A model for building a national, patient-driven database to track contraceptive use in women with rare diseases.
Tatiana JosephyDeena R LoefflerMolly PamEmily Maria GodfreyPublished in: Journal of the American Medical Informatics Association : JAMIA (2021)
Data on the safety and effectiveness of contraception among women with rare diseases are critical and sorely lacking. To fill this gap, we propose a national, patient-driven database that tracks contraceptive safety and effectiveness among women with rare diseases. We built a pilot database focusing on women with cystic fibrosis in 3 phases: (1) database design input from patients and experts, (2) merging of contraceptive survey data with relevant clinical outcomes from the Cystic Fibrosis Foundation Patient Registry (CFFPR), and (3) forming a data guide to facilitate accessible output data. We successfully linked 62 contraceptive survey variables with 362 relevant clinical outcome variables for 150 patients. This pilot represents a breakthrough in linking contraceptive data to disease-specific outcomes and informs how to build a national, patient-driven contraceptive database for women with rare diseases.
Keyphrases
- electronic health record
- end stage renal disease
- case report
- cystic fibrosis
- adverse drug
- big data
- chronic kidney disease
- randomized controlled trial
- ejection fraction
- newly diagnosed
- systematic review
- quality improvement
- emergency department
- machine learning
- metabolic syndrome
- adipose tissue
- skeletal muscle
- clinical trial
- patient reported outcomes
- deep learning
- polycystic ovary syndrome
- glycemic control
- insulin resistance
- pregnancy outcomes
- breast cancer risk