Evidence map›Paper›PMID 40245430›Full record

Observational studyJMIR medical informatics2025

Effect of Smartphone-Based Messaging on Interns and Nurses at an Academic Medical Center: Observational Study.

Sankirth Madabhushi, Andrew M Nguyen, Katie Hsia, Sucharita Kher, William Harvey, Jennifer Murzycki, Daniel Chandler, Michael Davis

Abstract readObservational Study
In one paragraph

Observational study in JMIR medical informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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

8 authors.

Sankirth Madabhushi *Department of Medicine, UMass Chan Medical School, 55 N Lake Ave, Worcester, MA, 01655, United States, 1 (774) 442-2173.ORCID 0009-0002-6643-2415
Andrew M Nguyen *Tufts University School of Medicine, Boston, MA, United States.ORCID 0000-0002-8145-4308
Katie HsiaDepartment of Medicine, Tufts Medical Center, Boston, MA, United States.ORCID 0000-0002-9133-0309
Sucharita KherDepartment of Medicine, Tufts Medical Center, Boston, MA, United States.ORCID 0000-0002-3051-6441
William HarveyInformation Systems, Tufts Medical Center, Boston, MA, United States.ORCID 0000-0001-6254-8037
Jennifer MurzyckiDepartment of Pediatrics, Tufts University School of Medicine, Boston, MA, United States.ORCID 0000-0002-5330-8717
Daniel ChandlerDepartment of Medicine, Tufts Medical Center, Boston, MA, United States.ORCID 0000-0002-9413-8443
Michael DavisDepartment of Medicine, Tufts Medical Center, Boston, MA, United States.ORCID 0000-0001-8575-6613

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Digital communication between nurses and medicine interns plays a crucial role in patient care. However, excessive messaging may contribute to alert fatigue, potentially affecting workflow efficiency and clinical decision-making. Although prior research has examined general messaging behaviors among clinicians, few studies have specifically analyzed messaging patterns between nurses and interns, who serve as primary points of contact in inpatient care. objectives: This study aims to quantitatively characterize messaging patterns between the primary nurse and primary provider (ie, medicine intern) of hospitalized patients at an academic medical center in order to identify communication burdens and potential inefficiencies. By identifying trends in message volume, timing, and response rates, we seek to inform strategies to optimize communication workflows and mitigate alert fatigue. Methods: At a large academic hospital (Tufts Medical Center, Boston, MA), we analyzed secure messaging transactions between internal medicine interns and nurses across three medical-surgical units over 6 months. Transaction metadata, time stamps, and unique message tokens were extracted. Data processing was performed using Python, Microsoft Excel, and R. Message volume, interaction frequencies, and response times were analyzed using measures of central tendency and statistical tests of significance. Results: A total of 61,057 unique messages were exchanged between interns and nurses, with interns exchanging 2.5 times more messages per day with nurses than vice versa (P<.001). Messaging volume exhibited diurnal variation, indicating periods of increased communication burden. Interns read messages from nurses within a median of 35 (range: 0-3589) seconds, whereas nurses read messages from interns within a median of 26 (range: 0-3584) seconds (P<.001). The longest message response delays occurred at 4 AM, whereas the shortest occurred at 8 AM. Conclusions: Interns experience a significantly higher messaging burden than nurses, with distinct peaks in message volume during morning rounds and overnight shifts. These findings suggest a need for interventions such as optimized digital communication protocols to reduce nonessential messaging and alert fatigue. Future research should explore the effectiveness of these interventions in enhancing workflow efficiency and the development of both in-person and digital interventions to optimize communication workflows and mitigate alert fatigue.

Indexed as

Internship and ResidencyNursesSmartphoneText MessagingAcademic Medical CentersHumansWorkflowalert fatigueappapplicationburnoutclinical communicationdigital healthdigital interventiondigital technologyhealthcare communicationmedical informaticsmessagingmessaging patternssecure messagingsmartphonetexttextingTigerConnect

Identifiers

PMID40245430
PMCPMC12021371

What OpenQuestion holds

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LicenceCC BY
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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.