A Framework for the Modeling and Simulation of Health Care Systems

Augusto, Vincent; Xie, Xiaolan; Grimaud, Frédéric

doi:10.1109/coase.2007.4341698

Cited by 12 publications

(13 citation statements)

References 6 publications

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“…After implementation of suggested improvements authors found significant decrease in waiting times, rise in efficiency, and decrease of overtime hours. [2] proposed a flexible framework for modeling and simulation of inpatient health care. Authors offered a methodology which accommodates three types of view on health care system: process, resource, and organization.…”

Section: Literature Reviewmentioning

confidence: 99%

Health Care Provider Processes Analysis

Kawczynski

Taisch

2010

IFIP Advances in Information and Communication Technology

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Abstract. In every society there is a need for an efficient health care system. This is a case study paper that summarizes process analysis performed at a US provider clinic. This paper provides an analysis of factors (arrival accuracy and no shows) influencing main processes within the clinic. The numerical relations between influencing factors and key processes are exhibited. Moreover, the abilities of a health care provider to deal with variations of arrival time are exhibited. The predicted probabilities for arrival accuracy and no shows are discussed. The paper provides an interesting statistical approach to analyze operations of a health care provider, which can be beneficial for stakeholders of the clinic.

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Section: Literature Reviewmentioning

confidence: 99%

Health Care Provider Processes Analysis

Kawczynski

Taisch

2010

IFIP Advances in Information and Communication Technology

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“…Finally, discrete-event simulation (DES) has been widely used in the literature to model and assess the performance of health-care systems (Jun, Jacobson, and Swisher 1999;Augusto and Xie 2006;Augusto and Xie 2014). However, except in Pehlivan, Augusto, and Xie (2013), DES has not been used jointly with MDP analysis to assess the validity of theoretical models under many assumptions.…”

Section: Prodel Augusto and Xiementioning

confidence: 98%

Hospitalization admission control of emergency patients using Markovian Decision Processes and discrete event simulation

Prodel

Augusto

Xie

2014

Proceedings of the Winter Simulation Conference 2014

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This paper addresses the hospitalization admission control policies of patients from an emergency department that should be admitted shortly or transferred. When an emergency patient arrives, depending on his/her health condition, a physician may decide to hospitalize him/her in a specific department. Patient admission depends on the availability of beds, the length of stay (LOS) and the reward of hospitalization which are both patient-class specific. The problem consists in determining patient admission policies in order to maximize the overall gain. We first propose a Markov Decision Process (MDP) Model for determination of the optimal patient admission policy under some restrictive and necessary assumptions such as exponentially distributed LOS. A simulation model is then built to assess MDP admission policies under realistic conditions. We show that MDP policies significantly improve the overall gain for different types of facilities.

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“…This paper recognizes that patients with different acuities have different needs, addressed by different step sequences, and using many kinds of resources, all of whose schedules affect lag times. Short-term planning and scheduling using Petri nets and DES [1] similarly lacks the intricate details of the healthcare process our models capture, such as work shifts, patient handoffs. Similarly, while UML modeling with discrete-timed Petri nets can guide optimization to determine the minimum required resources, and simulation can then evaluate EDs [11], unlike our approach, this method does not consider the complex constraints and policies of resource utilization.…”

Section: Related Workmentioning

confidence: 99%

Discrete-Event Simulation and Integer Linear Programming for Constraint-Aware Resource Scheduling

Shin

Brun

Balasubramanian

et al. 2018

IEEE Trans. Syst. Man Cybern, Syst.

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This paper presents a method for scheduling resources in complex systems that integrate humans with diverse hardware and software components, and for studying the impact of resource schedules on system characteristics. The method uses discrete-event simulation and integer linear programming, and relies on detailed models of the system's processes, specifications of the capabilities of the system's resources, and constraints on the operations of the system and its resources. As a case study, we examine processes involved in the operation of a hospital emergency department, studying the impact staffing policies have on such key quality measures as patient length of stay (LoS), number of handoffs, staff utilization levels, and cost. Our results suggest that physician and nurse utilization levels for clinical tasks of 70% result in a good balance between LoS and cost. Allowing shift lengths to vary and shifts to overlap increases scheduling flexibility. Clinical experts provided face validation of our results. Our approach improves on the state of the art by enabling using detailed resource and constraint specifications effectively to support analysis and decision making about complex processes in domains that currently rely largely on trial and error and other ad hoc methods.

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A Framework for the Modeling and Simulation of Health Care Systems

Cited by 12 publications

References 6 publications

Health Care Provider Processes Analysis

Health Care Provider Processes Analysis

Hospitalization admission control of emergency patients using Markovian Decision Processes and discrete event simulation

Discrete-Event Simulation and Integer Linear Programming for Constraint-Aware Resource Scheduling

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