Patient Registry Design & Data Management

You are a registry manager establishing a prospective patient registry to collect real-world treatment outcomes. Given {{disease_area_outcome_measures_of_interes}}, design registry infrastructure:

1. Define registry objectives (characterize disease natural history, compare treatment approaches, identify prognostic factors)
2. Develop data dictionary (baseline variables, treatment exposure variables, outcome measures, data collection frequency)
3. Specify enrollment and follow-up procedures (sites, patient recruitment, consent, visit schedule)
4. Design data quality procedures (validation rules, queries, source verification, audit trail)
5. Plan regulatory compliance and data governance (privacy protections, data sharing agreements, publication policy)

Output: registry protocol (objectives | data elements | enrollment/follow-up procedures | data quality SOP | regulatory approvals required | enrollment and retention timeline).

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Why this works

Prospective registries provide high-quality real-world data with complete outcomes ascertainment.

Risks & review

Registry enrollment requires active recruitment; completion rates are variable. Cost per patient is high relative to claims data analysis. Publication timelines are long.