Implementing complex task allocation in a cytology lab via HCCM using Flexsim HC

Pongjetanapong, Kanokporn; O'Sullivan, Michael J.; Walker, Cameron; Furian, Nikolaus

doi:10.1016/j.simpat.2018.05.007

Cited by 14 publications

(14 citation statements)

References 10 publications

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“…Pongjetanapong et al describes in detail the conceptual model for the cytology laboratory, following the HCCM framework proposed by Furian et al This model is summarized in the following subsections.…”

Section: Methodsmentioning

confidence: 99%

“…We use our conceptual model to develop a simulation of the Cytology Department using FlexSim Health care (FlexSim HC, version 4.3.10). For technical details of the model implementation, including a detailed conceptual model, data collection, model implementation, verification, and validation, see the study of Pongjetanapong et al We present here a brief overview of the main issues with implementing a conceptual model that follows the HCCM framework in FlexSim Health care (FlexSim HC). FlexSim HC is an object‐oriented DES tool, designed for health care applications .…”

Section: Model Implementationmentioning

confidence: 99%

“…The data required to drive our simulation model is obtained from the cytology department, where available, or from RCPath's guidelines. 6 The data used for the simulation model are summarized here: 15 We present here a brief overview of the main issues with implementing a conceptual model that follows the HCCM framework in FlexSim Health care (FlexSim HC). FlexSim HC is an object-oriented DES tool, designed for health care applications.…”

Section: Model Datamentioning

confidence: 99%

“…With the development of the hierarchical control conceptual modeling (HCCM) framework, the complexity of a pathologist's workflow is more easily captured. The technical details of the DES model used here are the subject of a separate paper, which focuses on demonstrating the implementation of the HCCM framework via an off‐the‐shelf health care simulation package . The purpose of this paper is to advocate for the use of DES when evaluating whether resourcing decisions will enable pathology labs, and other healthcare departments, to meet performance targets.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Exploring trade‐offs between staffing levels and turnaround time in a pathology laboratory using discrete event simulation

Pongjetanapong

Walker

O'Sullivan

et al. 2019

Health Planning & Management

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Summary Many health delivery services have required performance targets. Typically, these targets are presented as percentiles of patients to be seen within specified timeframes. These targets present hospital administrators with a resourcing problem complicated by conflicting objectives: How to minimize costs while maximizing throughput to achieve the performance targets? In this paper, we describe the use of a simulation model to evaluate the effect of changes to staff levels in a cytology department, investigating the trade‐off between staff levels and turnaround times in light of performance targets specified by government. Standard practice for determining staffing levels in a cytology department uses average workload estimates and does not take into account target performance measures, task variability, and the interruptive nature of the workload of pathologists. We develop a simulation model for pathologist workload within a cytology department in New Zealand. We describe the model construction process that follows the hierarchical control conceptual modeling (HCCM) framework. We use the resulting simulation model to examine the trade‐offs between staffing levels (and associated rosters) and task turnaround time. The results indicate that consideration of variation in task arrivals is important when considering the effect of staffing levels on turnaround time. Furthermore, as the cytology department is required to meet performance targets that involve maximum service times for a percentile of patients, such an approach is necessary in order to estimate the performance level of a staffing roster.

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Section: Methodsmentioning

confidence: 99%

Section: Model Implementationmentioning

confidence: 99%

Section: Model Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Exploring trade‐offs between staffing levels and turnaround time in a pathology laboratory using discrete event simulation

Pongjetanapong

Walker

O'Sullivan

et al. 2019

Health Planning & Management

Self Cite

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show abstract

“…According to Pawlewski (2018) and Jones et al 2015, simulation has been done in the healthcare scope since the 2000s. Although it is not easy to model a healthcare system (Pongjetanapong et al, 2018), the simulation method still becomes an effective method because of the simulation of healthcare resulting in the optimum value of each element in the system (Persson, 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

Queue analysis of public healthcare system to reduce waiting time using flexsim 6.0

Amalia

Cahyati

2020

Int. J. Ind. Optim.

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Public healthcare is a health service facility from the government at a low cost. The problem is the long queue, which makes long patients’ waiting times. The patients are waiting for a maximum of more than 3 hours in the general polyclinic. Besides, the registration counter is almost busy all the time. The utilization is about 96.96%. Therefore, the objective of this research is to reduce the patients’ waiting time using the simulation method. Flexsim 6.0 software is employed to develop the public healthcare system and also develop some alternatives to improve the problem. The simulation model has been verified and validated. The result shows the waiting time is decreased by more than 80% by adding the resource in the registration counter. For managerial insight, this research could help the public healthcare system in satisfying the patients.

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A Study on Modeling and Simulation of Automobile Painting Process Based on Flexsim

Reyes

Aldas

Castelo

et al. 2019

Advances and Applications in Computer Science, Electronics and Industrial Engineering

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Implementing complex task allocation in a cytology lab via HCCM using Flexsim HC

Cited by 14 publications

References 10 publications

Exploring trade‐offs between staffing levels and turnaround time in a pathology laboratory using discrete event simulation

Exploring trade‐offs between staffing levels and turnaround time in a pathology laboratory using discrete event simulation

Queue analysis of public healthcare system to reduce waiting time using flexsim 6.0

A Study on Modeling and Simulation of Automobile Painting Process Based on Flexsim

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