Violence against healthcare workers at the Emergency Department

Hooton, Amy; Bloom, Ben; Backus, Barbra E.

doi:10.1097/mej.0000000000000905

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…An estimated 1.7 million agitation episodes occur annually in EDs across the USA alone 5 6. When an individual becomes agitated, they may cause harm to themselves, hospital staff, and property 7–9. Rapid management of agitation is imperative and use of physical restraint may be necessary to facilitate patient assessment and prevent injury 5.…”

Section: Introductionmentioning

confidence: 99%

“…5 6 When an individual becomes agitated, they may cause harm to themselves, hospital staff, and property. [7][8][9] Rapid management of agitation is imperative and use of physical restraint may be necessary to facilitate patient assessment and prevent injury. 5 Although physical restraints are routinely used in the ED, 10 11 they are associated with up to 37% risk of injury in patients, including blunt chest trauma, asphyxiation, respiratory depression, and sudden death.…”

Section: Introductionmentioning

confidence: 99%

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Formative evaluation of an emergency department clinical decision support system for agitation symptoms: a study protocol

Wong,

Nath,

Shah

et al. 2024

BMJ Open

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Introduction The burden of mental health-related visits to emergency departments (EDs) is growing, and agitation episodes are prevalent with such visits. Best practice guidance from experts recommends early assessment of at-risk populations and pre-emptive intervention using de-escalation techniques to prevent agitation. Time pressure, fluctuating work demands, and other systems-related factors pose challenges to efficient decision-making and adoption of best practice recommendations during an unfolding behavioural crisis. As such, we propose to design, develop and evaluate a computerised clinical decision support (CDS) system, Early Detection and Treatment to Reduce Events with Agitation Tool (ED-TREAT). We aim to identify patients at risk of agitation and guide ED clinicians through appropriate risk assessment and timely interventions to prevent agitation with a goal of minimising restraint use and improving patient experience and outcomes. Methods and analysis This study describes the formative evaluation of the health record embedded CDS tool. Under aim 1, the study will collect qualitative data to design and develop ED-TREAT using a contextual design approach and an iterative user-centred design process. Participants will include potential CDS users, that is, ED physicians, nurses, technicians, as well as patients with lived experience of restraint use for behavioural crisis management during an ED visit. We will use purposive sampling to ensure the full spectrum of perspectives until we reach thematic saturation. Next, under aim 2, the study will conduct a pilot, randomised controlled trial of ED-TREAT at two adult ED sites in a regional health system in the Northeast USA to evaluate the feasibility, fidelity and bedside acceptability of ED-TREAT. We aim to recruit a total of at least 26 eligible subjects under the pilot trial. Ethics and dissemination Ethical approval by the Yale University Human Investigation Committee was obtained in 2021 (HIC# 2000030893 and 2000030906). All participants will provide informed verbal consent prior to being enrolled in the study. Results will be disseminated through publications in open-access, peer-reviewed journals, via scientific presentations or through direct email notifications. Trial registration number NCT04959279; Pre-results.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Formative evaluation of an emergency department clinical decision support system for agitation symptoms: a study protocol

Wong,

Nath,

Shah

et al. 2024

BMJ Open

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Surveillance of Health Care-Associated Violence Using Natural Language Processing

Waltzman,

Ozonoff,

Fournier

et al. 2024

Pediatrics

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BACKGROUND AND OBJECTIVES: Patient and family violent outbursts toward staff, caregivers, or through self-harm, have increased during the ongoing behavioral health crisis. These health care-associated violence (HAV) episodes are likely under-reported. We sought to assess the feasibility of using nursing notes to identify under-reported HAV episodes. METHODS: We extracted nursing notes across inpatient units at 2 hospitals for 2019: a pediatric tertiary care center and a community-based hospital. We used a workflow for narrative data processing using a natural language processing (NLP) assisted manual review process performed by domain experts (a nurse and a physician). We trained the NLP models on the tertiary care center data and validated it on the community hospital data. Finally, we applied these surveillance methods to real-time data for 2022 to assess reporting completeness of new cases. RESULTS: We used 70 981 notes from the tertiary care center for model building and internal validation and 19 332 notes from the community hospital for external validation. The final community hospital model sensitivity was 96.8% (95% CI 90.6% to 100%) and a specificity of 47.1% (39.6% to 54.6%) compared with manual review. We identified 31 HAV episodes in July to December 2022, of which 26 were reportable in accordance with the hospital internal criteria. Only 7 of 26 cases were reported by employees using the self-reporting system, all of which were identified by our surveillance process. CONCLUSIONS: NLP-assisted review is a feasible method for surveillance of under-reported HAV episodes, with implementation and usability that can be achieved even at a low information technology-resourced hospital setting.

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Violence against healthcare workers at the Emergency Department

Cited by 2 publications

References 8 publications

Formative evaluation of an emergency department clinical decision support system for agitation symptoms: a study protocol

Formative evaluation of an emergency department clinical decision support system for agitation symptoms: a study protocol

Surveillance of Health Care-Associated Violence Using Natural Language Processing

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