Peer-reviewed article

Decentralized rare disease studies in Germany: first results and hurdles of secondary use of patient data.

Zoch M, Gierschner C, Weidner J, Sedlmayr M, Müller G et al. · 12 Aug 2026

Abstract

Background The research challenges associated with rare diseases is characterized by a scarcity of information as well as reliable data due to their low prevalence. The problem of the "underpowered studies" is stemmed from a small research community, limited study participants and scarce data. The German project "Collaboration on Rare Diseases - Medical Informatics" (CORD-MI), tackles these problems by improving research opportunities and patient care by employing innovative IT solutions for collaborative data use across 20 German university hospitals. One possibility was to conduct decentralized studies based on secondary data. Three studies based on four rare diseases served as examples: (1) Cystic Fibrosis (CF), (2) Phenylketonuria (PKU), and (3) Kawasaki Disease and Multisystem Inflammatory Syndrome in Children (MIS-C). Methods All three decentralized studies were conducted using routine inpatient data from German university hospitals. For each use case, an interdisciplinary team defined research questions, created analysis scripts based on the Core Data Set of the Medical Informatics Initiative (MII), executed these locally at participating sites, and aggregated anonymized results for descriptive statistical analysis. A frequency threshold rule was applied to protect patient privacy. Study protocols and analysis scripts for data extraction and evaluation are publicly available, and the studies were reported in detail in accordance with the Reporting of Studies Conducted Using Observational Routinely-Collected Health Data (RECORD) Statement. Results Results from up to 17 German university hospitals were achieved for all three decentralized studies. The challenges in the areas of health care process bias, inaccurate coding of rare diseases, difficult verification of study results, and loss of information due to masking of small study case numbers as well as imprecise definition of the cohorts are discussed. Conclusions Naming the hurdles enables the identification of areas for improvements, which will be the base for the development of new approaches or adaptations of existing tools and methodologies for the future. Although adaptation would make an impact, the initial results already show that decentralized analyses based on secondary use of patient data can improve research and thus also the care for people with rare diseases. Clinical trial number Not applicable.