Kensaku Kawamoto: Well, this will be about 11 minutes.
So, thanks.
I'm Ken Kawamoto.
I'm from the University of Utah.
I was recently recruited there to direct the Operational Clinical Decision Support and
Knowledge Management, obviously not just for personalized medicine, but for all traditional
medicine at the health system.
And I'm also a faculty member in biomedical informatics.
So, before I begin, I just need to give a few disclosures.
I am or have recently been a consultant to partners in Flexion and RAND.
Also, was formerly a consultant for Relegent and formerly a co-owner and consultant for
a company called Clinica that's in the space.
I have no financial competing interests related to OpenCDS, which is the open source technology
I'll be discussing today.
So, today, my objectives are to, first, describe what clinical decision support, or CDS, is.
I think this is an audience that probably is familiar with the term but may need a little
bit more information on it.
And I want to specifically talk about how I can help bridge the translation gap for
personalized medicine.
And then I'm going to identify requirements for scaling decision support for personalized
medicine on a national scale and then describe an open source standard decision support resource
known as OpenCDS that through, actually, funding from the NHGRI through a career development
award, I was able to start for enabling decision support for personalized medicine.
So, I'm going to begin with a definition.
So, clinical decision support can be defined as the provision of pertinent knowledge and/or
person-specific information to clinical decision makers such as patients and clinicians to
enhance health and health care.
And by supporting evidence-based decision making, clinical decision support can support
the translation of the evidence into practice.
So, for example, when we looked at clinical decision support systems that have been evaluated
through randomized control trials, we found that when they provide patient-specific care
recommendations that are automatically provided within clinical workflows, they significantly
improve care quality in over 90 percent of randomized control trials.
So just to give you a sense of what people typically mean by decision support in this
field, here's an example, a disease management dash board that I created while we were -- I
was back at Duke Health System.
So this is a disease management dashboard available to clinicians as a part of the electronic
health record system.
So when you pull this up, for example, here for diabetes, it will show you care metrics
that the American Diabetes Association says you need to worry about, pulls in the relevant
patient data, when things were last done, what the last values were, and a bottom-line
very actionable recommendation on what you should do.
So this was very well liked, used very widely at Duke.
So when you think of what this kind of technology can do for personalized medicine, I think
there is a few points where it clearly makes sense.
So, for example, for test ordering, you can definitely recommend an indicated test.
So, as clinicians are working, you can imagine that you would get some sort of reminder or
pop-up or whatnot that says, "Hey, did you know that for this type of patient, you should
be doing this genetic test, make sure that you don't get an adverse event?"
Or perhaps more importantly, you can recommend against a test with dubious clinical utility.
So, you can imagine a clinician is about to order a test, and you know that this shouldn't
be done, you can say, "Well, you know what?
You probably shouldn't be doing this, and this is why."
You could also personalize test interpretations.
So, instead of getting a genetic test result back that says here's the genotype and in
general this is what it means, you can imagine that it can be combined with clinical data
to provide a personalized risk assessment and care guidance.
So the key part there is you need to pull in the clinical data at the same time to provide
that guidance.
Also, as our next speaker, I think, will be talking about, you can clearly make use of
family histories to, for example, tailor preventive care guidance for cancer prevention.
And, of course, you can manage therapy, so for example, decision support can help you
provide genetically informed pharmacotherapy guidance.
So this is an example of a warfarin pharmacogenetic dosing system that I developed when I was
at Duke.
So this is for a commercial order entry system called McKesson.
So you can see here when you collect a number of relevant clinical data, also identify the
patient's relevant genotype, and then other relevant factors like previous doses, and
based on that, you provide very specific care recommendations on what the dosing should
be for this patient for warfarin.
So, I think some of these examples show -- I mean, I think intuitively and based on review
of the evidence, this approach is very effective.
It can really help provide better care.
The problem with this approach is that decision support has been very limited in its scale.
So, despite decades of demonstrated effectiveness of these kind of tools, decision support is
not widely available.
So, for the practicing clinicians here, unless you happen to work at one of the leading institutions
for developing these kinds of systems, you typically have a fairly low or no decision
support available in your clinical practice.
So, this happens because most decision support is designed to only work within specific institutions
and health IT systems.
So it's great that you have a paper in The New England Journal showing that, you know,
this kind of approach for decision support works, except 20 years later, most people
still aren't able to do that in their clinical practice.
So we still are trying to scale up traditional decision support capabilities that have been
validated for decades.
And my main point here is that unless we focus on scalability, decision support will have
a limited impact on personalized medicine for decades to come.
