Evidence map›Paper›PMID 39133348›Full record

ArticleJournal of medical systems2024

Intelligent Clinic Nurse Scheduling Considering Nurses Paired with Doctors and Preference of Nurses.

Yu-Chung Tsao, Danny Chen, Feng-Jang Hwang, Vu Thuy Linh

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Article in Journal of medical systems, 2024. 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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0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Yu-Chung TsaoDepartment of Industrial Management, National Taiwan University of Science and Technology, Taipei, Taiwan. yctsao@mail.ntust.edu.tw.
Danny ChenDepartment of Industrial Management, National Taiwan University of Science and Technology, Taipei, Taiwan.
Feng-Jang HwangDepartment of Business Management, National Sun Yat-sen University, Kaohsiung, Taiwan.
Vu Thuy LinhDepartment of Industrial Management, National Taiwan University of Science and Technology, Taipei, Taiwan.

Funding

National Science and Technology Council 112-2628-E-011-009
6 · The paper itself

Abstract

The nurse scheduling problem (NSP) has been a crucial and challenging research issue for hospitals, especially considering the serious deterioration in nursing shortages in recent years owing to long working hours, considerable work pressure, and irregular lifestyle, which are important in the service industry. This study investigates the NSP that aims to maximize nurse satisfaction with the generated schedule subject to government laws, internal regulations of hospitals, doctor-nurse pairing rules, shift and day off preferences of nurses, etc. The computational experiment results show that our proposed hybrid metaheuristic outperforms other metaheuristics and manual scheduling in terms of both computation time and solution quality. The presented solution procedure is implemented in a real-world clinic, which is used as a case study. The developed scheduling technique reduced the time spent on scheduling by 93% and increased the satisfaction of the schedule by 21%, which further enhanced the operating efficiency and service quality.

Indexed as

Job SatisfactionPersonnel Staffing and SchedulingEfficiency, OrganizationalHumansNursing Staff, HospitalPhysiciansHybrid metaheuristicsNurse schedulingPairing rulePreferenceSatisfactionService industry

Identifiers

PMID39133348

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.