Real-world data

Real-world data

The value of real-world evidence depends on how consistently and comprehensively the underlying information is collected. Administrative claims and electronic medical records are useful, but they were not designed to provide a uniform, symptom-level assessment of every participant.

EVALRI uses Ad-Infer to conduct adaptive, structured interviews in which each question is selected from the information already provided. The same clinical definitions, follow-up logic, and internal consistency checks are applied across participants, sites, languages, and assessment waves.

This approach supports detailed phenotyping of sleep, mental health, neurologic, medical, behavioral, and functional domains. It has been used in census-based representative samples and longitudinal cohorts in North America, Europe, and Asia.

The result is not simply a larger dataset. It is structured evidence in which symptoms, diagnoses, trajectories, treatments, and outcomes can be examined together while preserving a transparent link to the questions and clinical rules from which they were derived.

The current Ad-Infer architecture integrates an LLM as an internal functional component. Its questions and semantic interpretation are directed by clinical hypotheses; responses and interaction evidence return to EVAL-KBS for causal re-evaluation, fuzzy processing and belief updating.