Dan Masys: Thank you, Heidi.
And my role is to react, so I’m going to react.
All right.
So, my reaction is, I think, framed by what I think is persistent wisdom from Larry Weed,
the father of the electronic medical record -- problem-oriented medical record system.
He said this, actually, in the 1970s; both of these quotes are from the ‘70s.
They are more true now, I think than when he said them: “Modern healthcare is a spectacle
of fragmented intention.”
And the question, really, for this group is: “Is CSER on a pathway to increase the size
of the spectacle of fragmented intention?”
The second thing that Larry Weed said is “We practice healthcare as if we never wrote anything
down.”
Right, so, you know, we actually write millions of things down, but we’ve structured healthcare
as an industry as if we didn’t, so that, perhaps, an individual practitioner might
become better on their own by looking at their own experience.
But we never, as an industry, learn from the collective experience of everything we’ve
done.
So, that frames my reaction, and I apologize for those who have seen this graphic before,
but actually, I wanted to use the graphic to point out that the 200 papers produced
by CSER are down here in the graphic, and the guidelines are not even in the basket.
So, clearly, the method to ensure that you either have decades long delays, or you simply
make no systems-level impact in healthcare is you rely upon clinicians reading and remembering
clinical reports, the published literature, and guidelines.
So, to paraphrase James Bond, the world of all of these acronyms we’re talking about
this morning really is not enough.
And it’s not enough because none of them, at least in my view, would contribute in even
a small way to the goal of self-optimizing systems that learns from every decision event.
And here, of course, you can’t correlate what somebody read with their eyeballs, but
if you had a decision support infrastructure of -- that recognized a situation for which
there was best guidance available and provided the guidance, and then, you had some way to
just track what happened next, you could learn from every decision event whether or not the
user followed the best evidence guidance.
And that ability to do that would contribute both to improved local operations -- so every
practitioners, every clinic, every hospital would be happy -- and it would contribute
to the combined real world experience with genotypes and phenotypes.
So, NIH would be happy, and we would all move ahead.
And so, really, the goal needs to be could CSER be configured so it contributes to a
framework where guidance improves as a by-product of care delivery, specifically, care delivery
not provided by CSER investigators.
You know?
That’s where you get the impact.
So, I had my own idea of how you might do this, and two pieces of it are well known.
That is that we’ve been doing clinical decision report in the world of medical informatics
for over three decades.
And so, the idea of having recognition logic that is keyed off things coming into the electronic
medical records -- that is, elements of phenotype or laboratory results or diagnoses that basically
create a situation where a rule could fire that gives guidance to some kind of target
user; could be a clinician or patient or family.
But by and large, what we have failed to do, and what I believe CSER, to date, and ClinGen,
and other groups have failed to do is then have that rule connected to a downstream decision
support, like recognition logic -- that is, automated recognition logic -- for the correct
thing happening, or a good outcome, or a bad outcome.
And that’s really all we would need.
And that is if we are systematically recognizing the need to evoke clinical decision support,
if we systematically track the outcomes of it, now we can close the loop.
Now, there are some tools that are widely distributed, and even more so with meaningful
use support authoring systems are coming online in large numbers.
We have event monitors that are essential computer programs that just spend their whole
day just watching things happen in the EMR and looking to see whether rules could be
satisfied.
And we do have system-generated alerts at, hopefully, the teachable moment of testing,
therapy, decision making, counseling.
But what we largely lack are the things in the italics.
That is, we then do not downstream to have automated tracking of outcomes versus those
uses and decisions.
And we also lack something that is prominently features in the precision medicine planning
for its cohort, and that is incorporation of patient reported outcomes, and that is
what did the human being for whom this was intended actually experience.
So, an ideal genomic infrastructure would have a public library that somehow would spontaneously
-- that is, without an NIH grant to do it -- would spontaneously incentivize this bidirectional
engagement with healthcare and research organizations, and it would need to be managed by a trusted
organization.
It seems to me that you could have a quid pro quo that if you use a public library’s
resources, you could have both the technology for easily uploading aggregates.
You wouldn’t have HIPA of identification of individuals, but the aggregate local experience
with the use of that decision support infrastructure.
And it is the case that since the last workshop, where this prominently featured -- Genomic
Medicine Seven -- there has been notable progress.
So, the IOM’s digitized project is most of the way through designing and implementing
to pharma-genomics use cases.
eMERGE and Ignite [spelled phonetically] have, in fact, developed the knowledge library -- at
least that first part of the recognition logic and the advice.
I don’t know if there’s a lot of attention to downstream monitoring in an automated fashion.
And also, an Epicory [spelled phonetically] funded project that Guisinger [spelled phonetically]
is doing a comparative effectiveness trial to look at patient facing decision support
to improve patient-provider communication.
So, it’s good that we’re making small steps there, but I think the risk of NIH funding
research consortium that is inwardly directed is losing the opportunity as one makes incremental
progress on expanding the knowledge base, of instrumenting it with -- by building an
engine, at least a prototype of an engine, that if the funding stopped, the engine would
still run.
It would still learn.
It would still get better as a legacy of the project.
So, that’s my challenge and my reaction.
Thank you.
Male Speaker: We’ll decide later whether you deserve that
applause.
[laughter].
[end of transcript]