I can confirm the same experience too (my company uses both AWS and Google Cloud) where most of the human touch points related to startup credits are with the sales team for Google Cloud instead of an account manager (we still don't have one after about a year now) if you manage to get pass their usual generic responses.
Agree on FHIR APIs are becoming ubiquitous especially with Apple Health pushing its adoption in consumer facing applications, not so much on the legacy enterprise side from what I've seen thus far (I work primarily in the clinical trials side of things) and we prototyped our own integration stack to help with wrangling with different standards (more like the lack thereof). It is worth actually building that ourselves in the early days as we have learned so much about the challenges to integrate with our partners and will at some point looking into interoperability partners as we scale.
The scientific process typically involves making inferences and conclusions based on an observation, whereby the result often include biases and in some cases even ill-constructed hypotheses that are caused by assumptions and imperfect information.
One way to reduce 'fraud' and also increase accountability of the individuals and organizations is to secure raw data at its source which the veracity of the data that will be subsequently used to support downstream conclusions can be easily verified and attributed to its source. I'd say a bottom up approach here is more holistic as it incentivizes people to produce high quality research that future work can then be reliably be built on top of.