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AI-assisted systematic literature review, evidence automation and dossier tools extend conventional workflows by embedding machine-learning and rules-based automation into search management, screening, data extraction and documentation and by structuring the transfer of evidence into global value dossiers, health technology assessment submissions and payer communication materials.

Application-accompanying data collection (anwendungsbegleitende Datenerhebung, AbD), is a post-launch evidence-generation mechanism used to reduce clinical uncertainty remaining after regulatory approval.

Recent reforms under the Medical Research Act (Medizinforschungsgesetz, MFG) are intended to strengthen Germany as a location for clinical research and early launches.

Meta-analysis and indirect comparison methods translate multiple clinical trials into a coherent view of how treatments compare. They sit between systematic literature reviews, which identify and describe the evidence, and economic modelling and value communication, which use the comparative estimates.

For the submission of medicines in NICE’s Single Technology Appraisal (STA) process, the clinical evidence section is central. Demonstration of the medicine’s effectiveness in relation to its use in the NHS allows NICE to assess the benefits and risks of the technology compared to standard treatment options currently offered. The submission must include robust clinical data, following NICE’s guidelines on the types and quality of evidence.

Systematic literature reviews sit at the centre of clinical evidence development for access, bridging trial design, indirect comparisons and real-world evidence within a single, transparent evidence base, providing the structured evidence foundation on which network meta-analyses, indirect treatment comparisons, economic models and global value dossiers are built.