Background: Polypharmacy is a common clinical issue. It increases in prevalence with older age and comorbidities of patients and has been recognized as a major cause for treatment complications. In psychiatry, polypharmacy is also commonly seen in younger patients and can lead to reduced treatment satisfaction and incompliance. A variety of structured polypharmacy interventions have been investigated. This systematic review provides a comprehensive overview of the field and identifies research gaps. Methods:We conducted a systematic review on structured interventions aimed at optimizing polypharmacy of psychotropic and somatic medication in psychiatric inpatient and outpatient settings as well as nursing homes. A search protocol was registered with PROSPERO (CRD42020187304). Data were synthesized narratively.Results: Fifty-eight studies with a total of 30,554 participants met the inclusion criteria. Interventions were most commonly guided by self-developed or national guidelines, drug assessment scores, and lists of potentially inappropriate medications. Tools to identify underprescribing were less commonly used. Most frequently reported outcomes were quantitative drugrelated measures; clinical outcomes such as falls, hospital admission, cognitive status, and neuropsychiatric symptom severity were reported less commonly. Reduction of polypharmacy and improvement of medication appropriateness were shown by most studies.Conclusions: Improvement of drug-related outcomes can be achieved by interventions such as individualized medication review and educational approaches in psychiatric settings and nursing homes. Changes in clinical outcomes, however, are often nonsubstantial and generally underreported. Patient selection and intervention procedures are highly heterogeneous. Future investigations should establish standards in intervention procedures, identify and assess patient-relevant outcome measures, and consider long-term follow-up assessments.

Background Computational linguistic methodology allows quantification of speech abnormalities in non-affective psychosis. For this patient group, incoherent speech has long been described as a symptom of formal thought disorder. Our study is an interdisciplinary attempt at developing a model of incoherence in non-affective psychosis, informed by computational linguistic methodology as well as psychiatric research, which both conceptualize incoherence as associative loosening. The primary aim of this pilot study was methodological: to validate the model against clinical data and reduce bias in automated coherence analysis. Methods Speech samples were obtained from patients with a diagnosis of schizophrenia or schizoaffective disorder, who were divided into two groups of n = 20 subjects each, based on different clinical ratings of positive formal thought disorder, and n = 20 healthy control subjects. Results Coherence metrics that were automatically derived from interview transcripts significantly predicted clinical ratings of thought disorder. Significant results from multinomial regression analysis revealed that group membership (controls vs. patients with vs. without formal thought disorder) could be predicted based on automated coherence analysis when bias was considered. Further improvement of the regression model was reached by including variables that psychiatric research has shown to inform clinical diagnostics of positive formal thought disorder. Conclusions Automated coherence analysis may capture different features of incoherent speech than clinical ratings of formal thought disorder. Models of incoherence in non-affective psychosis should include automatically derived coherence metrics as well as lexical and syntactic features that influence the comprehensibility of speech.