TY - GEN
T1 - A multi-method scheduling framework for medical staff
AU - Rashwan, Wael
AU - Arisha, Amr
AU - Fowler, John
N1 - Publisher Copyright:
© 2018 IEEE
PY - 2018/7/2
Y1 - 2018/7/2
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.
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U2 - 10.1109/WSC.2018.8632247
DO - 10.1109/WSC.2018.8632247
M3 - Conference contribution
AN - SCOPUS:85062594569
T3 - Proceedings - Winter Simulation Conference
SP - 1464
EP - 1475
BT - WSC 2018 - 2018 Winter Simulation Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 Winter Simulation Conference, WSC 2018
Y2 - 9 December 2018 through 12 December 2018
ER -