Queueing for Healthcare

Palvannan, R. Kannapiran; Teow, Kiok Liang

doi:10.1007/s10916-010-9499-7

Cited by 59 publications

(40 citation statements)

References 10 publications

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“…12 By tracking each change, the simulation represents the activities of the clinic. Prior models of this type have assumed that processing times are either exponentially or normally distributed to be consistent with basic queuing models and/or to be easily understood by practitioners.…”

Section: Study Methodsmentioning

confidence: 99%

Using Process Analysis to Assess the Impact of Medical Education on the Delivery of Pain Services

et al. 2012

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Background:The medical, social, and economic effects of the teaching mission on delivery of care at an academic medical center (AMC) are not fully understood. When a freestanding private practice ambulatory clinic with no teaching mission was merged into an AMC, a natural experiment was created. The authors compared process measures across the two settings to observe the differences in system performance introduced by the added steps and resources of the AMC's teaching mission. Methods: After creating process maps based on activity times realized in both settings, the authors developed discrete-event simulations of the two environments. The two settings were comparable in the levels of key resources, but the AMC process flow included three residents/fellows. Simulation enabled the authors to consider an identical schedule across the two settings. Results: Under identical schedules, the average accumulated processing time per patient was higher in the AMC. However, the use of residents allowed simultaneous processing of multiple patients. Consequently, the AMC had higher

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Section: Study Methodsmentioning

confidence: 99%

Using Process Analysis to Assess the Impact of Medical Education on the Delivery of Pain Services

et al. 2012

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“…The most common industrial and systems engineering techniques for modeling and evaluating inpatient bed capacity are discrete event simulation and queueing theory [10][11][12][13][14][15]. Previous studies that applied discrete event simulation to health care systems focused on individual units such as the emergency department or intensive care unit [16][17][18][19].…”

Section: Previous Approaches To Bed Capacity Decisionsmentioning

confidence: 99%

StratBAM: A Discrete-Event Simulation Model to Support Strategic Hospital Bed Capacity Decisions

et al. 2015

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The ability to accurately measure and assess current and potential health care system capacities is an issue of local and national significance. Recent joint statements by the Institute of Medicine and the Agency for Healthcare Research and Quality have emphasized the need to apply industrial and systems engineering principles to improving health care quality and patient safety outcomes. To address this need, a decision support tool was developed for planning and budgeting of current and future bed capacity, and evaluating potential process improvement efforts. The Strategic Bed Analysis Model (StratBAM) is a discrete-event simulation model created after a thorough analysis of patient flow and data from Geisinger Health System's (GHS) electronic health records. Key inputs include: timing, quantity and category of patient arrivals and discharges; unit-level length of care; patient paths; and projected patient volume and length of stay. Key outputs include: admission wait time by arrival source and receiving unit, and occupancy rates. Electronic health records were used to estimate parameters for probability distributions and to build empirical distributions for unit-level length of care and for patient paths. Validation of the simulation model against GHS operational data confirmed its ability to model real-world data consistently and accurately. StratBAM was successfully used to evaluate the system impact of forecasted patient volumes and length of stay in terms of patient wait times, occupancy rates, and cost. The model is generalizable and can be appropriately scaled for larger and smaller health care settings.

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“…Waiting time variability and the average wait can be reduced 13. SEMs are increasingly being used in healthcare and can consist of several components: pooled/common waiting lists (consolidation of multiple waiting lists); centralised intake (a single point-of-entry through which referrals are received and service provision arranged) and triage (through which referrals are assessed for appropriateness and/or urgency) 14.…”

Section: Introductionmentioning

confidence: 99%

What is the influence of single-entry models on access to elective surgical procedures? A systematic review

et al. 2017

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BackgroundSingle-entry models (SEMs) for the management of patients awaiting elective surgical services are designed to increase access and flow through the system of care. We assessed scope of use and influence of SEMs on access (waiting times/throughput) and patient-centredness (patient/provider acceptability).MethodsSystematic review of articles published in 6 relevant electronic databases included studies from database inception to July 2016. Included studies needed to (1) report on the nature of the SEM; (2) specify elective service and (3) address at least 1 of 3 research questions related to (1) scope of use of SEMs; (2) influence on timeliness and access; (3) patient-centredness and acceptability. Article quality was assessed using a modified Downs and Black checklist.Results11 studies from Canada, Australia and the UK were included with mostly weak observational design—2 simulations, 5 before–after, 2 descriptive and 2 cross-sectional studies. 9 studies showed a decrease in patient waiting times; 6 showed that more patients were meeting benchmark waiting times; and 5 demonstrated that waiting lists decreased using an SEM as compared with controls. Patient acceptability was examined in 6 studies, with high levels of satisfaction reported. Acceptability among general practitioners/surgeons was mixed, as reported in 1 study. Research varied widely in design, scope, reported outcomes and overall quality.ConclusionsThis is the first review to assess the influence of SEMs on access to elective surgery for adults. This review demonstrates a potential ability for SEMs to improve timeliness and patient-centredness of elective services; however, the small number of low-quality studies available does not support firm conclusions about the effectiveness of SEMs to improve access. Further evaluation with higher quality designs and rigour is required.

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Queueing for Healthcare

Cited by 59 publications

References 10 publications

Using Process Analysis to Assess the Impact of Medical Education on the Delivery of Pain Services

Using Process Analysis to Assess the Impact of Medical Education on the Delivery of Pain Services

StratBAM: A Discrete-Event Simulation Model to Support Strategic Hospital Bed Capacity Decisions

What is the influence of single-entry models on access to elective surgical procedures? A systematic review

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