Completely agree with the core argument. A lot of healthcare AI discussions focus on model selection, but production success usually comes down to data quality, interoperability, governance, and compliance.
FHIR is one of those decisions that feels like overhead during MVP development but becomes a major advantage once you start integrating EHRs, devices, patient portals, and third-party systems. Retrofitting interoperability later is usually far more expensive than designing for it upfront.
We've seen a similar pattern at IT Path Solutions when working on AI-powered platforms: the AI layer gets the attention, but the real complexity often sits in the underlying data architecture. Models can be swapped out every few months; rebuilding data foundations is a much bigger undertaking.
The most successful healthcare AI products will likely be the ones that treat interoperability as core infrastructure rather than a compliance checkbox.
Completely agree with the core argument. A lot of healthcare AI discussions focus on model selection, but production success usually comes down to data quality, interoperability, governance, and compliance.
FHIR is one of those decisions that feels like overhead during MVP development but becomes a major advantage once you start integrating EHRs, devices, patient portals, and third-party systems. Retrofitting interoperability later is usually far more expensive than designing for it upfront.
We've seen a similar pattern at IT Path Solutions when working on AI-powered platforms: the AI layer gets the attention, but the real complexity often sits in the underlying data architecture. Models can be swapped out every few months; rebuilding data foundations is a much bigger undertaking.
The most successful healthcare AI products will likely be the ones that treat interoperability as core infrastructure rather than a compliance checkbox.