Narrative case notes have the potential to predict seclusion 3 days in advance: A mixed-method analysis

Abstract

Objectives: Current risk assessment tools can predict problematic behavior and the need for coercive measures, but only with a moderate level of accuracy. The aim of this study was to assess antecedents and triggers of seclusion. Methods: Narrative notes of health care professionals on psychiatric inpatients were analyzed daily starting 3 days prior to seclusion in the case group (n = 26) and compared to a matched control group without seclusion (n = 26) by use of quantitative and qualitative research methods, based on qualitative content analysis. Results: Quantitative measures showed more aggression in the case group with highly significant differences between the groups (p < 0.001) at all measurement times. Seclusion was significantly associated with the total word count of the narrative notes. Subjective emotional expressions by staff were more apparent before seclusion (p = 0.003). Most frequently, subjective expressions regarding “arduous/provocative” (p < 0.001) and “anxious” (p = 0.010) sentiments could be identified in the case group. Description of patients' behavior in the case group included more negatively assessed terms (p = 0.001). Moreover, sleep loss, refusing medication, high contact frequency, demanding behavior and denied requests were present in a significantly higher frequency before seclusion. Expressions like “threatening” (p = 0.001) were found only before seclusion and appeared to have the function of personal risk assessment. The expression “manageable” (p = 0.035) appeared often in difficult situations that could still be handled. Conclusion: Several factors preceding seclusion could be identified. Narrative notes of staff already showed differences 3 days before the escalation. Particularly the word count, the analysis of terms describing patients' behavior, subjective expressions of staff, and terms used as a function of personal risk assessment could help to provide better predictions of aggressive incidents and to prevent coercive measures.