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Journal Article

Citation

Bass GA, Chang CWJ, Winkle JM, Cecconi M, Kudchadkar SR, Akuamoah-Boateng K, Einav S, Duffy CC, Hidalgo J, Rodriquez-Vega GM, Gandra-d'Almeida AJ, Barletta JF, Kaplan LJ. Crit. Care Med. 2024; ePub(ePub): ePub.

Copyright

(Copyright © 2024, Society of Critical Care Medicine, Publisher Lippincott Williams and Wilkins)

DOI

10.1097/CCM.0000000000006189

PMID

38236075

Abstract

OBJECTIVES: To provide a narrative review of hospital violence (HV) and its impact on critical care clinicians. DATA SOURCES: Detailed search strategy using PubMed and OVID Medline for English language articles describing HV, risk factors, precipitating events, consequences, and mitigation strategies. STUDY SELECTION: Studies that specifically addressed HV involving critical care medicine clinicians or their practice settings were selected. The time frame was limited to the last 15 years to enhance relevance to current practice. DATA EXTRACTION: Relevant descriptions or studies were reviewed, and abstracted data were parsed by setting, clinician type, location, social media events, impact, outcomes, and responses (agency, facility, health system, individual). DATA SYNTHESIS: HV is globally prevalent, especially in complex care environments, and correlates with a variety of factors including ICU stay duration, conflict, and has recently expanded to out-of-hospital occurrences; online violence as well as stalking is increasingly prevalent. An overlap with violent extremism and terrorism that impacts healthcare facilities and clinicians is similarly relevant. A number of approaches can reduce HV occurrence including, most notably, conflict management training, communication initiatives, and visitor flow and access management practices. Rescue training for HV occurrences seems prudent.

CONCLUSIONS: HV is a global problem that impacts clinicians and imperils patient care. Specific initiatives to reduce HV drivers include individual training and system-wide adaptations. Future methods to identify potential perpetrators may leverage machine learning/augmented intelligence approaches.


Language: en

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