Modeling Patient Flows Through the Health care System

Hall, Randolph W.; Belson, David; Murali, Pavan; Dessouky, Maged

doi:10.1007/978-1-4614-9512-3_1

Cited by 31 publications

(22 citation statements)

References 9 publications

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“…The classification of studies on applying queuing theory to healthcare service can be based upon waiting time and utilization analysis (Yeo et al, 2014), which can be further classified into those on reneging (Broyles and Cochran, 2007), variable arrival rate (Worthington, 1987), priority queuing discipline (Fiems et al, 2005) and blocking (Koizumi et al, 2005); whereas studies on system design with respect to queuing (Green et al, 2006;Park and Kwag, 2009) are classified into cost minimization (Gorunescu et al, 2002) and blocking. Based on size of the system (Hall et al, 2013), studies have been conducted at department-level which includes Departments of Internal Medicine (Hwang, 2006), Orthopaedics (Yeo et al, 2014), Emergency Room (Kim et al, 2009;Mandelbaum et al, 2012), Radiology (Park and Kwag, 2009) and MRI (Green and Savin, 2008); while those conducted at healthcare center-level had the whole outpatient department (Park, 2001;Ko, 2010;Kim et al, 2008).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The studies on models of queuing theory are provided by Green (2006a) and analysis of the effect of waiting times on patients in the Emergency Department is given by (Siddhartan et al, 1996). The flow of patients in a queue can lead to good patient flow if the queuing is minimized whereas queuing delays can lead to patient suffering causing poor patient flow (Hall et al, 2006). The demand for health care services can be determined by effective resource allocation and capacity planning (Murray, 2000).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Application of queuing theory for analysis and modeling of processes that involves waiting lines is used widely in industries for optimizing the supply of fixed resources at variable demand conditions, however, the healthcare industry views itself differently from other industry (Kritchanchai, 2012). The hospital operations managers should be aware of the status of business processes to improve operational performances and reduce the waiting time (Hall et al, 2006). There are several analytical tools for understanding the complexity of a system's performance, among which the queuing theory is a tool to analyze systems which include queues and consist of clients, servers, and queues (Kay et al, 1996;Van Wyk and Walubo, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Application of Queuing Theory to Optimize Waiting-Time in Hospital Operations

Yaduvanshi

Sharma

2019

OSCM: An Int. Journal

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Waiting time is inherent to the healthcare service sector in India and a major challenge faced by almost every big hospital is queuing. Long waiting time can be a reflection of inefficiency in hospital operations. The outpatient department (OPD) has the biggest queue as compared to other departments in hospital operations. This study comprises of in-depth analysis of OPD from different dimensions. Like in many big hospitals across India, the OPD of Fortis Escorts Hospital in Jaipur, India is managed using experience and rule of thumb rather than strategic research-based techniques such as queuing theory. The Fortis Escorts Hospital in Jaipur receives a large number of patients each day which results in longer waiting time for patients due to long queues. To address this challenge, a SWOT analysis was conducted for the OPD of Fortis Escorts Hospital Jaipur (FEHJ) which resulted into dissecting the queuing problem and coming out with solutions knowing where the hospital operations can excel and where there is a scope of improvement to make the working and processes better. Additionally, after examining the problem analytically and applying queuing theory, measures were suggested to improve the delay points and make the OPD more efficient in order to gain a high patient satisfaction rating.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of Queuing Theory to Optimize Waiting-Time in Hospital Operations

Yaduvanshi

Sharma

2019

OSCM: An Int. Journal

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“…By completing the treatment time in ED, the patient can be admitted to another unit where they may encounter more processes and delays. Finally, the beds are prepared for the next patient after the discharge process (Hall et al 2013), where the patients are discharged from the ED.…”

Section: Introductionmentioning

confidence: 99%

Optimising the service of emergency department in a hospital

Allihaibi

Masoud

Cholette

et al. 2017

Syme, G., Hatton MacDonald, D., Fulton, B. And Piantadosi, J. (Eds) MODSIM2017, 22nd International Congress on Modelling and Si

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Nowadays, the rapidly increasing healthcare cost has become a serious problem because of inefficient usage of medical resources. The Emergency Department (ED) plays a vital role in the hospital system and has critical effects on the overall efficiency in a hospital. The ED deals with patient's arrival, triage, physician assessment, imaging and laboratory studies, treatment planning, nursing procedures, decisions to discharge or admit access to inpatient beds and physicians. These activities generally occur in a sequential manner and the delayed activities of the patient flow can cause bottlenecking and reduce the service level. Optimising the service of the ED is challenging because the arrival times of patients are dynamic and their expected treatment times are volatile. This paper develops a new ED optimisation model using stochastic mathematical programming approach under limited budget and resource capacity. The objectives of the proposed model are for increasing the system efficiency, serving more patients in specific time, or providing the same quality of the service with the use of less medical resources. A numerical investigation is presented and demonstrates that high-quality solutions are obtainable for industry-scale applications in a reasonable time. Computational experiments have been conducted using CPLEX and ExtendSim to solve the ED-Stochastic Optimisation Mixed Integer Programming model and ED-Simulation model sequentially. Real data for Royal Brisbane and Women's Hospital (RBWH) is used in this paper to validate the proposed solution approach.

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“…[6] One of the major fundamentals in improving efficiency in the delivery of health care services is patient flow. Meaning of Good patient flow is that patient queuing is minimized and improper patient flow means that patients suffer considerable queuing delays [7] . Effective resource allocation and capacity planning are determined by patient flow because it informs the demand for health care services [8] .…”

Section: Literaturementioning

confidence: 99%