Capacity planning with demand uncertainty for outpatient clinics

Nguyen, Thu Ba T.; Sivakumar, Appa Iyer; Graves, Stephen C.

doi:10.1016/j.ejor.2017.11.038

Cited by 13 publications

(13 citation statements)

References 39 publications

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“…Consequently, care demand patterns are characterized by periods of surge and recession, leaving demand projections fraught with uncertainty. It is possible to account for this uncertainty in the formulas used to project demand and/or estimate resource requirements (Nguyen, Sivakumar, Appa, & Graves, ). Failure to do so increases the likelihood of supply–demand imbalances and an ineffective surge capacity that results in missed care (Blackman et al, ).…”

Section: Economic Theorymentioning

confidence: 99%

Systems, economics, and neoliberal politics: Theories to understand missed nursing care

Jones

Drach‐Zahavy

Amorim‐Lopes

et al. 2020

Nursing & Health Sciences

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The phenomenon of missed nursing care is endemic across all sectors. Nurse leaders have drawn attention to the implications of missed care for patient outcomes, with calls to develop clear political, methodological, and theoretical approaches. As part of this call, we describe three structural theories that inform frameworks of missed care: systems theory, economic theory, and neoliberal politics. The final section provides commentary on the strengths and limitations of these three theories, in the light of structuration theory and calls to balance this research agenda by reinstating nurse agency and examining the interactions between nurses as agents and the health systems as structures. The paper argues that a better understanding of variations in structure–agency interaction across the healthcare system might lead to more effective interventions at strategic leverage points.

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Section: Economic Theorymentioning

confidence: 99%

Systems, economics, and neoliberal politics: Theories to understand missed nursing care

Jones

Drach‐Zahavy

Amorim‐Lopes

et al. 2020

Nursing & Health Sciences

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“…Capacity planning in primary care clinics has been widely studied (Cayirli, Dursun & Gunes, 2019;Nguyen, Sivakumar & Graves, 2018;Qu, Peng, Shi & LaGanga, 2015). Capacity planning is even more critical in cancer clinics as patients benefit from early access to oncologists.…”

Section: Literature Reviewmentioning

confidence: 99%

Improving patient access in oncology clinics using simulation

Keshtzari

Norman²

2022

JIEM

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Purpose: Providing timely access is an important measure of patient satisfaction in specialty care clinics such as cancer centers. Excessive patient wait time to see an oncologist is very critical for cancer patients as they often benefit from starting the treatment process as soon as possible. This paper addresses capacity planning for both new and returning patients in cancer clinics. This research is motivated by a cancer center in Texas that seeks to improve its clinical performance to decrease new patient wait time to see an oncologist.Design/methodology/approach: A simulation model is proposed to assess new patient access to oncologists when employing several tactical and operational policies such as resource flexibility, specialization flexibility, and reserving slots for new patients. The model utilizes two years of data collected from a cancer center in Texas.Findings: The results suggest the best combination of operating policies in order to allocate patient demand to providers. This study also determines the required capacity level to provide timely access for new patients.Originality/value: Although the literature in outpatient scheduling and capacity planning is rich, new patient access in oncology clinics has received limited attention. The few existing studies do not consider patient no-shows and cancellations, and to the best of our knowledge, no study addresses individual oncologist clinic flexibility and the idea of reserving slots for new patients.

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“…Although there is a wide range of applications using DES, ranging from manufacturing to service provision, few studies present an analysis of resource capacity that includes service levels. For instance, there are numerous simulation studies in literature attempting to understand the relationship that may exist between various inputs into a system (e.g., patient scheduling and staff resources) and different output performance measures from the system (e.g., patient waiting times) [8], [11]. However, they rarely combine service levels and resource capacity applied to financial services [7].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The health sector has been using DES as an effective tool for allocating scarce resources to improve patient flow, while minimizing healthcare delivery costs and increasing patient satisfaction [8]- [10]. Previous studies have examined the resource allocation problems to optimally manage the arrival demands for different service sectors [11]- [13], occasionally for financial services. Given that financial services have a closer relationship with retail, the alliance between a financial institution and a retailer consists of the allocation of a Point of Sale (POS) for credit cards that share the brand of both the retailer and the financial institution.…”

Section: Introductionmentioning

confidence: 99%

Resources Allocation in Service Planning Using Discrete-Event Simulation

Fernández

Herrera

Trejos

et al. 2021

IyU

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Objective: Calculate the required personnel and resources needed to fulfill the service promise agreed with the customer. Methods and materials: This paper presents a discrete event simulation (DES) model developed to select and implement a Point of Sale (POS) for a company providing financial products. First, the paper shows the characterization of the system components and times per process. Then, hypothesis testing and goodness-of-fit statistics are estimated. Subsequently, the simulation scenarios assess the times between arrivals and the number of commercial advisers. Results and discussion: This model allows us to assess the allocation of resources to fulfill the service promise, which is that 80 % of customers must be served within one hour or less. This paper provided the service isoquants allowing us to observe the behavior of the performance metrics (service promise fulfillment) among different scenarios. Conclusions: The use of DES techniques allows for the evaluation of the assignment of personnel to achieve the fulfillment of the service promise, including facilities, equipment, and the evaluation of related processes. These methods can be extended to the analysis of resource allocation in the development of other processes, observing the relationship between service quality and operating costs.

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Capacity planning with demand uncertainty for outpatient clinics

Cited by 13 publications

References 39 publications

Systems, economics, and neoliberal politics: Theories to understand missed nursing care

Systems, economics, and neoliberal politics: Theories to understand missed nursing care

Improving patient access in oncology clinics using simulation

Resources Allocation in Service Planning Using Discrete-Event Simulation

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