Protecting Genetic Data
Privacy and security concerns are slowing adoption of genomic testing, in turn limiting its ability to produce insights and better various healthcare procedures and processes.
Clinical trial research is one of the most costly parts of modern healthcare, with each trial valued at $100 million on average, pushing up the cost of developing a single new drug to up to $2.6 billion.
Context & Challenges
The federal government has implemented a plan to spend $4 billion to sequence and study a million people in the US, providing them with results as a participation reward.
This is a response to the vast potential of genome testing to enable better diagnosis, disease prevention, and personalized therapies. Once genomic data is in the hands of researchers, identification, diagnosis and treatment can be dramatically enhanced, driving financial opportunities for all facets of the health industry. Genomic testing allows for precision medicine, which not only improves the level of care provided, but also generates cost savings for health providers. Through genomic, testing providers can circumvent many expensive tests and lead much more efficiently to a pinpointed diagnosis. In fact, one study of 1,000 subjects revealed that practices can save $1,100-1,600 per patient annually by performing genomic testing.
As an indication of its potential, the market has grown rapidly and dramatically, whereby in 2001 the first human genome was sequenced at a cost of $3 billion and today the same process can cost anywhere between $1000-$10,000.
Despite these numbers, genomic testing is seeing slow adoption rates due to cost and privacy concerns. While costs have decreased, the price of full testing is still outside many consumers’ capacities.
Additionally, reimbursement is not always immediately accessible for genomic testing. There is significant variance between coverage plans across states and different providers, with the lack of standardization, making reimbursement difficult.
These economic concerns are backed by growing privacy concerns. As it stands today, companies like 23andme act as middlemen between data owners and buyers, profiting off individual’s data and raising privacy concerns. This creates a cycle whereby the industry is seeing low participation and as a result cannot reach its full potential with only 2% of people tested finding results that assist them in diagnosis or prevention of disease.
Health providers are also not able to use genomic data as efficiently as they could, with results often standing apart from EHRs. Typically, results are reported on paper or scanned PDFs that cannot be clinically integrated into the patient's health record or EHR. Test results are often not translated into a clinical vocabulary that makes them actionable and an understandable part of the patient's overall record.
STRATO addresses cost, privacy, and integration concerns, incentivizing greater and more productive adoption of genomic testing.
- In terms of privacy, STRATO’s transparent and immutable ledger ensures users maintain control over their data, and provides an updated and holistic record of access
- In terms of security, STRATO encrypts data so that it can only be accessed by permissioned parties, and that only the relevant data can be viewed, often leaving more raw and personal data encrypted
- In terms of consumer control, data owners can decide who views their data and how they use it, while also charging a fee in tokens to do so so that for example, pharmaceutical companies cannot use the data for free
- In terms of integration with EHRs and existing systems, STRATO's single source of truth, enterprise data modeling, and scalability allows for communication between systems, automatically integrating with a patient's EHR
Any STRATO insurance solution leverages STRATO's enterprise grade features
RESTful APIs for direct connection of IoT devices such as provider's iPads to the blockchain network
Identity Management, OAuth and SSO capabilities for simplified IoT authorization and user login
Privacy via private chains to keep sensitive data private and control who sees what data
Enterprise Data Modeling for integration into existing data systems, and to ensure genetic data is meaningful and actionable