This paper is in the following e-collection/theme issue:

RCTs - Protocols/Proposals (eHealth) (541) Clinical Information and Decision Making (1411) Decision Support for Health Professionals (1191) Nursing (99) Nursing in a Hospital Setting (102) Machine Learning (1471) Natural Language Processing (693) Safety and Error Prevention in Health (204) Quality Improvement (254)

Published on in Vol 10, No 12 (2021): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/30238, first published .
The Communicating Narrative Concerns Entered by Registered Nurses (CONCERN) Clinical Decision Support Early Warning System: Protocol for a Cluster Randomized Pragmatic Clinical Trial

The Communicating Narrative Concerns Entered by Registered Nurses (CONCERN) Clinical Decision Support Early Warning System: Protocol for a Cluster Randomized Pragmatic Clinical Trial

The Communicating Narrative Concerns Entered by Registered Nurses (CONCERN) Clinical Decision Support Early Warning System: Protocol for a Cluster Randomized Pragmatic Clinical Trial

Authors of this article:

Sarah Collins Rossetti1, 2 Author Orcid Image ;   Patricia C Dykes3, 4 Author Orcid Image ;   Christopher Knaplund1 Author Orcid Image ;   Min-Jeoung Kang3, 4 Author Orcid Image ;   Kumiko Schnock3, 4 Author Orcid Image ;   Jose Pedro Garcia Jr3 Author Orcid Image ;   Li-Heng Fu1 Author Orcid Image ;   Frank Chang3 Author Orcid Image ;   Tien Thai3 Author Orcid Image ;   Matthew Fred5 Author Orcid Image ;   Tom Z Korach3, 4 Author Orcid Image ;   Li Zhou3, 4 Author Orcid Image ;   Jeffrey G Klann4 Author Orcid Image ;   David Albers1, 6 Author Orcid Image ;   Jessica Schwartz2 Author Orcid Image ;   Graham Lowenthal3 Author Orcid Image ;   Haomiao Jia2 Author Orcid Image ;   Fang Liu1 Author Orcid Image ;   Kenrick Cato2 Author Orcid Image
Article Authors Cited by (9) Tweetations (5) Metrics

Journals

  1. Hobensack M, Song J, Scharp D, Bowles K, Topaz M. Machine learning applied to electronic health record data in home healthcare: A scoping review. International Journal of Medical Informatics 2023;170:104978 View
  2. Scharp D, Hobensack M, Davoudi A, Topaz M. Natural Language Processing Applied to Clinical Documentation in Post-acute Care Settings: A Scoping Review. Journal of the American Medical Directors Association 2024;25(1):69 View
  3. Hobensack M, Withall J, Douthit B, Cato K, Dykes P, Cho S, Lowenthal G, Ivory C, Yen P, Rossetti S. Identifying Barriers to The Implementation of Communicating Narrative Concerns Entered by Registered Nurses, An Early Warning System SmartApp. Applied Clinical Informatics 2024;15(02):295 View
  4. Van Bulck L, Reading Turchioe M, Topaz M, Song J. Exploring the full potential of the electronic health record: the application of natural language processing for clinical practice. European Journal of Cardiovascular Nursing 2024 View
  5. Zhang Y, Huang Y, Rosen A, Jiang L, McCarty M, RoyChoudhury A, Han J, Wright A, Ancker J, Steel P, Chaurasia A. Aspiring to clinical significance: Insights from developing and evaluating a machine learning model to predict emergency department return visit admissions. PLOS Digital Health 2024;3(9):e0000606 View
  6. Kang M, Rossetti S, Lowenthal G, Knaplund C, Zhou L, Schnock K, Cato K, Dykes P. Designing and testing clinical simulations of an early warning system for implementation in acute care settings. JAMIA Open 2024;7(4) View
  7. Kim M, Kim Y, Choi M. Intensive care unit nurses’ experiences of nursing concerns, activities, and documentation on patient deterioration: A focus-group study. Australian Critical Care 2024:101126 View

Books/Policy Documents

  1. Tiase V, Hobensack M. The Future of Nursing 2020-2030: Global Applications to Advance Health Equity. View
  2. Bokhari S, Krstovski K, Withall J, Lee R, Dykes P, Tran M, Cato K, Rossetti S. Pervasive Computing Technologies for Healthcare. View