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Updated: Jun 10, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
[Profiling involuntarily hospitalized patients according to risk to themselves and/or risk to others]
Franziska Abelein1, Sophia Kabuya-Mulamba1, Uwe Enders1
1Bezirkskliniken Mittelfranken, Psychiatrie, Psychotherapie und Psychosomatik, Germany, Ansbach.
Objectives:
Emergency situations can lead to immediate admissions according to mental health act. It has never been investigated within the scope of the Bavarian Mental Health Act, which differences might be associated with specific risk constellations. We therefore grouped patients according to admission due to a risk to themselves, a risk to others, or a combination of both and analysed epidemiological and clinical properties.
Methodology:
We used case-based, pseudonymized data from the large hospital in Ansbach and described differences in terms of age, gender, diagnoses, coercive measures, and mode of discharge.
Results:
2,153 involuntary admissions accounted for 16.8% of all inpatient cases (n=12,787) from 2021 to 2023. Self-harm was significantly more frequently associated with alcohol addiction, depressive episodes, acute stress reactions, and personality disorders, and more often resulted in discharge the following day. Risk to others was, at the same average age, more frequently associated with male gender, diagnoses within the addiction spectrum involving cannabinoids and stimulants, as well as within the spectrum of schizophrenic psychoses and manic episodes. Coercive measures and prolonged involuntary hospitalizations occurred more frequently. The group with a combination of self-harm and harm to others was used as a means of validating the comparison between the two groups.
Conclusions:
These distinct characteristics enable specific preventive and therapeutic measures to be implemented both before admission, as well as during, and after inpatient treatment. The results of the pseudonymized analysis confirm the added value of moving away from strictly anonymized data collection at the Bavarian state level.
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