Evidence map›Paper›PMID 42027849›Full record

ArticleInternational journal of nursing studies advances2026

Care demand networks in maternity care - an innovative approach exploring the complexity of care demands with routine data: Retrospective observational study.

Diana Trutschel, Luisa Eggenschwiler, Niklaus Gygli, Jack Kuipers, Giusi Moffa, Michael Simon

Abstract read
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Article in International journal of nursing studies advances, 2026. 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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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Diana TrutschelInstitute of Nursing Science, University of Basel, Basel, Switzerland.
Luisa EggenschwilerInstitute of Nursing Science, University of Basel, Basel, Switzerland.
Niklaus GygliChief Nursing Office, University Hospital of Basel, Basel, Switzerland.
Jack KuipersDepartment of Biosystems Science and Engineering, ETH Zürich, Switzerland.
Giusi MoffaDepartment of Mathematics and Computer Science, University of Basel, Switzerland.
Michael SimonInstitute of Nursing Science, University of Basel, Basel, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Care demand is complex, variable, and intense. Yet, most staffing tools that aim to inform staffing requirements reduce it to overly simplified metrics. Electronic health records contain substantial information that could support decision-making as affected by care demands. Standardized care activity catalogs document nursing interventions and the associated care time spent, offering rich insights into care delivery. However, their combined potential to inform staffing models and for exploring the complexity of care remains untapped. Network analysis allows for the analysis and visualization of critical patterns in care demands. Objective: This study applies network analysis to explore maternity care demand and illustrates how to describe care demand networks according to network terminology. Design: Retrospective observational study using routinely collected data. Settings: Maternity units in a Swiss tertiary hospital. Participants: 2 346 maternal case records during the postnatal period. Methods: A correlation network analysis was conducted using 244 382 recorded care activities, with the minutes associated for each activity and following individual cases summed up over the hospital stay. Pearson correlations between activities were estimated to create a partial-correlation network, while edges with |correlations| ≥ 0.15 were retained. The final undirected, weighted graph was analyzed using standard network metrics to explore the features of care from the network structure. Results: A total of 113 different care activities were recorded, with an average duration of 3.7 min. The resulting network suggests dense subgroups of correlated care activities in the maternity care process. Conclusions: This study demonstrated the successful application of network methodology to visualize and enhance the understanding of maternity care using routinely collected data. The network approach can be further explored to understand day-to-day care demands and eventually to predict care demands for upcoming days, which fosters staff rostering based on previous care demands. Registration:

Indexed as

Delivery of health careElectronic health recordsMaternal health servicesMidwiferyNursing staffRetrospective studiesRoutinely collected health data

Identifiers

PMID42027849
PMCPMC13101619

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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.