So here's an example.
Here's a New England Journal paper called "Protocol-based Computer Reminders, the Quality
of Care and the Non-Perfectability of Man."
Great paper, a randomized control trial, had several hundred rules on disease management,
adverse event prevention that showed, you know, this improves care.
The notable part about this was published in 1976, so this was published before I was
born.
[laughter]
Right?
So, this is -- and we are still trying to replicate this capability across most health
care systems.
You look at the rules here.
Most health care systems do not have the rules that were validated in this randomized control
trial in the 1970s.
So the question becomes what's needed to scale decision support for personalize medicine?
So we published a paper on this topic where we thought through this.
And I'm particularly interested in this space also because I co-chair the Decision Support
Work Group for a group called HL7 which is the standard-setting body for health IT.
So there's at least three main components.
So, one, you need to standardize the representation of relevant patient data.
If people talk about diabetes differently, medications differently, observations differently,
you're never going to scale this.
So this is for both clinical genetic data and needs to be standardized.
You also need centrally managed repositories from high quality computer processable medical
knowledge, because if you don't know what you should do, you're not going to able to
do anything.
And you also need standard approaches for leveraging this knowledge to provide patient-specific
advice.
So what I'm going to talk about briefly now is a tool called OpenCDS, which is an open
source standard space, freely available decision support platform designed to enable decision
support at scale.
So, through funding from NHGRI, I was able to start this initiative.
It's certainly intended to support and address the requirements for a national decision support
infrastructure for personalized medicine, but it also encompasses more traditional medicine
as well.
So some key components of this, it uses standard data models.
So I won't get into the details, but, you know, a lot of my activities over the past
several years has been defining and getting adoption of some of these models as international
standards.
It can also leverage various knowledge resources, so looking at all these different knowledge
resources available, it can leverage them both when their externally and internally
developed.
And it provides a standard approach for EHR systems to leverage these kind of resources
over the Internet through what we call Web Services call to obtain patient-specific advice.
So this has grown into a fairly large collaboration.
There's a number of collaborators from both the public and private sector and actually
a fair amount from outside the U.S.
And these are all folks who are interested in pursuing a standard open source approach
to creating the infrastructure for this kind of decision support that scales.
So without getting into technical details, I'm just going to show you how this kind of
approach works.
So you have, you know, a very simple diagram, a given institution with an electric health
record system and their data sources.
And then you have OpenCDS as a care guidance service.
So what you can see here, for example, is this is the Duke electronic health record
system in this kind of approach.
When a clinician clicks on, "Oh, I want the disease summary for this patient," patient
data is collected, sent to the service over the Web, of course, using standards.
And then again, using standards, you got care advice that's generated, returned back to
the system so you can do things like this disease management recommendation screen I
showed a little bit earlier.
Here's another example.
So, as I mentioned, this approach isn't restricted to particular types of knowledge representation.
So here's WarfarinDosing.org, which was mentioned yesterday, and I think a lot of people are
familiar with.
So it's a website that provides genetically guided warfarin dosing.
So, if you'll notice with the screenshot I showed earlier, it'll say here, it says, "Dosing
guidance received from WarfarinDosing.org."
So we showed how we can use the standard wrapper and they can provide this information.
And just to show you what we have in terms of the actual web-based authoring platform.
So, without going into details, I'll just show that there's web-based authoring of,
for example, human readable decision rules, decision tables.
So, for example, this is for vaccination schedule, and this all allows you to provide these kind
of recommendations.
And our latest edition of, for example, decision diagrams, so you can see here, this is how
we're encoding family history risk analysis for identifying if a patient's at increased
risk for BRCA mutations.
So just as a status, we are releasing a 1.0 release later this month.
We already have a beta release that's available to interested parties.
And we have multiple ongoing initiatives.
So, I'll close with some recommendations.
So I think it's really important to make decision support a core component of the personalize
medicine vision.
I think without it, it's going to be difficult for clinicians to make sense of this.
We also need to consider and prioritize scalability for decision support.
So, like I mentioned, if you don't consider this, we'll have nice, you know, funded, published,
New England Journal papers in 2015 that shows, you know, this is a great way to improve patient
care, and in 2050, which is about how long it's been since that New England Journal Paper,
we'll still be trying to figure out how we do it in most health care settings.
You should leverage also relevant resources.
OpenCDS is one I described, and there's multiple other efforts I didn't have time to get into
that are specifically focusing on this area, just not in the personalized medicine space
that could be leveraged.
