Healthcare organizations and researchers have long been imagining a day when the promise of precision medicine is realized—where we are able to detect the onset of disease at its earliest stages, screen for rare genetic markers, and maximize drug efficacy, while at the same time increasing the efficiency of the healthcare system and still improve quality, accessibility, and affordability.
Whole-genome sequencing and other omic technologies generate massive amounts of data. The sheer size of “omics” data makes finding genetic and molecular variants related to a disease a daunting task. When combined with clinical information, the problem of isolating an appropriate drug treatment plan becomes even more complex. The PHEMI Central Big Data Warehouse can simplify and scale the problem of genetically-based medicine to better disease prevention and treatment.
PHEMI Central controls rightful access to data and automatically enforces data sharing agreements. Consent directives can be explicitly linked to individual patient data and automatically enforced with fine-grained access control rules.
Depending on the user’s authorization and how data sensitivity is tagged, PHEMI Central automatically hides, de-identifies, masks, or shows individual data elements, controlling what users can view and export. PHEMI Central aligns with appropriate privacy and security requirements, including Health Insurance Portability and Accountability Act (HIPAA) and Health Information Technology for Economic and Clinical Health (HITECH) Act as well as Canadian federal and provincial legislations.
With a solution based on the PHEMI Central Big Data Warehouse, healthcare organizations can realize visions in:
- Pharmacogenomics, integrating a rules base to help flag drug-gene interactions for drug safety, dose, and efficacy in cancer and other diseases.
- Precision medicine (personalized medicine), using whole genome or exome sequencing information to recommend a drug therapy for treatment and prevention of cancer, rare genetic diseases, and infectious diseases.
- Biomarker discovery, integrating and collaborating using de-identified genotype and phenotype information, including lab results and pathology reports, to find biomarkers pointing to novel clinically relevant disease subtypes with different prognoses and responses to therapy.
- Detecting, classifying, and understanding microbes, collecting microbiome data and comparing it against a library of known samples to study infectious diseases, methods of transmission, and drug resistance. High-throughput sequencing can help model the source of an outbreak, the strain involved, and the treatment plan.
- Disease screening and diagnosis, storing and analyzing diagnostic whole genome and exome sequencing data and lab information system logs for quality, research, and analytics purposes.
For the first time, pharmaceutical researchers, bioinformaticians, researchers, and health organizations needing to protect and govern the use of their information can take advantage of big data technology to access, catalog, and analyze their digital assets at speed and scale.