Evidence map›Paper›PMID 41509660›Full record

ArticleHealth systems (Basingstoke, England)2025

A chance-constrained program for the allocation of nurses in acute home healthcare.

Jedidja Lok-Visser, Hayo Bos, Erwin W Hans, Gréanne Leeftink

Abstract read
In one paragraph

Article in Health systems (Basingstoke, England), 2025. 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

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

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.

Jedidja Lok-VisserCenter of Healthcare Operations Improvement and Research, Industrial Engineering & Business Information Systems, University of Twente, Enschede, The Netherlands.
Hayo BosCenter of Healthcare Operations Improvement and Research, Industrial Engineering & Business Information Systems, University of Twente, Enschede, The Netherlands.
Erwin W HansCenter of Healthcare Operations Improvement and Research, Industrial Engineering & Business Information Systems, University of Twente, Enschede, The Netherlands.
Gréanne LeeftinkCenter of Healthcare Operations Improvement and Research, Industrial Engineering & Business Information Systems, University of Twente, Enschede, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Home healthcare capacity is under great pressure due to demographic developments. Existing literature has exclusively focused on the planning, scheduling, and routing of non-acute care activities. However, similar to other healthcare settings, home healthcare also experiences acute care activities that disrupt operational performance. We study the planning and control of an acute care team for dealing with unplanned and urgent home healthcare activities. Particularly, we focus on determining the number of nurses per care level and their standby locations. The primary aim of this study is to introduce this novel problem, which we define as the acute care team location problem. We formulate this problem as a chance-constrained program. We solve the single location problem to optimality, and the multi-location problem with sample average approximation. The results show that our approach enables decision makers to optimally configure their acute care team, to respond quickly to acute care incidents. From a managerial perspective, our research provides a model that supports tactical capacity planning in HHC organisations and presents a benchmark for acute care management policies.

Indexed as

acute care managementcapacity planningchance-constraintshealthcare facility planningHome healthcareprobabilistic set covering problem

Identifiers

PMID41509660
PMCPMC12777871

What OpenQuestion holds

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LicenceCC BY
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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.