JOURNAL ARTICLE

Improving suicide risk screening in the emergency department.

  • Published In: Emergency Nurse, 2024, v. 32, n. 6. P. 21 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Englund, Heather 3 of 3

Abstract

Why you should read this article: • To enhance your awareness and understanding of the Columbia-Suicide Severity Risk Scale • To learn about a project to improve suicide risk screening in three emergency departments in the US • To acknowledge the need to provide nurses with adequate training and tools on suicide risk assessment. Suicide is a significant and increasing public health concern. Research has shown that screening for suicide risk is inconsistent in acute care settings and that a variety of different tools are used for that purpose. The Columbia-Suicide Severity Risk Scale (C-SSRS) has emerged as a validated and recognised suicide risk screening tool. This article describes a quality improvement project designed to improve the screening of patients for suicide risk in a large hospital system in the Midwestern US. As part of the project, 97% of nurses working in the organisation’s emergency departments self-completed a 30-minute interactive learning module on the background, relevance and application of the C-SSRS. The C-SSRS enables nurses to classify the severity of suicide risk, which helps to provide interventions commensurate with patients’ level of risk. Following completion of the module, there was a significant increase in the percentage of patients screened for suicide risk. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Emergency Nurse. 2024/11, Vol. 32, Issue 6, p21
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2024
  • ISSN:1354-5752
  • DOI:10.7748/en.2024.e2198
  • Accession Number:180649038
  • Copyright Statement:Copyright of Emergency Nurse is the property of Royal College of Nursing of the United Kingdom (The) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.