- Anthem and doc.ai announced August 1 the release of their collaborative healthcare artificial intelligence (AI) data trial on blockchain.
The trial begins this month and will span the next 12 months. It will focus on whether it’s possible to use AI to predict when patients will experience allergies. The trial will employ a framework developed by Harvard Medical School that will leverage machine learning to discover predictive models for allergies.
These models are based on physical and hereditary characteristics, location and exposure to weather, and physical activity. Participants will provide personal health insights via blockchain that will then be analyzed and pieced together by AI.
“Anthem is focused on the safe and responsible use of artificial intelligence and emerging technologies to create a better healthcare future for all Americans,” Anthem President and CEO Gail K. Boudreaux said in a statement. “We are pleased to partner with doc.ai on this innovative study that can have near-term benefit for our employees and, longer-term, the potential to redefine how we treat disease and manage chronic medical conditions to achieve better personalized health outcomes.”
Using blockchain in healthcare is relatively new. Currently, healthcare blockchain is mostly financially focused, while organizations are working to make blockchain effective for other aspects of healthcare such as population health.
“Any initiative in healthcare using AI needs scale to succeed. We are very excited to welcome Anthem as our partner in supporting and using technology to enable individuals to collect and own their health data while empowering data scientists using deep learning to collaborate with consumers, doctors and researchers to find personalized healthcare solutions,” said doc.ai Co-founder Walter De Brouwer.
Adding an AI layer to distributed ledger technology can give patients more control over their data and enable collaboration with clinicians to take predictive analytics into consideration.
IBM has also previously expressed interest in combining blockchain and AI technologies. IBM Watson collaborated with the CDC late last year to join AI and blockchain.
“Blockchain is very useful when there are so many actors in the system,” IBM Watson Health Chief Science Officer Shahram Ebadollahi said in a statement. “It enables the ecosystem of data in healthcare to have more fluidity, and AI allows us to extract insights from the data.”
As healthcare organizations collect more data, it becomes difficult to sort through it all without the help of AI. More data is being shared with blockchain, which drives the need for the technology to process data quickly so it can be used to give insight into a patient’s condition or to give additional insight for population health purposes.
Healthcare organizations that are considering adopting blockchain should have use a layered approach to implement the technology. The HIMSS Blockchain Workgroup has been researching how to realistically implement blockchain in healthcare over the past year.
HIMSS suggested a four-layer approach to build up blockchain infrastructure. Layer 1 enables secure sharing of health data across B2B networks. Layer 2 introduces smart contracts to increase automation to improve efficiency of transactions. Layer 3 adds cryptocurrencies and tokens to enable new commerce and incentive systems.
Finally, Layer 4 uses AI and machine learning to reduce costs and improve care. Sharing data among different healthcare organizations such as scans and other medical images can be shared securely via blockchain and examined by AI. Clinicians will have access to more information faster resulting in increased accuracy in diagnoses at the point of care and less return visits.
As healthcare organizations look to leverage data entered by patients or collected by connected medical devices, technology needs to be implements so that data can be processed and analyzed. If trials bringing blockchain and AI together continue to be developed and tested, organizations may be able to leverage the technology and apply it to population health and other data intensive initiatives.