Evaluating the effects of increasing surgical volume on emergency department patient access

Levin, Scott R.; Dittus, Robert S.; Aronsky, Dominik; Weinger, Matt

doi:10.1136/bmjqs.2008.030007

Cited by 25 publications

(19 citation statements)

References 21 publications

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“…For example, our ‘cardiac surgery load’ variable was simply a count (i.e., a snapshot) of cardiac surgical procedures occurring within a 1 hour period of the ACS care event being analyzed. In a previously published study, we demonstrated that variability in elective surgery schedules had more significant adverse effects on ED boarding times than sources of natural or random variability [46]. …”

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confidence: 99%

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Factors Influencing Time-Dependent Quality Indicators for Patients With Suspected Acute Coronary Syndrome

et al. 2016

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Objectives Rapid risk stratification and timely treatment are critical to favorable outcomes for acute coronary syndrome (ACS) patients. Our objective was to identify patient and system factors that influence time-dependent quality indicators (QIs) for unstable angina (UA)/non-ST elevation Myocardial Infarction (NSTEMI) patients in the emergency department (ED). Methods A retrospective, cohort study was conducted over a 42-month period of all patients aged 24 years or older suspected of having ACS as defined by receiving an electrocardiogram (ECG) and at least one cardiac biomarker test. Cox regression was used to model the effects of patient characteristics, ancillary service utilization, staffing provisions, equipment availability and ED and hospital crowding on ACS QIs. Results ED adherence rates to national standards for ECG read out time and biomarker turn-around-time were 42% and 37%, respectively. Cox regression models revealed that chief complaints without chest pain and the timing of stress testing and medication administration were associated with the most significant delays. Conclusions Patient and system factors both significantly influenced QI times in this UA/NSTEMI cohort. These results illustrate both the complexity of diagnosing NSTEMI patients and the competing effects of clinical and system factors on patient flow through the ED.

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Section: 0 Discussionmentioning

confidence: 99%

“…In fact, ED visit patterns are fairly predictable by time of day, day of week, and season of year [46]. Nationally, ED occupancy typically increases from early afternoon until night, and it spikes on Friday and Saturday night and annually during influenza season [47].…”

Section: 0 Discussionmentioning

confidence: 99%

Factors Influencing Time-Dependent Quality Indicators for Patients With Suspected Acute Coronary Syndrome

et al. 2016

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“…It is also important to note that using survival analyses within DES was previously demonstrated by the authors (Levin et al 2008;Levin et al 2011). These pUblications were designed for presentation to a clinical audience (cardiology) with limited description of the DES and no explanation of the logic for regression model integration.…”

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confidence: 86%

Simulating wait time in healthcare: Accounting for transition process variability using survival analyses

Levin¹,

Garifullin²

2015

2015 Winter Simulation Conference (WSC)

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Maxim GarifullinGE Healthcare 540 W Northwest Hwy, Barrington, IL 60010, USA Wait or queuing time is a principal performance measure for many discrete-event simulation (DES) models in healthcare. However, variation in wait time is often caused by both occupied downstream servers (e.g., beds) and organizational and human transition processes. DES models that attribute wait solely to occupied servers, ignoring transition process variability, face challenges in adequate baseline validation. Embedding regression models for survival data in DES to estimate patient wait times is a method capable of integrating the effects of transition processes with queuing. Developing these models as a sUb-component is further valuable in understanding the socio-technical system factors that drive pro longed waits. These general methods are exhibited in a DES for a large urban hospital with a primary output of wait time in the emergency department (ED) for transfer to an inpatient bed (boarding time). Simulated boarding time is compared before and after accounting for transition processes using survival analysis. 1 978-1-4673-9743-8/15/$31.00 ©2015 IEEE

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“…Also noteworthy is delayed patient transfer from the ED to surgery, particularly given the need for acute care within EDs (Levin, Dittus, Aronsky, Weinger, & France, 2011). Researchers have effectively addressed this bottleneck and reduced patient wait-times by assigning a dedicated surgical consultant and additional staff within the ED (Qureshi et al, 2011).…”

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confidence: 99%

Development of a cost‐optimization model to reduce bottlenecks: A health service case study

Lau

Nakandala

Evans³

et al. 2018

Expert Systems

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With the increasing use of emergency departments, many public hospitals experience bottlenecks that hinder patient flow within the health system. Mitigating bottlenecks can enhance workflow efficiency and reduce patient wait‐time. Yet given the complexity of health services, current techniques have a limited capacity to address this issue. This article introduces an innovative generic cost‐optimization model based on genetic algorithm to alleviate bottlenecks without the need for complex mathematical analysis. A case study is presented to validate its feasibility, demonstrating an evidence‐based, pragmatic way to alleviate bottlenecks that practitioners can readily implement.

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Evaluating the effects of increasing surgical volume on emergency department patient access

Abstract: Simulating competition dynamics for hospital admissions provides prospective planning (ie, decision making) information and demonstrates how interventions to increase inpatient throughput will have a much greater effect on higher priority surgical admissions compared with ED admissions.

Cited by 25 publications

References 21 publications

Factors Influencing Time-Dependent Quality Indicators for Patients With Suspected Acute Coronary Syndrome

Factors Influencing Time-Dependent Quality Indicators for Patients With Suspected Acute Coronary Syndrome

Simulating wait time in healthcare: Accounting for transition process variability using survival analyses

Development of a cost‐optimization model to reduce bottlenecks: A health service case study

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