ArticleJournal of medical systems2025
Social Network Analysis of Secure Text Messaging Metadata During Clinical Deterioration in an Inpatient Children's Hospital Setting.
Article in Journal of medical systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mitigating clinical deterioration relies upon recognition (afferent limb) and interventions (efferent limb) by a healthcare team. Healthcare provider (HP) communication by text messaging plays a role in facilitating both limbs in the inpatient setting. We sought to quantitatively characterize healthcare provider team communications through the social network analysis (SNA) of secure text messages exchanged in the inpatient setting, and as they relate to a subgroup of patients demonstrating a deterioration during their hospitalization. Messages linked to inpatients exchanged between HPs over a 12-month period, including a cohort of messages linked to patients experiencing deterioration were analyzed using SNA. Subnetworks corresponding to individual patient encounters were constructed, including a series of subnetworks pertaining to patients with an impending clinical deterioration. Network and network participant characteristics were calculated and analyzed. From October 2022 through September 2023 there were 1,065,225 messages delivered by 3,272 HPs, associated with 4,328 inpatient hospital encounters, of which 120 hospital encounters were associated with a deterioration. SNA demonstrated significantly higher measures of eigenvector centrality among frontline providers (FLP) including advanced practice providers and housestaff, relative to attending physician (p < 0.001) and registered nurses (p < 0.001), consistent with greater influence of the FLP on information dissemination through the entire network. Within individual subnetworks associated with the care of patients experiencing a clinical deterioration, FLP participants demonstrated greater overall network influence (p = 0.032) relative to FLP counterparts in networks not associated with a deterioration, despite comparable numbers of participants and connections. Using SNA, we quantitatively characterized a text messaging network within an inpatient hospital setting, demonstrating the importance of FLPs on information dissemination, a finding demonstrated specifically within subnetworks dedicated to the care of individual deteriorating patients. Understanding characteristics of a dynamic communication network of healthcare providers may prove a valuable target in facilitating communication and in mitigating the risks of deterioration.IRB Approval: Johns Hopkins Medicine IRB (#CIR00419339).Clinical trial number: Not applicable.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.