Data integration via ETL (exchange, transfer, and load) is the most common, but it is not the most effective or scalable because it is complex and slow. An alternative is data federation. Data federation is a form of data virtualization where data does not leave its source. It is aggregated virtually with an added layer that serves as a common interface. Cisco notes that data federation and virtualization can generate significantly faster analytics and intelligence, while saving as much as 75% compared to data replication and consolidation. Enforced common governance is not necessary. Each source maintains their own governance while adhering to individuals’ privacy and security.

Creating and owning the virtual layer that manages access to data sources requires planning and work. It is easier within an organization. For interorganizational data federation, the role of a trustee or broker may become necessary. That can be a government agency or a non-profit organization. Organizations with valuable datasets can participate. A trustee is an independent party that helps make valuable connections for the benefit of healthcare in general. Access to data is incentivized per use or by the attribution to value each dataset creates.


Locking steps between AI innovation and regulation
Regulators believe AI innovators are moving too fast, and AI innovators think regulators are moving slow. Regardless of who’s right, the pace has to align. The European Union (EU) finally passed the AI Act in December 2023 to regulate the use of AI and manage the risk levels it poses to users. It is the first targeted AI regulation in the world. It was initially proposed in April 2021—that’s over two years of negotiation. Other countries, especially the USA, will follow. But at this pace, AI innovation is always going to be ahead.

Innovators and regulators both agree that there is a responsibility on innovators to support regulations to help advance it and bring it up to speed. When something goes wrong—and it is bound to happen—regulations will become stricter and more limiting. The benefits outweigh the risks now, but one harmful incident, especially in healthcare, can flip the balance. The error rates of AI overall and in specific healthcare diagnosis are less than 5%, which beats that of doctors. However, people are more forgiving of other people than machines.

There is a flood of experimentations with AI. While it is good to test different implementation methods and options, it also creates noise. While technology systems are relatively cheap, AI models and data training is not. Funding will be spread thin. Wider enterprise value-driven guidelines and monitoring may be needed to help support the good ones through and weed the bad ones out.

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