Identifying Type II workplace violence from clinical notes using natural language processing

Byon, Ha Do; Harris, Catherine R.; Crandall, Mary; Song, Jiyoun; Topaz, Maxim

doi:10.1177/21650799231176078

Cited by 4 publications

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Given that healthcare clinicians verbal and physical violence is under-reported, but the events or patient or visitor behaviors are often recorded in the medical record, such data are potentially abstractable. Natural language processing (NLP) is one tool that may accomplish that goal for the inpatient setting, as it has been demonstrated to identify unreported violent events from the medical record notes of home healthcare nurse visits (65). Because hospital incident reporting systems capture reported events, cues that may predict HV occurrence may be similarly gleaned using NLP (66).…”

Section: Role Of Machine Learning/augmented Intelligence In Violence ...mentioning

confidence: 99%

In-Hospital Violence and Its Impact on Critical Care Practitioners

Bass,

Chang,

Winkle

et al. 2024

Critical Care Medicine

View full text Add to dashboard Cite

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.

show abstract

Section: Role Of Machine Learning/augmented Intelligence In Violence ...mentioning

confidence: 99%

In-Hospital Violence and Its Impact on Critical Care Practitioners

Bass,

Chang,

Winkle

et al. 2024

Critical Care Medicine

View full text Add to dashboard Cite

show abstract

Incorporation of artificial intelligence into nursing research: A scoping review

Yasin,

Al‐Hamad,

Metersky

et al. 2024

International Nursing Review

View full text Add to dashboard Cite

BackgroundThe integration of artificial intelligence (AI) across different sectors, notably healthcare, is on the rise. However, a thorough exploration of AI's incorporation into nursing research, as well as its advantages and obstacles, is still lacking.ObjectiveThe aim of this scoping review was to map the roles, benefits, challenges, and potentials for the future development and use of AI in the context of nursing research.MethodsAn exhaustive search was conducted across seven databases: MEDLINE, PsycINFO, SCOPUS, Web of Science, CINAHL, Google Scholar, and ProQuest. Articles were additionally identified through manual examination of reference lists of the articles that were included in the study. The search criteria were restricted to articles published in English between 2010 and 2023. The Joanna Briggs Institute (JBI) approach for scoping reviews and the PRISMA‐ScR guidelines guided the processes of source selection, data extraction, and data presentation.ResultsTwenty articles met the inclusion criteria, covering topics from ethical considerations to methodological issues and AI's capabilities in data analysis and predictive modeling.ConclusionThe review identified both the potentials and complexities of integrating AI into nursing research. Ethical and legal considerations warrant a coordinated approach from multiple stakeholders.ImplicationThe findings emphasized AI's potential to revolutionize nursing research, underscoring the need for ethical guidelines, equitable access, and AI literacy training to ensure its responsible and inclusive use.

show abstract

Understanding the Experience of Workplace Violence in Hospitals as Documented by Nursing Staff: Using the READ Approach

Brunero,

Tetik,

Donnelly

et al. 2024

Workplace Health Saf

View full text Add to dashboard Cite

Background: Workplace violence has a significant impact on patients, families, and staff safety. Workplace violence can produce traumatic results for those involved; the importance of preventive measures needs to be paramount in health service policy and process. Health care staff are required to document their experiences of violent incidents after every occasion, usually via an incident reporting system, which allows for a free text description of the event. There is a lack of understanding of how health care staff document reports of violence and how they explain the events. Methods: This study aims to determine the circumstances surrounding workplace violent events as documented by health care staff. The four-step Review, Extract, Analyze, and Document (READ) approach to document analysis was used to examine workplace violence incident reports over 12 months (September 2021–September 2022) in a tertiary referral hospital. Findings: Six categories of workplace violence were found: (a) “Escalation Dynamics”—patterns and progression of how violence incidents intensify; (b) “Warning Behaviors”—verbal or non-verbal signals that may foreshadow physical violence if not addressed; (c) “Authoritative Institutional Interventions”—how aggression correlates to protocols involving security personnel or law enforcement; (d) “Care Delivery Methods”—how certain treatment procedures and approaches might inadvertently elicit violence; (e) “Situational Stressors”—external circumstances or changes that act as triggers for violence; and (f) “Unprovoked triggers”—sudden and seemingly unprovoked violent outbursts. Conclusions and application to practice: Workplace violence in health care is a multifaceted interplay of events with the nurse involved in all aspects of the process. These findings can be used by occupational health nurses in education and policy development. The findings can be used to focus education on how violent incidents may escalate and provide more opportunities for de-escalation by health care staff.

show abstract

Identifying Type II workplace violence from clinical notes using natural language processing

Cited by 4 publications

References 20 publications

In-Hospital Violence and Its Impact on Critical Care Practitioners

In-Hospital Violence and Its Impact on Critical Care Practitioners

Incorporation of artificial intelligence into nursing research: A scoping review

Understanding the Experience of Workplace Violence in Hospitals as Documented by Nursing Staff: Using the READ Approach

Contact Info

Product

Resources

About