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Integration of real-world clinical data into the Munich Mental Health Biobank – clinical and scientific potential and challenges

Published online by Cambridge University Press:  01 September 2022

J. Kálmán ,
G. Burkhardt ,
O. Pogarell ,
F. Padberg ,
T. Schulze  and
P. Falkai
J. Kálmán*
Affiliation:
University Hospital, LMU Munich, Department Of Psychiatry And Psychotherapy, Munich, Germany
G. Burkhardt
Affiliation:
University Hospital, LMU Munich, Department Of Psychiatry And Psychotherapy, Munich, Germany
O. Pogarell
Affiliation:
University Hospital, LMU Munich, Department Of Psychiatry And Psychotherapy, Munich, Germany
F. Padberg
Affiliation:
University Hospital, LMU Munich, Department Of Psychiatry And Psychotherapy, Munich, Germany
T. Schulze
Affiliation:
University Hospital, LMU Munich, Institute Of Psychiatric Phenomics And Genomics, Munich, Germany
P. Falkai
Affiliation:
University Hospital, LMU Munich, Department Of Psychiatry And Psychotherapy, Munich, Germany
*
*Corresponding author.

Abstract

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Introduction

New insights into the pathophysiology of mental disorders and innovations in psychiatric care depend on the availability of representative, longitudinal and multidimensional datasets across diverse, transdiagnostic populations. Biobanks usually attempt to collect such data in parallel to clinical routine, which is resource-intensive, puts additional burden on health-care providers, and may reduce the generalizability of the results. Despite containing rich phenotypic and biological information, data generated in routine clinical care is seldomly used for research purposes, because it is usually unstructured and locked in data silos. To truly link clinical practice and research, solutions that optimize the generation and scientific utilization of real-world clinical data are needed.

Objectives

Evaluation of a new digital infrastructure which warrants the efficient, automatized, and structured collection of real-world data in psychiatric care, and integrates the generated data into existing biobanking efforts.

Methods

We have developed a new documentation system which augments the existing IT-structures, enables the collection of routine clinical data in a structured format and involves patients in the data generation process. In an implementation science approach, to replicate and extend the findings of Blitz et al. (JMIR Ment Health 2021), we are investigating the acceptance, efficacy, and safety of the system in our outpatient clinic for affective disorders.

Results

First results describing the technical safety, usage metrics, and acceptance of the system, and the quality of the collected data will be presented.

Conclusions

Challenges of collecting real-world data for biobanking and research purposes and perspectives on future digital solutions will be discussed.

Disclosure

No significant relationships.

Keywords

digitalizationbiobankreal-world dataaffective disorders
Type
Abstract
Information
European Psychiatry , Volume 65 , Special Issue S1: Abstracts of the 30th European Congress of Psychiatry , June 2022 , pp. S568
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of the European Psychiatric Association
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