Evidence map›Paper›PMID 30027337›Full record

ArticleHealth care management science2019

Cyclic shift scheduling with on-call duties for emergency medical services.

Tristan Becker, Pia Mareike Steenweg, Brigitte Werners

Abstract read
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In one paragraph

Article in Health care management science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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.

Tristan BeckerChair of Operations Research, Ruhr University Bochum, Universitätsstraße 150, 44801, Bochum, Germany. tristan.becker@rub.de.
Pia Mareike SteenwegChair of Operations Research, Ruhr University Bochum, Universitätsstraße 150, 44801, Bochum, Germany.
Brigitte WernersChair of Operations Research, Ruhr University Bochum, Universitätsstraße 150, 44801, Bochum, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In workforce scheduling for emergency medical services, it is important to ensure sufficient coverage at all time. Thus, planning has to consider unpredictable employee absences. To hedge against this type of uncertainty, on-call duties can be assigned to employees. In practice, these are often assigned ex-post based on the regular schedule. Little literature on workforce scheduling for ambulances or the planning of on-call duties exists. We present new set covering based integer programming formulations for cyclic stint-based staff scheduling with on-call duties. It is desirable for employees to work on consecutive days, called a stint, with a subsequent recovery period. On-call duties can be individually scheduled in-between two stints. Our model formulations integrate different cycle times for regular and on-call duties. A simple schedule that repeats quickly is devised for regular duties, while the on-call duty schedule rotates after each cycle to ensure fairness. The proposed models are applied to a local German emergency medical services provider. Using our stint-based model formulations, the planning complexity has been greatly reduced and reasonably large problem instances can be solved to optimality. Employee preferences, such as fairness, less work on weekends and longer recovery times, were taken into account to a high degree.

Indexed as

Shift Work ScheduleDecision MakingEmergency Medical ServicesGermanyHumansModels, OrganizationalOrganizational Case StudiesOrganizational InnovationPersonnel Staffing and SchedulingWorkloadCyclical schedulingEmergency medical servicesPersonnel planningTactical planning

Identifiers

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.