the personnel need to meet patient demand.

UR - http://www.scopus.com/inward/record.url?scp=85062594569&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85062594569&partnerID=8YFLogxK

U2 - 10.1109/WSC.2018.8632247

DO - 10.1109/WSC.2018.8632247

M3 - Conference contribution

T3 - Proceedings - Winter Simulation Conference

SP - 1464

EP - 1475

BT - WSC 2018 - 2018 Winter Simulation Conference

PB - Institute of Electrical and Electronics Engineers Inc.

ER -