ehr-advantage-designer

To design an EHR real-world data study, you map parameters like RESEARCH_QUESTION, DRUG, and OUTCOME_INTEREST into structured components that leverage longitudinal narrative notes and lab results, enabling higher fidelity exposure and outcome definitions than claims databases capture.

Yes, you can use clinical progress notes and lab results to define outcomes for an observational study. Translating narrative text and clinical summaries into research-ready elements allows higher fidelity outcome definitions than claims data permits.

The best way to structure EHR-driven clinical research is applying a defined data asset framework and an explicit EHR advantage process. This ensures reproducible extraction pipelines, structured summaries, and robust outcome definitions across conditions.

No, specifying a comparator drug is optional. You must provide a RESEARCH_QUESTION, DRUG, INDICATION, and OUTCOME_INTEREST, while COMPARATOR and SUBGROUPS are optional input parameters for generating the full study design.

Use EHR real-world data over claims data to exploit clinical strengths not available in claims databases. EHR data provides longitudinal text, clinical summaries, and laboratory results that enable research questions claims databases cannot answer.

You ensure reproducible pipelines by requiring explicit frontmatter fields and a defined data asset framework. This structured EHR advantage process governs extraction, structuring, and evaluation of EHR real-world data.