Evidence map›Paper›PMID 26370396›Full record

ArticleHealth care management science2016

The operating room case-mix problem under uncertainty and nurses capacity constraints.

Zakaria Yahia, Amr B Eltawil, Nermine A Harraz

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Article in Health care management science, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Zakaria YahiaDepartment of Industrial Engineering, Faculty of Engineering, Fayoum University, 63514, Fayoum, Egypt. zakaria.abdelgawad@ejust.edu.eg.
Amr B EltawilDepartment of Industrial Engineering and Systems Management, Egypt - Japan University of Science and Technology (E-JUST), Alexandria, Egypt.
Nermine A HarrazProduction Engineering Department, Faculty of Engineering, Alexandria University, Alexandria, Egypt.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Surgery is one of the key functions in hospitals; it generates significant revenue and admissions to hospitals. In this paper we address the decision of choosing a case-mix for a surgery department. The objective of this study is to generate an optimal case-mix plan of surgery patients with uncertain surgery operations, which includes uncertainty in surgery durations, length of stay, surgery demand and the availability of nurses. In order to obtain an optimal case-mix plan, a stochastic optimization model is proposed and the sample average approximation method is applied. The proposed model is used to determine the number of surgery cases to be weekly served, the amount of operating rooms' time dedicated to each specialty and the number of ward beds dedicated to each specialty. The optimal case-mix selection criterion is based upon a weighted score taking into account both the waiting list and the historical demand of each patient category. The score aims to maximizing the service level of the operating rooms by increasing the total number of surgery cases that could be served. A computational experiment is presented to demonstrate the performance of the proposed method. The results show that the stochastic model solution outperforms the expected value problem solution. Additional analysis is conducted to study the effect of varying the number of ORs and nurses capacity on the overall ORs' performance.

Indexed as

Efficiency, OrganizationalModels, TheoreticalUncertaintyAppointments and SchedulesComputer SimulationDiagnosis-Related GroupsHumansLength of StayMedicineNursing Staff, HospitalOperating RoomsOperative TimePersonnel Staffing and SchedulingTime FactorsWaiting ListsHealthcare managementMixed integer programmingOperating rooms schedulingSample average approximationStochastic case-mix problemStochastic programming

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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.