Evidence map›Paper›PMID 34633589›Full record

ArticleHealth care management science2022

Decision support algorithms for optimizing surgery start times considering the performance variation.

Shing Chih Tsai, Wu Hung Lin, Chia Cheng Wu, Shao Jen Weng, Ching Fen Tang

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Article in Health care management science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Shing Chih TsaiDepartment of Industrial and Information Management, National Cheng Kung University, Tainan, Taiwan.
Wu Hung LinDepartment of Industrial and Information Management, National Cheng Kung University, Tainan, Taiwan.
Chia Cheng WuDepartment of Nursing, Chung-Kang Branch, Cheng Ching General Hospital, Taichung City, Taiwan. romp5655@yahoo.com.tw.
Shao Jen WengDepartment of Industrial Engineering and Enterprise Information, Tunghai University, Taichung City, Taiwan.
Ching Fen TangCenter of Management, Chung-Kang Branch, Cheng Ching General Hospital, Taichung City, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper, we consider a stochastic optimization model for a surgical scheduling problem with a single operating room. The goal is to determine the optimal start times of multiple elective surgeries within a single day. The term "optimal" is defined as the largest surgically related utility value while achieving a given threshold defined by the performance variation of a reference solution. The optimization problem is analytically intractable because it involves quantities such as expectation and variance formulations. This implies that traditional mathematical programming techniques cannot be directly applied. We propose a decision support algorithm, which is a fully sequential procedure using variance screening in the first phase, and then employing multiple attribute utility theory to select the best solution in the second phase. The numerical experiments show that the proposed algorithm can find a promising solution in a reasonable amount of time.

Indexed as

Models, TheoreticalPersonnel Staffing and SchedulingAlgorithmsHumansOperating RoomsTime FactorsMultiple attribute utility theoryOperations researchOR in healthcareRanking and selectionSimulation-based optimizationSurgery planned start timeSurgery scheduling under uncertainty

Identifiers

PMID34633589

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.