So, if you're middle-aged and you're starting to discover the difference
between the very low risk associated with bad cholesterol (LDL) and the very
high risk associated with insulin resistance and the fact that the medical
standards are still written for LDL and cholesterol and, unfortunately, your
Doc is about, maybe 10 years behind or more if he's not focusing on this. You're
trying to get help and you're not getting help from your Doc. Don't beat up
on your doc too much. Yeah, again, I've been there. I've done that. I've lived
that life and it's not easy being a Doc. There are a few things that get in the
way. Maybe, the top 2 things that keep your Doc from helping you are - Number 1
Insurance and No. 2, medical records. So, I'm going for those of you who want a
bottom line. Here's the thing - I'm gonna talk about artificial intelligence.
There's an article here in Harvard Business Review that reminded me about
this issue again and I'll go into some details about artificial intelligence
and helping your Doc. So ask your doctor if he's actually using any artificial
intelligence. At this point, see what kind of look you get. So Harvard Business
Review (HBR) is saying there are things that are starting to be done with
artificial intelligence to improve medical records. Before we talk about
that, let me go back and just make a couple of comments about those 2
things that keep your doctor from helping you. Remember we talked about
insulin resistance and your Doc not being aware. There's a couple of reasons
why. Again insurance and - and health - electronic health records with insurance.
The insurance companies are basically set up to pay for Docs to do the
standards of medicine. Standards are a good thing but, remember, look at the
search. It typically takes about 20 years between the time that something is
discovered and confirmed in science and added to the standard and that's even in
the digital age. You used to take over a hundred years, two hundred years. So
there's still a massive delay between stuff getting into the science - getting
proven and getting into the standards. Most recently, you saw that with
antibiotic treatment for ulcer disease. It took, again, well over 20 years for
that to happen. Now, so that's what insurance companies
do. They slow the doc down to that least common denominator - standards of care.
Number 2. Now, I'm not saying standards are bad. Gosh, you know we've - the problem
we've had in the past was the doctors weren't even keeping up with the
standards. Now, we need better ways of speeding our standards up to keep up
with the science and what you see on this channel is much more of the science
edge, not the standards of Medicine edge, but we talked about another thing
getting in the way - keeping your Doc from spending time with you - finding out
what's going on with you, helping you deal with with your - with your problems.
Oh, again, medical records and it's
interesting that some of the most important information and - and important
tools in something like medicine come from outside artificial intelligence.
Again, will be one of them. Now, for those of you who want to get a little bit more
into detail on it, we'll do that. The reason I showed you this this app: K
health, I'm working with the group that's applying artificial intelligence in
multiple areas which we'll talk about in this HBR article and I hope to open - I
have plans to open my own practice again, seeing new patients using this
artificial intelligence. So, let's go back to the HBR article - Using AI (Artificial
Intelligence) to improve electronic records. Now, first of all - why are health
records such a problem? I remember I've gotten bloody noses in many corporate
battles because I came on with companies like Premise - the predecessors, CHS,
Meridian - and they were so proud of what they've done with their medical records.
My perspective was "Yeah, you've got a medical director, a medical record, and
here's the thing - it's incrementally better but they are
all bad." After a couple of years of getting my nose bloodied in these
corporate battles, I finally had Stuart my friend and CEO saying the same - saying
the same thing - that tough message that medical records are a "pick your poison".
They had to do with design and they have to do with being similar to insurance:
over regulated. So your Docs are just way over regulated. They are way too - they
function with tools that are way too complicated. When somebody said once,
there's a quote about design and it says something like the - "You know you've
gotten the best design when you can't find anything else to take out" In other
words, the best design is clearly the simplest. EMRs electronic health records
or medical records or anything but simple but there is hope and changes are
happening and that's what this - that's what this article is about. Basically,
they start off talking about how we're starting to apply context knowledge,
administrative process knowledge, procedure knowledge, and artificial
intelligence in these. Now, they go on to list - there are a couple of companies
that are actually writing their own. One medical wrote their own system. I've
worked with a couple of companies that that were very proud and excited with
the fact that they wrote their own medical system as - as well. Medical record
system. Those of you who read a lot, Atul Gawande, the - I believe he
wrote the checklist manifesto as a surgeon and author - well-known, who helps
look at and criticized, and helps move ahead the standards of care in - in health
care. He says the same thing, you know, the systems are just way too complex -way too
difficult to navigate and, again, the outcome is - you go to see your doctor and
you get 4 minutes of time and it's time which doesn't include the credit
the - the key current science. So let's go over 3 things that - that they talked
about using. Adding data extraction from free text. Athena
healthcare is starting to do it. One medical, again, is starting to do it. Flatiron
Health is starting to do it and, again, my friends - they are starting to do it.
Diagnostic and predictive algorithms - that's actually more of the sweet spot
for K. They're using diagnostic and predictive algorithms but, again, based on
not so much what some expert Doc said in the past, but what the Machine is
learning on a regular basis with current
experience with tens of thousands of Docs and millions of patients. So, again,
it's that - I did a video recently comparing this to what you see with
Deep Blue and Gary Kasparov. How it finally took over chess, and there's no -
no human in the world that can think as well as a computer. At this point,
another thing that they're talking about is adding clinical documentation and
data entry, and then again clinical decision support. They mention IBM Watson.
You - if you fly very much, you see a lot of ads around the airports for IBM
Watson. Watson is a - they're basically starting to apply that artificial
intelligence. That Deep Blue concept to many - many
areas of Industry and they need to start applying it to - to medicine. That's what
you're hearing here in this article. Firms like Epic, Cerner, Allscripts, Athena -
all are looking. They're the usual suspects, in terms of electronic health records.
They're all starting to head this way and, as I said, I'll be going there too
and I hope it hope it will allow me to expand my impact - expand access to
state-of-the-art prevention for you and for others. So, thank you with - with your
patience, as we go through the changes that we're going through in medicine, and
thank you for your interest, if you've made it this far in the video.