[Diagnosis and admission center : Establishment and evaluation of an integrated translational infrastructure for clinical psychiatric research]. / Diagnose- und Aufnahmezentrum : Etablierung und Evaluation einer integrierten translationalen Infrastruktur für die klinisch-psychiatrische Forschung.

ABSTRACT

The routine in-depth characterization of patients with methods of clinical and scale-based examination, neuropsychology, based on biomaterials, and sensor-based information opens up transformative possibilities on the way to personalized diagnostics, treatment and prevention in psychiatry, psychotherapy, and psychosomatics. Effective integration of the additional temporal and logistical effort into everyday care as well as the acceptance by patients are critical to the success of such an approach but there is little evidence on this to date. We report here on the establishment of the Diagnosis and Admission Center (DAZ) at the Central Institute of Mental Health (ZI) in Mannheim. The DAZ is an outpatient unit upstream of other care structures for clinical and scientific phenotyping across diagnoses as a starting point for data-driven, individualized pathways to further treatment, diagnostics or research. We describe the functions, goals, and implementation of the newly created clinical scientific translational structure, provide an overview of the patient populations it has reached, and provide data on its acceptance. In this context, the close integration with downstream clinical processes enables a better coordinated and demand-oriented allocation. In addition, DAZ enables a faster start of disorder-specific diagnostics and treatment. Since its launch in April 2021 up to the end of 2022, 1021 patients underwent psychiatric evaluation at DAZ during a pilot phase. The patient sample corresponded to a representative sample from standard care and the newly established processes were regarded as helpful by patients. In summary, the DAZ uniquely combines the interests and needs of patient with the collection of scientifically relevant data.