Family History Working Group Update: Breakthroughs in Healthcare - Geoff Ginsburg


Uploaded by GenomeTV on 11.05.2012

Transcript:
Geoff Ginsburg: Thanks. So, this is one of the outputs from
the working groups that you -- you've heard some of already, and you'll hear more of over
the course of, I guess, this afternoon and certainly tomorrow related to family history.
I -- after hearing Reed Tuckson and the payer's discussion, I decided to put Breakthroughs
in Health Care as a subtitle for --
[laughter]
-- for this group because the definition of breakthrough that I heard was something that
would have improved quality and take cost out of the system, and I think family history
really has that potential to do it in a very short period of time in the near term.
So, as you heard from the sequencing group, we've had -- this group emerged out of the
December Genomic Medicine II meeting, and we've had four meetings, including at that
one. Our goals were to develop an agenda that would really help advance family history into
clinical medicine and to develop ideas and concepts that actually might be responsive
to an RFA or other funding opportunities either from an HGRI or other funding sources.
Now, when Teri gave her overview of why we were here, I was -- I noticed that in contrast
to some of her prior talks that she didn't have a Far Side cartoon, so I decided to make
up for Teri's deficiencies and indicate, you know, certainly family history is underutilized.
It's poorly documented in the medical record and has a significant opportunity to identify
individuals at risk. Certainly many studies have documented the advantages of family history
and identifying risk characteristics. The GWAS has really missed out on with odds ratios
two, three, and four for family history-derived information as opposed to from some of the
genome-wide studies we've seen.
So, that's the reason why we're pursuing this. Over the last three calls we've grown in size.
We have a, you know, a diversity of individuals representing a number of institutions and
organizations that have participated in our discussions, and I put the E to indicate those
individual institutions and organizations that are part of EMERGE [spelled phonetically],
because I think having electronic medical integration is part of -- an objective becomes
something that could be quite important for family history.
So, as you heard earlier, I think from Rex, that we came out of the December meeting with
an interesting and diverse list of topics to develop a outcomes research agenda, link
to family history implementation, and to really to use the principles of implementation science
to understand how one might put family history into the clinical work flow in a number of
diverse clinical environments. We thought that we might coalesce into some type of advisory
group that would help inform, particularly NIH, clinical trials and other studies of
where the opportunities might be to introduce and capture family history information, which
is often not done, and also to provide educational tools to both the patient community and the
provider community on both family history, its merits, and its importance.
And so here we also have spent some time thinking about how to explore electronic media, particularly
social networking media, to help patients gather family history in the community. And,
also, how do we validate family history was -- became an important question. Patient -- particularly
patient-reported information, how valid is that? And I think the -- an ideal goal, aspirational
goal is to take family history data, molecular data, clinical data, and build the ultimate
risk model.
And, so, as we thought about these over the last several calls, the red indicate where
we've really prioritized our efforts, at least up until today, and I think what's highlighted
in blue are really the -- our correlative goals that I think we could achieve if we
approach the three that are highlighted in red.
So, one of the -- the first topics that came up was -- well, what is the landscape of family
history tools that are out there? And, of course, the surgeon general's tool has been
mentioned here and are familiar to I think most of you. And a number of organizations
have created their own. And I think you may have heard us present on a family history
platform that we developed at Duke University. Intermountain Healthcare has developed one
of their own, and several others, including the University of Virginia, were mentioned.
But, we decided one of the things to do was to have a bakeoff, meaning a way to compare,
you know, amongst all the stakeholders in our working group, at least give them an exposure
to the opportunities, so we had a webinar in which we looked at both the MeTree tool
as the Duke University tool is called and the Intermountain Healthcare's Our Family
Health Tool.
So, just briefly for those of you that couldn't participate, MeTree was developed around 2004
and 2005 and is a web-based tool that is -- where patients will enter their family history data,
perhaps with some guidance from a navigator or some type of counselor in the office if
they need it. The -- the goal is to collect three generation family histories on as many
individuals as possible. The software is set up to handle 48 disease areas. Our clinical
decision support tools have been developed for the four areas that you can see here,
three cancers and thrombosis, and the platform generates a pedigree, a tabular family history,
and provides a report both to the provider as well as to the patient, and I'll show you
an example of those in a moment.
Given its 2003, 2004 origins, it's a pretty boring user interface right now, using radio
buttons to enter information, but I can tell you that there's an iPad version that will
be launched sometime in the next few months that is pretty nifty.
So, I'm just going to take you through a couple of screen shots that are pretty obvious about
entering information about your relatives, their names, their ages, whether they're alive
or dead, and whether you actually -- or whether you actually don’t know, and also who you
might have talked to to gather your family history information, and then at the end when
you exit the program, there's -- there are a number of surveys that are done to look
at your understanding and comprehension of what the information, what you did, how it
might have helped you in some way, and also some process measures that we can talk about
later.
So, the report -- one of the reports that's generated, as I said, is a fairly standard
family pedigree that you're quite familiar with, and then there's this tabular version
of the same report that is also generated -- and these are generated for the providers.
And, interestingly enough, and I think this has been found by others, the providers actually
prefer this way of representing family history over the pedigree, most particularly in primary
care. Most providers really just don't understand the circles and the squares and a bunch of
other things, and it's very hard for them to distill it down into something that's meaningful
-- not that -- I'm not sure that they -- how they -- what meaning they get out of this,
but they certainly prefer it.
