Evaluation of a DIC Broad Consent Cohort

Kampf MO, Prokosch HU, Gulden C, Kraska D, Ganslandt T, Seuchter SA (2025)


Publication Type: Book chapter / Article in edited volumes

Publication year: 2025

Publisher: IOS Press

Edited Volumes: Intelligent Health Systems – From Technology to Data and Knowledge

Series: Studies in Health Technology and Informatics

Book Volume: 327

Pages Range: 492-496

DOI: 10.3233/SHTI250386

Abstract

This single-center retrospective observational study accesses potential differences between adult patients who were admitted to the University Hospital Erlangen between March 2021 and December 2023 (hospital cohort) and adult patients who have given consent to use their documented data for research purposes (broad consent cohort). Demographic and clinical data (ICD-10 diagnoses) were extracted from the university hospitalt's FHIR research data repository and analyzed in pseudonymized form. The two cohorts comprise 98,564 and 1,678 patients respectively and were compared concerning representativity of the BC cohort. The results suggest that the ongoing stepwise rollout of the consent obtainment process creates biases in clinical and demographic characteristics. For as long as these biases persist, we suggest researchers to prefer federated over centralized approaches to data analysis, where broad consent is not required and the analyses can be based on the total hospital cohort.

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How to cite

APA:

Kampf, M.O., Prokosch, H.-U., Gulden, C., Kraska, D., Ganslandt, T., & Seuchter, S.A. (2025). Evaluation of a DIC Broad Consent Cohort. In Elisavet Andrikopoulou, Parisis Gallos, Theodoros N. Arvanitis, Rosalynn Austin, Arriel Benis, Ronald Cornet, Panagiotis Chatzistergos, Alexander Dejaco, Linda Dusseljee-Peute, Alaa Mohasseb, Pantelis Natsiavas, Haythem Nakkas, Philip Scott (Eds.), Intelligent Health Systems – From Technology to Data and Knowledge. (pp. 492-496). IOS Press.

MLA:

Kampf, Marvin O., et al. "Evaluation of a DIC Broad Consent Cohort." Intelligent Health Systems – From Technology to Data and Knowledge. Ed. Elisavet Andrikopoulou, Parisis Gallos, Theodoros N. Arvanitis, Rosalynn Austin, Arriel Benis, Ronald Cornet, Panagiotis Chatzistergos, Alexander Dejaco, Linda Dusseljee-Peute, Alaa Mohasseb, Pantelis Natsiavas, Haythem Nakkas, Philip Scott, IOS Press, 2025. 492-496.

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