Optimal booking and scheduling in outpatient procedure centers

Berg, Bjorn P.; Denton, Brian T.; Erdogan, S. Ayca; Rohleder, Thomas R.; Huschka, Todd R.

doi:10.1016/j.cor.2014.04.007

Cited by 103 publications

(68 citation statements)

References 37 publications

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“…Related studies include Lau (1992, 1999), Lau and Lau (2000) and Wijewickrama and Takakuwa (2005). Similarly, Berg et al (2014) formulated a booking, sequencing and scheduling decision model of a single stochastic server model with an expected profit function including the above cost components. The weighted multi-objective function considered here includes additional concern on waiting room congestion but will be applied to a larger number of patients scheduled to a more complex service network.…”

Section: Literature Reviewmentioning

confidence: 99%

An adaptive scheduling heuristic with memory for the block appointment system of an outpatient specialty clinic

Lin

2015

International Journal of Production Research

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This work analysed the appointment system of outpatient clinics serving multiple patient classes with different flow sequences through the multi-phase-multi-server service system. Scarce resources are doctors, nurses and medical professionals with different start times and availability. Block appointment systems are typically used in public hospitals to help regulate patient flow while minimising patient waiting time, staff overtime and waiting room congestion. The patient scheduling problem in this complex environment is formulated by a mixed integer programme (MIP). Making use of waiting time information, an adaptive scheduling heuristic is designed to improve an initial schedule iteratively by identifying procedures with large average waiting times and reassigning their related patient classes to less congested time blocks probabilistically. An impact index based on the weighted multi-objective function is developed to allow servers select an available patient for the next treatment. A memory of distinct solutions is maintained to avoid recycling. Experiments are conducted based on a case study of an eye clinic in a public hospital. Performance is evaluated by comparing with the MIP and well-known dispatching rules for job shop scheduling problems. Sensitivity analysis is conducted for increase in appointment quota, two alternative staffing plans and changes in patient class distribution.

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Section: Literature Reviewmentioning

confidence: 99%

An adaptive scheduling heuristic with memory for the block appointment system of an outpatient specialty clinic

Lin

2015

International Journal of Production Research

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“…The scheduling of patients with different priority classes and medical resource allocation to these classes has gotten a lot of attention in recent years, as evidenced by the large number of papers in the literature (Patrick et al 2008, Qu et al 2013, Berg et al 2014, Feldman et al 2014, Kong et al 2015, Jiang et al 2017. Ahmadi-Javid et al (2017) provide a comprehensive review of recent analytical and numerical studies in the area of outpatient scheduling.…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluating Appointment Postponement in Scheduling Patients at a Diagnostic Clinic

et al. 2020

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Diagnostic clinics are among healthcare facilities that suffer from long waiting times which can cause medical issues and lead to increases in patient no-shows. Reducing waiting times without significant capital investments is a challenging task. We tackle this challenge by proposing a new appointment scheduling policy for such clinics that does not require significant investments. The clinic in our study serves outpatients, inpatients, and emergency patients. Emergency patients must be seen on arrival, and inpatients must be given next day appointments. Outpatients, however, can be given later appointments. The proposed policy takes advantage of this by allowing the postponement of the acceptance of appointment requests from outpatients. The appointment scheduling process is modeled as a two-stage stochastic programming problem where a portion of the clinic capacity is allocated to inpatients and emergency patients in the first stage.In the second stage, outpatients are scheduled based on their priority classes. After a detailed analysis of the solutions obtained from the two-stage stochastic model, we develop a simple, non-anticipative policy for patient scheduling. We evaluate the performance of this proposed, easy-to-implement policy in a simulation study which shows significant improvements in outpatient indirect waiting times. ) classify waiting time of patients into two categories. They define direct waiting time as the time the patient waits in the healthcare facility on the day of appointment and indirect waiting time as the time between the day the patient requests an appointment and the appointment day. Unfortunately, long indirect waiting times are common in practice. For instance, Kesling and Nissenbaum (2014) reported that 84% of patients in Veterans Affairs (VA) hospitals wait more than 14 days to see a physician. In addition to the medical issues that long indirect waiting times cause, they can also lead to increases in patient no-shows (Green et al. 2006) which have significant effect on annual revenues (Moore et al. 2001). Thus, healthcare managers face the challenge of improving their appointment systems to decrease waiting times and no-shows without incurring major capital costs. Diagnostic clinics are among the healthcare facilities that generally suffer from long indirect waiting times (McCarthy et al. 2000). One such clinic is the Radiology Department at PrismaHealth, our collaborator on this study. The clinic provides service to outpatients, inpatients, and emergency patients. The requests for appointments are handled on a first-come-first-served (FCFS) basis. The emergency patients are the highest priority group, followed by inpatients and then outpatients. The outpatients are further categorized into a number of priority classes based on co-morbidities and chronic conditions. The emergency patients are seen as soon as they arrive if there is capacity or immediately referred to another clinic. The inpatients are either given a next day appointment during regular hours or seen during overt...

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“…Dynamic scheduling models have been developed to optimize chemotherapy outpatient scheduling and reduce waiting times [15][16][17]. Although the mathematical modeling of these templates suggests that they can offer improvements to chemotherapy scheduling, there is little evidence so far of their use in real-life practice.…”

Section: Other Initiatives and Tools To Evaluate Efficiency Of Suppormentioning

confidence: 99%

The Importance of Supportive Care in Optimizing Treatment Outcomes of Patients with Advanced Prostate Cancer

Scotté

2012

The Oncologist

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Optimal oncologic care of older men with prostate cancer, including effective prevention and management of the disease and treatment side effects (so-called best supportive care measures) can prolong survival, improve quality of life, and reduce depressive symptoms. In addition, the proportion of treatment discontinuations can be reduced through early reporting and management of side effects. Pharmacologic care may be offered to manage the side effects of androgen-deprivation therapy and chemotherapy, which may include hot flashes, febrile neutropenia, fatigue, and diarrhea. Nonpharmacologic care (e.g., physical exercise, acupuncture, relaxation) has also been shown to benefit patients.

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Optimal booking and scheduling in outpatient procedure centers

Cited by 103 publications

References 37 publications

An adaptive scheduling heuristic with memory for the block appointment system of an outpatient specialty clinic

An adaptive scheduling heuristic with memory for the block appointment system of an outpatient specialty clinic

Evaluating Appointment Postponement in Scheduling Patients at a Diagnostic Clinic

The Importance of Supportive Care in Optimizing Treatment Outcomes of Patients with Advanced Prostate Cancer

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