Evidence map›Paper›PMID 41865476›Full record

ArticleInternational journal of medical informatics2026

Characterizing nursing home care team communication via text messaging: A social network analysis.

Kimberly R Powell, Matthew S Farmer, Mihail Popescu, David R Mehr, Gregory L Alexander

Abstract read
In one paragraph

Article in International journal of medical informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

5 authors.

Kimberly R PowellSinclair School of Nursing, University of Missouri, Columbia, MO 65211, USA. Electronic address: powellk@missouri.edu.
Matthew S FarmerSinclair School of Nursing, University of Missouri, Columbia, MO 65211, USA.
Mihail PopescuDepartment of Biomedical Informatics, Biostatistics, and Medical Epidemiology, University of Missouri, Columbia, MO, USA.
David R MehrDepartment of Family and Community Medicine, University of Missouri, Columbia, MO, USA.
Gregory L AlexanderSchool of Nursing, Center for Health Policy, Columbia University, New York, NY, USA.

Funding

Reducing Avoidable Nursing Home-to-Hospital Transfers of Residents with ADRD: An Analysis of Interdisciplinary Team Communication using Text MessagesR01AG078281 · NIA · UNIVERSITY OF MISSOURI-COLUMBIA · PI POWELL, KIMBERLY RYAN · 2022 to 2024
$1.1M
NIA NIH HHS R01 AG078281
6 · The paper itself

Abstract

purposeCommunication breakdowns among healthcare teams contribute substantially to preventable adverse events in nursing homes. Although text messaging platforms are increasingly used to support care coordination, little is known about how these technologies are embedded within care team communication networks. The purpose of this study was to characterize patterns of text-message communication among nursing home care teams and to examine how network structures reflect underlying social system dynamics.

methodsSocial network analysis was applied to text messages (n = 5,092) linked to (n = 585) nursing home-to-hospital resident transfers over 5 years (2015-2020). Message metadata and content were used to construct communication networks and to calculate network measures, including density, centralization (in-degree and out-degree), and reciprocity. Networks were qualitatively classified into communication models based on shared structural characteristics and interpreted using Rogers' Diffusion of Innovations theory. All analysis and visualization was conducted using Python (3.13.9).

resultsThree distinct communication models were identified. The Integrated model exhibited high message volume, high connectivity, and low hierarchy, consistent with mature adoption of text messaging as a general collaboration tool. The Hub-and-Spoke model showed moderate message volume with centralized information flow, reflecting protocol-driven, hierarchical communication. The Siloed model demonstrated high message volume but low role diversity, indicating niche use within specific professional roles rather than facility-wide coordination. Across models, individuals occupying central or bridging positions appeared to function as informal opinion leaders, influencing communication through frequent interaction rather than formal authority.

conclusionsThe structure and quality of communication networks shape how information flows, influence is exercised through electronic messaging, and innovations are integrated into care processes. Social network analysis offers a rigorous approach for evaluating implementation and guiding strategies to support effective, team-based communication in nursing home settings. Future work should examine whether increasingly integrated communication networks are associated with improved resident outcomes.

Indexed as

CommunicationNursing HomesPatient Care TeamSocial Network AnalysisText MessagingHumansCommunicationDiffusion of innovationsNursing homesNursing informaticsSocial network analysisText messages

Identifiers

PMID41865476
PMCPMC13488489

What OpenQuestion holds

Textmetadata
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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.