Also, as a note, I think it's important to align with an influence EHR meaningful use
regulations.
So, for those of you who aren't familiar, this is the regulation that is driving through
various reimbursement models EHR foci in this country.
So, in order to have an impact in this space, it's really important for, I think, the NHGRI
and relevant parties to really influence that.
And I think, as a final point, we need to start building this infrastructure now.
If we don't do it, divergent, incompatible approaches will develop.
And it's actually a really good thing that a lot of this has not yet been developed because
among the morasses in other aspects of traditional clinical medicine in this space is people
have done so many divergent things that it's really hard to get everyone on the same page
at this point.
So I'd like to acknowledge NHGRI for funding me as one of those few dots at the end of
spectrum, the University of Utah Department of Biomedical Informatics and Office of the
National Coordinator, and also a number of collaborators.
If you'd like information, OpenCDS.org has all the information.
Basically, if you have a Google-based email access, I can give anybody a free access.
And I'd like to thank you for your attention and take any questions.
[applause]
Male Speaker: I had a question.
Are you interacting with some of the large EHR providers now, like, you know, Epic and
some of the others that seem to be --
Kensaku Kawamoto: Yeah, so, at the University of Utah, we were
working with Epic and Cerner installs.
There was also a collaborator from a health system called Mainline Health in Pennsylvania.
The CMIO, the chief medical informatics officer there was the head of Decision Support at
Siemens and just got a contract with them to try to integrate this time technology.
We're definitely interested and we will certainly be doing it for Epic and Cerner because that's
my operational duty to do that at the University of Utah.
Male Speaker: Okay.
And can I just ask you, was it easy to get Epic and Cerner interested in what you're
doing and involved or not?
[laughter]
Kensaku Kawamoto: Let me preface that to say I started in August
at the University of Utah, so we are still getting started.
But just as an anecdote, we had some collaborators from Kaiser of Southern California who wanted
to work with us because they were having difficulty having Epic do things for them.
And so I'm under no illusion that a company that's selling, you know, billions of dollars
of EHR systems without making any modifications will be that interested in changing the approach
they do things, but so, the jury's still out.
But I think that's why engagement in the meaningful use process will be very important because,
as part of some other work I've done, I've interviewed EHR vendor executives, and their
-- they basically say if it's not meaningful use, we're not interested right now.
Female Speaker: Can you show your slide again where it has,
like, with the cancers and the breast cancer --
Kensaku Kawamoto: This one?
Female Speaker: Yes.
Kensaku Kawamoto: I got a -- I need to make a preface there
that this still under development because it uses an open source technology which became
available a month ago.
Female Speaker: So does it ask anything about, you know, if
there is a male relative with breast cancer --
Kensaku Kawamoto: Yeah --
Female Speaker: -- has he been tested and if so, what the
mutation is to be able to then --
Kensaku Kawamoto: It doesn't -- I just took the straight U.S.
Preventive Services Task Force clinical guidance.
So, you know, none of this -- all this is only as good as the source of the recommendation.
Yes, sir?
Male Speaker: Is there a library on the open source server
site of these decisions?
Does this become something the community can access or we can work with and understand?
Kensaku Kawamoto: Certainly the infrastructure is all open source.
Because we've been focused on infrastructure, and as I noted, we're doing the 1.0 infrastructure
release this month.
And actually, some of the base standards on this, we just finalized as a standard in September.
[laughs] So, we've been focused on infrastructure, but we will be making a lot of the content
open source.
For example, we have a collaboration with the New York Citywide Immunization Registry
and the Alabama Department of Public Health, where they will be providing all of their
immunization-related decision support, all available open source through OpenCDS.
Mark Hoffman: Hi, Mark Hoffman with Cerner.
And great to hear about your work.
I think there's two perspectives that, at least within our company, we have.
We feel that clinical decision support is real within commercial EHR systems, but at
the same time we recognize the challenges of exchanging decision support logic across
systems and also recognize that the maintenance in the current model of decision support logic
is very cumbersome.
And so, we are working on ways to get past that burdensome maintenance of rules and,
at least speaking for Cerner, we see initiatives like yours that provide open source thought
capabilities as part of how we can work with the community to move forward.
So I'd be happy to talk with you afterwards --
Kensaku Kawamoto: That would be great.
And I'll just mention, Cerner has a wonderful capability called MPages, which allows you
very easily to integrate this kind of content.
So, right now my headache is primarily on the Epic side.
Yes?
Male Speaker: [inaudible]
Kensaku Kawamoto: Okay.
Oh, I think I'm out of time.
Thank you.