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Identifying Barriers to The Implementation of Communicating Narrative Concerns Entered by Registered Nurses, An Early Warning System SmartApp.
Hobensack, Mollie; Withall, Jennifer; Douthit, Brian; Cato, Kenrick; Dykes, Patricia; Cho, Sandy; Lowenthal, Graham; Ivory, Catherine; Yen, Po-Yin; Rossetti, Sarah.
Afiliación
  • Hobensack M; Brookdale Department of Geriatrics and Palliative Medicine, Icahn School of Medicine at Mount Sinai, New York City, New York, United States.
  • Withall J; Department of Biomedical Informatics, Columbia University, New York City, New York, United States.
  • Douthit B; Department of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee, United States.
  • Cato K; Department of Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, United States.
  • Dykes P; Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States.
  • Cho S; Department of Clinical Informatics, Newton-Wellesley Hospital, Newton, Massachusetts, United States.
  • Lowenthal G; Brigham and Women's Hospital, Boston, Massachusetts, United States.
  • Ivory C; Vanderbilt University Medical Center, Nashville, Tennessee, United States.
  • Yen PY; Washington University School of Medicine in St. Louis, St. Louis, MO, United States.
  • Rossetti S; Department of Biomedical Informatics, Columbia University, New York City, New York, United States.
Appl Clin Inform ; 15(2): 295-305, 2024 Mar.
Article en En | MEDLINE | ID: mdl-38631380
ABSTRACT

BACKGROUND:

Nurses are at the frontline of detecting patient deterioration. We developed Communicating Narrative Concerns Entered by Registered Nurses (CONCERN), an early warning system for clinical deterioration that generates a risk prediction score utilizing nursing data. CONCERN was implemented as a randomized clinical trial at two health systems in the Northeastern United States. Following the implementation of CONCERN, our team sought to develop the CONCERN Implementation Toolkit to enable other hospital systems to adopt CONCERN.

OBJECTIVE:

The aim of this study was to identify the optimal resources needed to implement CONCERN and package these resources into the CONCERN Implementation Toolkit to enable the spread of CONCERN to other hospital sites.

METHODS:

To accomplish this aim, we conducted qualitative interviews with nurses, prescribing providers, and information technology experts in two health systems. We recruited participants from July 2022 to January 2023. We conducted thematic analysis guided by the Donabedian model. Based on the results of the thematic analysis, we updated the α version of the CONCERN Implementation Toolkit.

RESULTS:

There was a total of 32 participants included in our study. In total, 12 themes were identified, with four themes mapping to each domain in Donabedian's model (i.e., structure, process, and outcome). Eight new resources were added to the CONCERN Implementation Toolkit.

CONCLUSIONS:

This study validated the α version of the CONCERN Implementation Toolkit. Future studies will focus on returning the results of the Toolkit to the hospital sites to validate the ß version of the CONCERN Implementation Toolkit. As the development of early warning systems continues to increase and clinician workflows evolve, the results of this study will provide considerations for research teams interested in implementing early warning systems in the acute care setting.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Enfermeras y Enfermeros Límite: Humans Idioma: En Revista: Appl Clin Inform Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Enfermeras y Enfermeros Límite: Humans Idioma: En Revista: Appl Clin Inform Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos