Innovative Strategies for Decreasing Blood Collection Wait Times for Patients in Early-Phase Cancer Clinical Trials

Mengistu, Bayabel; Ray, D.; Lockett, Passion; Dorsey, V.; Phipps, Ron A.; Subramanian, Hari H.; Atkins, Johnique T.; Osta, Badi El; Karp, Daniel D.

doi:10.1200/jop.2015.007674

Cited by 3 publications

(3 citation statements)

References 18 publications

(14 reference statements)

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“…The first is formulating the performance metric related to patients’ waiting time and OR overtime. Long waiting times are leading causes of patients’ dissatisfaction, which not only essentially decrease OR costs and surgical outcomes but also harm patients’ psychological status [ 6 ]. Hence, an accurate measure of patients’ waiting time should be provided as necessary for an OR scheduling model [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

“…The goal of the scheduling problem is to allocate a given amount of resources to achieve a certain performance metric, such as maximizing the utilization rates of the OR, minimizing the operational cost, minimizing OR overtime, minimizing patients’ waiting time, etc. [ 6 , 7 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. For example, Mancilla et al [ 9 ] developed a stochastic model to minimize the weighted cost associated with patients’ waiting time, OR idle time, and overtime.…”

Section: Introductionmentioning

confidence: 99%

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Robust Operating Room Scheduling Model with Violation Probability Consideration under Uncertain Surgery Duration

Liu

et al. 2022

IJERPH

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This paper proposes an operating room (OR) scheduling model to assign a group of next-day patients to ORs while adhering to OR availability, priorities, and OR overtime constraints. Existing studies usually consider OR scheduling problems by ignoring the influence of uncertainties in surgery durations on the OR assignment. In this paper, we address this issue by formulating accurate patient waiting times as the cumulative sum of uncertain surgery durations from the robust discrete approach point of view. Specifically, by considering the patients’ uncertain surgery duration, we formulate the robust OR scheduling model to minimize the sum of the fixed OR opening cost, the patient waiting penalty cost, and the OR overtime cost. Then, we adopt the box uncertainty set to specify the uncertain surgery duration, and a robustness coefficient is introduced to control the robustness of the model. This resulting robust model is essentially intractable in its original form because there are uncertain variables in both the objective function and constraint. To make this model solvable, we then transform it into a Mixed Integer Linear Programming (MILP) model by employing the robust discrete optimization theory and the strong dual theory. Moreover, to evaluate the reliability of the robust OR scheduling model under different robustness coefficients, we theoretically analyze the constraint violation probability associated with overtime constraints. Finally, an in-depth numerical analysis is conducted to verify the proposed model’s effectiveness and to evaluate the robustness coefficient’s impact on the model performance. Our analytical results indicate the following: (1) With the robustness coefficient, we obtain the tradeoff relationship between the total management cost and the constraint violation probability, i.e., a smaller robustness coefficient yields remarkably lower total management cost at the expense of a noticeably higher constraint violation probability and vice versa. (2) The obtained total management cost is sensitive to small robustness coefficient values, but it hardly changes as the robustness coefficient increases to a specific value. (3) The obtained total management cost becomes increasingly sensitive to the perturbation factor with the decrease in constraint violation probability.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Operating Room Scheduling Model with Violation Probability Consideration under Uncertain Surgery Duration

Liu

et al. 2022

IJERPH

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“…Management strategies have been shown to shorten the WT for a phlebotomy. It was reported that there was a 19% decrease in WT after changes were made to the collection of materials, the LabTracker automated database system was improved with wait time calculators and real-time information regarding patient status, and lower-complexity appointments were streamlined [8]. Gupta et al also concluded that analyzing the feedback of root causes was effective at maintaining and improving phlebotomy services [9].…”

Section: Introductionmentioning

confidence: 99%

Processing Cycle Efficiency to Monitor the Performance of an Intelligent Tube Preparation System for Phlebotomy Services

Lin

et al. 2021

IJERPH

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Background: The waiting time (WT) for a phlebotomy is directly related to patient satisfaction with a health service. However, the processing time varies widely depending on the type of patients. Monitoring of the WT alone may not enable an effective evaluation of the lean performance of the medical staff for patients with different characteristics. The objective of this study was to use process cycle efficiency (PCE) to assess the performance of an intelligent tube preparation system (ITPS) which automatically labeled test tubes and conducted patient rerouting for phlebotomy services, and to interpret the WT during peak hours. Methods: Three time periods were used. The baseline period was from 1 July to 31 July 2014. Phase 1 was after the establishment of the ITPS, with patients ≥80 years old being rerouted. In phase 2, patients ≥78 years old were rerouted. Those data were recorded with a calling system and ITPS, respectively. Results: PCE was significantly improved from 12.9% at baseline to 51.1% (p < 0.001) in phase 1 and 53.0% (p < 0.001) in phase 2. The WT of 16.9 min at baseline was reduced to 3.8 min in phase 1 (p < 0.001), and 3.6 min in phase 2 (p < 0.001). Moreover, the results showed that a WT < 10 min was consistent with a PCE ≥ 25%. Conclusions: Establishing an ITPS for phlebotomy can significantly increase PCE and shorten the WT. Furthermore, the PCE ≥ 25% could be a good assessment reference for the management of appropriate human resources for phlebotomy services, although it is a complex parameter.

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Scheduling patient appointment in an infusion center: a mixed integer robust optimization approach

Issabakhsh

Lee

Kang

2020

Health Care Manag Sci

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Innovative Strategies for Decreasing Blood Collection Wait Times for Patients in Early-Phase Cancer Clinical Trials

Cited by 3 publications

References 18 publications

Robust Operating Room Scheduling Model with Violation Probability Consideration under Uncertain Surgery Duration

Robust Operating Room Scheduling Model with Violation Probability Consideration under Uncertain Surgery Duration

Processing Cycle Efficiency to Monitor the Performance of an Intelligent Tube Preparation System for Phlebotomy Services

Scheduling patient appointment in an infusion center: a mixed integer robust optimization approach

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