EXPLORING THE POTENTIALS OF PATIENT-GENERATED HEALTH DATA FOR THE TREATMENT OF DEPRESSION

Authors

Philipp Reindl-Spanner, TUM School of Computation, Information and Technology, Technical University of MunichFollow
Barbara Prommegger, TUM School of Computation, Information and Technology, Technical University of Munich, Garching, GermanyFollow
Jochen Gensichen, Institute of General Practice and Family Medicine University Hospital of the Ludwig-Maximilians-University, Munich, GermanyFollow
Helmut Krcmar, TUM School of Computation, Information and Technology, Technical University of Munich, Garching, GermanyFollow

Abstract

Patient-generated health data (PGHD) enables healthcare professionals to get deeper insights into patients with depression, thus offering the opportunity to improve their treatment. However, due to the variety and methods for collecting PGHD, not all types are relevant for healthcare professionals in depression care. To identify relevant types of PGHD for the treatment of depression, we conducted a qualitative focus group study with 13 healthcare professionals and follow-up interviews. The study's key findings include relevant identified PGHD concerning their collection effort. In addition, the results show a clear preference for PGHD that both have strong connections to depressive symptoms and use passive collection methods, such as sleep data and activity levels. With this article, we contribute to the usage of PGHD in clinical settings and thus create a better understanding of relevant types of PGHD for the treatment of depression.

Recommended Citation

Reindl-Spanner, Philipp; Prommegger, Barbara; Gensichen, Jochen; and Krcmar, Helmut, "EXPLORING THE POTENTIALS OF PATIENT-GENERATED HEALTH DATA FOR THE TREATMENT OF DEPRESSION" (2023). ECIS 2023 Research Papers. 436.
https://aisel.aisnet.org/ecis2023_rp/436