Innovative technologies come to the market every day that promise to solve some of healthcare’s biggest problems. Unfortunately, many are abandoned before they have a chance to fulfill their potential. Often, the reason is that it’s too difficult to navigate our complex healthcare system in a way that makes collaboration, development, and deployment cost effective. The effort required just to connect to the right stakeholders is immense.
An example of such an innovation that holds enormous, industry changing potential is Generative AI (GenAI). GenAI is a specific type of artificial intelligence that generates text, images, and other modalities in response to prompts. The technology grows in value with each use by discerning patterns and applying deep-learning algorithms based on existing data to create new content.
In healthcare, the potential of GenAI is tremendous. From generating rich clinical notes after an encounter, to better and faster disease diagnoses and outcomes, to assisting with drug discovery, GenAI holds great promise for advancing healthcare and patient experiences in ways never imagined just a few years ago.
Regardless of the potential benefit of GenAI, there still exists the same roadblock to realizing its full value as innovators and entrepreneurs have faced for years—the inability to deploy new technologies in a way that is cost efficient, timely, and easy to scale. Healthcare systems have invested heavily in legacy systems that don’t typically play well with new technologies. Bolt-on solutions and multiple data interfaces are required just to share information with other providers and payers. And managing these systems takes a significant portion of most health systems’ IT budgets, leaving little room or resources to cost-effectively integrate GenAI solutions.
Today, the primary model for collecting, processing, and training data for GenAI models is centralized data aggregation, which comes with significant privacy and security risks. This approach is especially problematic when proprietary or sensitive data is used, and it requires considerable safeguard measures during model training and solution deployment. Unfortunately, this can limit the collective value of GenAI solutions. This approach also provides little to no transparency of how the data is used to train the models or how the generated data is eventually shared with users.
All these challenges can be addressed, however, through a decentralized network. A decentralized network can be a powerful GenAI enablement platform to accelerate adoption. It works by enabling:
Initial industry use cases for a GenAI-enabling network can help physicians with clinical documentation, such as transforming conversations into prescriptions, follow-up appointment letters, and consultation summaries. However, the potential use cases are practically limitless.
GenAI has the potential to genuinely transform our industry by enhancing patient experiences, improving outcomes, and significantly reducing clinical and administrative inefficiencies. But fully realizing these benefits requires a new kind of network through which these innovative technologies can be leveraged. That network is here today.
Avaneer Health is a secure, permissioned, decentralized network and platform built on a data fabric infrastructure. Once a participating payer or provider connects to the network, they never have to build a direct connection to any other participant. Data remains decentralized, and participants can control how and with whom they collaborate.
Through a permissioned process, their data can be shared with anyone on the network whom they have approved to receive it. Once the connection is established, data can flow freely in real time, eliminating interoperability barriers and allowing true data fluidity.
Learn how the Avaneer Health network and platform enables generative AI, large machine learning, and the development of models.
