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.
References
Multidiscip Respir Med. 2015 Mar 26;10(1):12
[PMID: 26052439]
