Surveillance of Health Care-Associated Violence Using Natural Language Processing

• Published in: Pediatrics (Evanston) Vol. 154; no. 2; p. 1
• Main Authors: Waltzman, Mark, Ozonoff, Al, Fournier, Kerri Ann, Welcher, Jennifer, Milliren, Carly, Landschaft, Assaf, Bulis, Jonathan, Kimia, Amir A
• Format: Journal Article
• Language: English
• Published: United States American Academy of Pediatrics 01.08.2024
• Subjects: Aggression; Electronic Health Records; Feasibility Studies; Health care; Hospitals, Community; Hospitals, Pediatric; Humans; Injury, Violence & Poison Prevention; Natural Language Processing; Nursing; Pediatrics; Population Surveillance - methods; Safety; Self-injury; Surveillance; Tertiary Care Centers; Workplace Violence - statistics & numerical data
• ISSN: 0031-4005; 1098-4275; 1098-4275
• DOI: 10.1542/peds.2023-063059

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. 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. 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. 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.