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A multi-method scheduling framework for medical staff

Published: 09 December 2018 Publication History

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.

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  • (2020)Big Data Applications in Healthcare AdministrationInternational Journal of Big Data and Analytics in Healthcare10.4018/IJBDAH.20200701025:2(12-37)Online publication date: 1-Jul-2020
  1. A multi-method scheduling framework for medical staff

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    cover image ACM Conferences
    WSC '18: Proceedings of the 2018 Winter Simulation Conference
    December 2018
    4298 pages
    ISBN:978153866570

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    IEEE Press

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    Published: 09 December 2018

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    WSC '18: Winter Simulation Conference
    December 9 - 12, 2018
    Gothenburg, Sweden

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    WSC '18 Paper Acceptance Rate 183 of 260 submissions, 70%;
    Overall Acceptance Rate 3,413 of 5,075 submissions, 67%

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    • (2020)Big Data Applications in Healthcare AdministrationInternational Journal of Big Data and Analytics in Healthcare10.4018/IJBDAH.20200701025:2(12-37)Online publication date: 1-Jul-2020

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