FLOWER is a completely virtual, nationwide, real-world observational study to collect, annotate, standardize, and report clinical data for rare diseases. Patients participate in the study by electronic consent (eConsent) and sign a medical records release to permit data collection. Medical records are accessed from institutions directly via eFax or paper fax, online from patient electronic medical record (EMR) portals, direct from DNA/RNA sequencing and molecular profiling vendors, and via electronic health information exchanges. Patients and their treating physicians may also optionally provide medical records. Medical records are received in or converted to electronic/digitized formats (CCDA, FHIR, PDF), sorted by medical record type (clinic visit, in-patient hospital, out-patient clinic, infusion and out-patient pharmacies, etc.) and made machine-readable to support data annotation, full text searches, and natural language processing (NLP) algorithms to further facilitate feature identification.
Alpha-Thalassemia, Beta-Thalassemia, Amyloidosis, Amyotrophic Lateral Sclerosis, Creutzfeld-Jakob Disease, Cystic Fibrosis, Duchenne Muscular Dystrophy, Early-Onset Alzheimer Disease, Ehlers-Danlos Syndrome, Huntington Disease, Gaucher Disease, GM1 Gangliosidosis, Myasthenia Gravis, Pompe Disease, Sickle Cell Disease, Transthyretin Amyloid Cardiomyopathy, Rare Diseases
FLOWER is a completely virtual, nationwide, real-world observational study to collect, annotate, standardize, and report clinical data for rare diseases. Patients participate in the study by electronic consent (eConsent) and sign a medical records release to permit data collection. Medical records are accessed from institutions directly via eFax or paper fax, online from patient electronic medical record (EMR) portals, direct from DNA/RNA sequencing and molecular profiling vendors, and via electronic health information exchanges. Patients and their treating physicians may also optionally provide medical records. Medical records are received in or converted to electronic/digitized formats (CCDA, FHIR, PDF), sorted by medical record type (clinic visit, in-patient hospital, out-patient clinic, infusion and out-patient pharmacies, etc.) and made machine-readable to support data annotation, full text searches, and natural language processing (NLP) algorithms to further facilitate feature identification.
FLOWER: Following Longitudinal Outcomes With Epidemiology for Rare Diseases
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xCures, Los Altos, California, United States, 94022
Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.
For general information about clinical research, read Learn About Studies.
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xCures,
2026-06-10