As the push to integrate artificial intelligence and increase interoperability evolves, Clinical Architecture sees a dire need for tools that can assess the quality of healthcare data. Poor quality data can lead to incorrect conclusions and wasted resources, hindering the progress of medical research, misguided policy decisions and investments, and ineffective and inequitable care by payers, providers, investors, and the government.

The infrastructure to support interoperability through initiatives such as TEFCA and the number of QHINs continues to grow, but the quality of the data, even structured data, varies widely. There needs to be a way to assess the data flowing through these pipes.

The stakes in healthcare could not be higher. For AI tools to be widely adopted in healthcare, it

See Full Page