You should check out doing an Aerobic Threshold (AeT) test and an Anaerobic Threshold (AnT) test. I believe there are details on the Uphill Athlete site. But you should absolutely determine your AeT if you’re trying to base build in zone 2 (180-age is a good place to start, but it is likely lower). AnT is useful as well for higher zone training. It’s a more demanding test than AeT but will give you a more practical upper bound for speed workouts than max HR.
This is definitely true for cloud retail prices. However, this becomes not true in cases I've seen when there is an existing discount. Reserved instances, for example.
Just read the MBA/Founder books instead of getting an MBA. Find a new job where you'll be challenged and can put the MBA/Founder skills to use, then quit your current job.
FHIR provides a structure for communicating these data, but does not solve the hard problem in the parent comment about the massive heterogeneity of health care data across institutions (and even within institutions). The same messiness of the original raw data in the proprietary EHR data format can be replicated in FHIR. But at least the mess is available then in an open data format and a bit more regimented on canonical elements (i.e. things like effectiveDate/DateTime).
Have you used HAPI FHIR in production at any scale? Our experience is that it completely falls over under any reasonable strain. Try working with a few thousands patents and ~100M Observations and you will see HAPI work very poorly. It's great that HAPI is very feature rich on the API side--they've implemented most of the spec. But JPA and how they're using it is not a healthcare-scale technology. Google's (Spanner based) and Microsoft's (CosmosDB & SQL Sever based) FHIR API are better at scale but have far fewer features. I'm actually ok with that because the FHIR spec's query functionality is actually much more limited. Google's BigQuery integration really helps here. I'm interested to see where AWS is going with Health Lake. In talking with them I believe they have the best paradigm for their technical implementation.
It is a shame that the open source FHIR servers haven't been great. I think they will probably improve once a scalable paradigm for working with FHIR emerges.
We'll, I tend to silo phi per client to provide for auditable data segregation, so I don't try to scale HAPI up vertically.
FHIR is nice in that you can do uri pointers to external FHIR systems for resources, so it's possible to build up a pretty complex care team and access relationships that way.
I was going down this path and had a lot of it built out, but we ended up changing direction so I never saw it all the way through.
I think a lot of the comments here are missing the point, and the author doesn't help by putting Zuck/Musk/Gaga at the top.
It seems to me the author's point is: Why do people who reach a certain level of wealth keep grinding away at jobs they may not like? For the sake of argument, lets exclude those who shift and work on what they care about. I think he's talking about those who do not necessarily love what they do. This group would be millionaires, and using his numbers are the top 0.005% globally (40M/7B). His answer is that they continue to strive for money because they look towards the next class above them and to provide for their children.
I agree with the author in that people are seeking higher and higher status, and I agree with a peer comment that people get used to luxury. But I would add that that this striving for climbing the class ladder is both endemic in the culture of the US as well as accessible. In many other places in the world there are stricter controls on upward class mobility. I'd posit that people continue on in this because they don't really create a philosophy of life or pay that much attention to how to live until they are much older. It's easy to just keep doing the same thing, especially if it provides a luxurious life. It is a subjective value statement on what to do when you reach that point that is clearly different for everyone.
