Evidence map›Paper›PMID 36197537›Full record

ArticleHealth care management science2023

Nurse rostering with fatigue modelling : Incorporating a validated sleep model with biological variations in nurse rostering.

Kjartan Kastet Klyve, Ilankaikone Senthooran, Mark Wallace

Open access · hybridFull text read
In one paragraph

Article in Health care management science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
1.0field-weighted citation impact, top 21% of its field
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

2 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.

  1. Pooled it
  2. Article
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 at 2 institutions in 2 countries.

Kjartan Kastet KlyveDepartment of Industrial Economics and Technology Management, Norwegian University of Science and Technology, Trondheim, Norway. kjartan.klyve@ntnu.no.ORCID http://orcid.org/0000-0002-6776-6604
Ilankaikone SenthooranMonash University, Melbourne, Australia.
Mark WallaceMonash University, Melbourne, Australia.
Monash University · AUNorwegian University of Science and Technology · NO

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We use a real Nurse Rostering Problem and a validated model of human sleep to formulate the Nurse Rostering Problem with Fatigue. The fatigue modelling includes individual biologies, thus enabling personalised schedules for every nurse. We create an approximation of the sleep model in the form of a look-up table, enabling its incorporation into nurse rostering. The problem is solved using an algorithm that combines Mixed-Integer Programming and Constraint Programming with a Large Neighbourhood Search. A post-processing algorithm deals with errors, to produce feasible rosters minimising global fatigue. The results demonstrate the realism of protecting nurses from highly fatiguing schedules and ensuring the alertness of staff. We further demonstrate how minimally increased staffing levels enable lower fatigue, and find evidence to suggest biological complementarity among staff can be used to reduce fatigue. We also demonstrate how tailoring shifts to nurses' biology reduces the overall fatigue of the team, which means managers must grapple with the issue of fairness in rostering.

Indexed as

AlgorithmsPersonnel Staffing and SchedulingHumansConstraint programmingFatigueLarge neighbourhood searchMixed-Integer programmingNurse rosteringOperations researchSleep

Identifiers

PMID36197537
PMCPMC10011308
OpenAlexW4301586223

What OpenQuestion holds

Textfull text, public
LicenceCC BY
measurements read22
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.