Observational studyJournal of medical Internet research2023
Characterizing the Patterns of Electronic Health Record-Integrated Secure Messaging Use: Cross-Sectional Study.
Observational study in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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.
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.
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Who cites it
21 citing papers in PubMed.
- Changes in Communication after Transition from Paging to Collaborative Text Messaging.Journal of the American College of Emergency Physicians open · 2026Article
- Informal Digital Coordination of Nurse Shift Swaps in a Saudi Tertiary Hospital: A Qualitative Descriptive Study.Healthcare (Basel, Switzerland) · 2026Article
- Secure Messaging Workload and the Effect of an Enhanced Workflow Intervention Among Hospitalist Clinicians.Journal of general internal medicine · 2026Article
- Emergency Department Clinician Perspectives on an EMR-Adjacent Microsoft Teams Consultation Workflow.Open access emergency medicine : OAEM · 2026Article
- Characterization of Secure Message Use in a Children's Hospital.Hospital pediatrics · 2026Article
- Physicians' Experiences Using Secure Messaging for Diabetes Management: A Qualitative Study.JMIR diabetes · 2025Article
- Association Between Conversational Multitasking and Clinician Work Behaviors at a Large US Health Care System: Cohort Study.Journal of medical Internet research · 2025Observational
- Predicting Secure Messaging Traffic in Clinical Settings.Studies in health technology and informatics · 2025Article
- Networked Behaviors Associated With a Large-Scale Secure Messaging Network: Cross-Sectional Secondary Data Analysis.JMIR medical informatics · 2025Article
- Association of EHR-Integrated Secure Messaging Use with Clinician Workload and Attention Switching.Journal of general internal medicine · 2025Article
- Article
- Effect of Smartphone-Based Messaging on Interns and Nurses at an Academic Medical Center: Observational Study.JMIR medical informatics · 2025Observational
- Emerging Domains for Measuring Health Care Delivery With Electronic Health Record Metadata.Journal of medical Internet research · 2025Review
- AI-generated draft replies to patient messages: exploring effects of implementation.Frontiers in digital health · 2025Article
- Secure Messaging Use and Wrong-Patient Ordering Errors Among Inpatient Clinicians.JAMA network open · 2024Article
- Primary Care Informatics: Vitalizing the Bedrock of Health Care.Journal of medical Internet research · 2024Article
- Human Factors and Organizational Issues in Health Informatics: Review of Recent Developments and Advances.Yearbook of medical informatics · 2024Review
- Article
- Secure Messaging and Telephone Use for Clinician-to-Clinician Communication.JAMA network open · 2024Article
- Contributors to Electronic Health Record-Integrated Secure Messaging Use: A Study of Over 33,000 Health Care Professionals.Applied clinical informatics · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCommunication among health care professionals is essential for the delivery of safe clinical care. Secure messaging has rapidly emerged as a new mode of asynchronous communication. Despite its popularity, relatively little is known about how secure messaging is used and how such use contributes to communication burden.
objectiveThis study aims to characterize the use of an electronic health record-integrated secure messaging platform across 14 hospitals and 263 outpatient clinics within a large health care system.
methodsWe collected metadata on the use of the Epic Systems Secure Chat platform for 6 months (July 2022 to January 2023). Information was retrieved on message volume, response times, message characteristics, messages sent and received by users, user roles, and work settings (inpatient vs outpatient).
resultsA total of 32,881 users sent 9,639,149 messages during the study. Median daily message volume was 53,951 during the first 2 weeks of the study and 69,526 during the last 2 weeks, resulting in an overall increase of 29% (P=.03). Nurses were the most frequent users of secure messaging (3,884,270/9,639,149, 40% messages), followed by physicians (2,387,634/9,639,149, 25% messages), and medical assistants (1,135,577/9,639,149, 12% messages). Daily message frequency varied across users; inpatient advanced practice providers and social workers interacted with the highest number of messages per day (median 19). Conversations were predominantly between 2 users (1,258,036/1,547,879, 81% conversations), with a median of 2 conversational turns and a median response time of 2.4 minutes. The largest proportion of inpatient messages was from nurses to physicians (972,243/4,749,186, 20% messages) and physicians to nurses (606,576/4,749,186, 13% messages), while the largest proportion of outpatient messages was from physicians to nurses (344,048/2,192,488, 16% messages) and medical assistants to other medical assistants (236,694/2,192,488, 11% messages).
conclusionsSecure messaging was widely used by a diverse range of health care professionals, with ongoing growth throughout the study and many users interacting with more than 20 messages per day. The short message response times and high messaging volume observed highlight the interruptive nature of secure messaging, raising questions about its potentially harmful effects on clinician workflow, cognition, and errors.
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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.