RealWorldDaten

RealWorldDaten are data collected outside the framework of randomized controlled trials, reflecting routine clinical practice and everyday life. Real-World Data (RWD) include electronic health records, administrative claims data, patient registries, and patient-generated information from apps, wearables, or surveys, as well as public health data. When analyzed to draw conclusions about health interventions, Real-World Data are used to produce Real-World Evidence (RWE) about effectiveness, safety, utilization, and outcomes in broader patient populations.

Sources and characteristics: Electronic health records provide clinical encounters and treatments; claims data record billing, procedures, and costs; registries focus on specific diseases or treatments; patient-reported outcomes and digital devices contribute symptoms, quality of life, and activity data. Data quality varies with completeness, accuracy, timeliness, and coding practices. Interoperability and standardization are ongoing challenges, with standards such as HL7 FHIR and, in some contexts, CDISC aiding data linkage and analysis.

Uses: Regulators and payers increasingly rely on RWE to supplement randomized trials, support post-marketing surveillance, and inform decision-making. Researchers employ RWD for observational studies, comparative effectiveness research, and pragmatic trials. Health technology assessment and policy analyses use RWE to evaluate real-world performance and value.

Methodological considerations: Observational designs are susceptible to biases and confounding; appropriate methods include propensity score techniques, regression adjustment, instrumental variables, and causal inference approaches. Data linkage and privacy-preserving techniques enable broader analyses while protecting individuals.

Governance and privacy: Data use is subject to privacy laws such as GDPR, with consent where required, de-identification, and governed data-sharing arrangements. Responsible stewardship, data access controls, and ethical review are integral to RealWorldDaten research.

Challenges: Fragmented data sources, variable data quality, limited representativeness, high costs, and evolving standards impede seamless use of RealWorldDaten in evidence generation.