Simulation of the Queston Physician Network

Swisher, James R.; Jacobson, Sheldon H.; Jun, Brian H.; Balcı, Osman

doi:10.1145/268437.268752

Cited by 8 publications

(5 citation statements)

References 8 publications

(5 reference statements)

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“…They tested three different alternatives for scheduling patients for ENT physicians and ended up selecting a policy that reduced the average patient wait time by 45% (Kachhal et al 1981). Swisher et al (1997) showed that under certain conditions staffing reductions could be made without sacrificing patient throughput or increasing staff overtime. They experimented with several models mixing different types of patients (Swisher et al 1997).…”

Section: Related Workmentioning

confidence: 99%

“…Swisher et al (1997) showed that under certain conditions staffing reductions could be made without sacrificing patient throughput or increasing staff overtime. They experimented with several models mixing different types of patients (Swisher et al 1997). They also showed that scheduling more of a certain type of patient (patients that require extensive physician interaction; longer service time) in the morning reduces employee overtime significantly.…”

Section: Related Workmentioning

confidence: 99%

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A detailed simulation model of an infusion treatment center

Huggins

Chipperfield

Waliullah

2014

Proceedings of the Winter Simulation Conference 2014

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Oncology clinics face several complexities in their processes. When patients arrive at the infusion chairs, nurses and pharmacy technicians must be available to get the patients ready for the infusion and mix their drug treatments. This requires having the right information at the right moment. This research develops a detailed discrete event simulation model which considers the interactions between resources, information, and patient flow. The model was used to evaluate different scheduling policies and determine which of them could be incorporated in the clinic with the objective of increasing daily throughput without affecting patient wait time or total time in the system.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A detailed simulation model of an infusion treatment center

Huggins

Chipperfield

Waliullah

2014

Proceedings of the Winter Simulation Conference 2014

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“…Little evidence of implementation was presented. 5.6 Modelling flows between the above departments, and whole system models Jun et al [3] identify multi-facility simulation models conducted by Hancock and Walters [100], Swisher et al [101] and Lowery and Martin [74].…”

Section: Summary-diagnostics Modelsmentioning

confidence: 99%

What is a ‘generic’ hospital model?—a comparison of ‘generic’ and ‘specific’ hospital models of emergency patient flows

Fletcher

Worthington

2009

Health Care Manag Sci

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The paper addresses the question in the title via a survey of experienced healthcare modellers and an extensive literature review. It has two objectives. 1. To compare the characteristics of 'generic' and 'specific' models and their success in hospitals for emergency patients 2. To learn lessons about the design, validation and implementation of models of flows of emergency patients through acute hospitals First the survey and some key papers lead to a proposed 'spectrum of genericity', consisting of four levels. We focus on two of these levels, distinguished from each other by their purpose. Secondly modelling work on the flow of emergency patient flows through and between A&E, Bed Management, Surgery, Intensive Care and Diagnostics is then reviewed. Finally the review is used to provide a much more comprehensive comparison of'generic' and 'specific' models, distinguishing three types of genericity and identifying 24 important features of models and the associated modelling process. Many features are common across model types, but there are also important distinctions, with implications for model development.

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“…Such issues can also affect how hospital resources are optimized to facilitate patient flow and reduce patient waiting time. Several studies have been conducted to show that there is a link between patient scheduling and admission policies and patient waiting time (Fitzpatrick et al , 1993; Klassen and Rohleder, 1996; Swisher et al , 1997; Kim et al , 2014). Another factor in reducing patient’s waiting time is the use of a fast track lane to reduce waiting times for low-priority non-urgent patients.…”

Section: Background and Introductionmentioning

confidence: 99%

From process flows to benchmark in an accident and emergency hospital with PERT approach

Ahmed

Mohammed

2019

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Purpose Accident emergency hospital (AEH) services require cohesive, collective, uninterrupted streamlined medical diagnostic and satisfactory patient care. Medical service efficiency in AEHs is difficult to quantify due to the clinical complexity involved in treatment involving various units, patient conditions, changes in contemporary medical practices and technological developments. This paper aims to show how to measure efficiency by eliminating waste in AEH system, identify service failure points, identify benchmark medical services, identify patient throughput time and measure treatment time when AEH services are nonstandard. The applications shown in this paper are distinct in particular; we the authors use nontraditional and systems engineering approach to collect data as the traditional data collection is difficult in real-time AEHs. Design/methodology/approach The authors show in this study how to measure overall patient treatment time from admission to discharge. Project evaluation and review technique (PERT) captures the inconsistencies involved in measuring treatment time, including measures of variability. The irregular treatment time and complexity involved in the emergency health-care services are usual. The research methodology illustrates how the time function map and service blueprint can improve value-added time in AEHs and benchmark services between similar AEHs. Findings The inconsistency in treatment time between AEH in public and private hospital is found to be in ratio of 1:20. The private hospital suggests variety of treatments and long stays for recovery. The PERT computations show that the average time a patient remains in a government AEH is about 10 days. The standard deviation of the AEH treatment time is about 0.043 per cent of the expected patient care time. The inconsistency is not significant as compared to the expected value. In 89.64 per cent of the cases, a patient may be discharged in less than 10 days’ time. The patient on average is discharged in 13 days in a private hospital. Originality/value The patient treatment time of an AEH is evaluated with PERT project management approach to account for inconsistencies in treatment time. This research makes new contributions in benchmarking AEH throughput time, identify medical service failure points with service blueprint, measure the efficiency with time function map and collect patient data with nontraditional methods. The inherent inconsistencies in a clinical process are identified by PERT analysis with the variance as a characteristic of the treatment time. Improvement of variability implies cost reduction in AEH system.

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Simulation of the Queston Physician Network

Cited by 8 publications

References 8 publications

A detailed simulation model of an infusion treatment center

A detailed simulation model of an infusion treatment center

What is a ‘generic’ hospital model?—a comparison of ‘generic’ and ‘specific’ hospital models of emergency patient flows

From process flows to benchmark in an accident and emergency hospital with PERT approach

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