As I mentioned in the MeTree tool, there's a patient report that is really meant to encourage
the patient to be -- to take action. So, it really tries to explain to them why they should
be talking to their physician about something that was triggered in their family history,
what perhaps is some of the rationale for it, and also where they could seek out other
information to become more educated about this. And, at the same time, the provider
gets a similar type of series of information that is very top line actions that they should
be taking, and if they are interested in drilling down further, why those actions are recommended,
and, even further, what are the guideline bases for those actions to be recommended
in the first place? So, depending on provider interest, there's lots of opportunities to
go down this menu. And, I should say that in this particular family history tool, the
decision algorithms are based on either a U.S. Preventative Task Force recommendations
or ask -- or Cancer Society recommendations. So, it's really guidelines-driven decision
support that's underpinning these recommendations.
So, the other tool that was demonstrated was the Intermountain Healthcare's Family History
platform, which is accessed through the patient portal. I should have mentioned that the MeTree
is accessed outside of a patient portal. It's a standalone entity at the moment, and it
really -- it's really a nifty tool, very sleek and well done. I think this was really the
reason why when Mark was Intermountain Healthcare that it was -- that this tool was created
was to really snoop around and figure out who was related to whom. But, nonetheless
-- no. They've really done a fantastic job. When you look at this interface, you have
a number of choices of how to enter the family history domain, either to just begin to just
piece together your pedigree or to just -- if you're not sort of graphically inclined, you
can just enter numbers of the people in your family. And, if you already have had your
ancestry done, you can directly import your ancestry data, and in the background, the
software will assemble your pedigree. And there's a lot of nifty drag and drop opportunities
with these icons. The probe end is right here in this diagram, and you can begin to add
many, many relatives -- probably more than you probably want to have in your family to
this display. And this is actually Grant Wood's family. He was very clear that this was okay
to disclose his family tree. I don't know if the information is actually correct.
So, you get this nice display of a pedigree. It's available through the portal. It's also
on display for you and for your provider, and you can import other information about
your health, other conditions you might have, and there's a -- on the right-hand side, you
can see there's a searchable feature in there where you can actually look for syndromes
that you've been told you might have or your family might have and document those as well.
So, at the end of the day, not only do you get a nice pedigree, but you also get this
sort of file -- this file type of report that, again, even in the Intermountain Healthcare
system, seems to be a preferred way of displaying family history to the pedigree illustration.
Now, if we look at these two software platforms side by side, in many respects they compare
quite well. The information is patient-entered from both. It is web accessible, either at
a kiosk in the waiting room or at home. A number of the informatics elements are in
place for both. The MeTree tool, I think, has embedded in it algorithms would lead ultimately
to a decision support mechanism that is, I think, quite attractive, and I think that's
generally what we're all looking for. But, the Intermountain Healthcare tool is not that
far behind. A number of -- a lot of the work that's going on right now with Our Family
Health is implementing the algorithms and developing the CDS tools that will enable
it to also function in that way within the Intermountain Healthcare System and perhaps
others.
So, at least in -- we were able to accomplish one goal of really beginning to compare and
contrast what types of platforms and characteristics they have and begin to think about which ones
we might implement in a future research project.
So, in the last teleconference our group had, we began to kick around what -- so what are
the ideas? What are the things that we really want to push forward as opportunities? So,
one, and I think this came from a number of the groups that were using Epic and potentially
other electronic health records was to really think about how would this integration occur,
how does -- do you integrate family history collection, software, and decision support
into existing electronic health records that are also being developed and implemented across
many health care delivery systems today, and possibly to do this in the context of an STTR
or SBIR type of mechanism with Epic or perhaps Surner [spelled phonetically] or others.
But the -- I think you can see this list of really critical questions that need to be
addressed for this to happen about standardization of the information, both on the input side
and the output side. Also, thinking about what are the information gaps and the workflow
gaps that need to be addressed for this to happen seamlessly? So, really critically looking
at that information flow pathway and how it occurs in the context of real world clinic
visits and making sure that the information is captured and delivered at the right time
points to be most effective. I mentioned the validation of the information that's collected
from the patients, and the idea that -- the possibility of bringing in other applications,
like MeTree, for example, that is a third party tool, and how does that interface -- how
do we create the notion of interoperability between these possibly, and making the family
history readable in the electronic medical record all seem to be reasonable things to
be investigating with some of the producers of electronic medical records.
I think it was Mark -- and I'm also a friend of Mark's -- that made the recommendation
that there's a -- there is this -- there's this group that's outside of what we're doing
here is -- has developed clinical decision support tools and a clinical decision consortium
-- a consortium has developed that would be great to link to that as well as to make this
-- make anything that we're talking about open source so that many different systems
can take the software and modify it to meet their needs and integrate it into their local
environments, which I think are very reasonable things to be considering as well. So, this
was idea number one.
Idea number two, which really came from Jonas Almeida -- I hope I'm pronouncing your name
correctly, Almeida -- at University of Alabama -- is really to take the social networking
media that we have today, Facebook-type of applications and the sort, as a way and an
opportunity to capture family health history data. And, so, visiting both -- you can see
at the bottom of the slide, some of you, that there's a -- there is a website for a YouTube
video that Jonas made to demonstrate this concept, at least some of the early phases
of the development of the -- mostly the informatics mechanics to allow this to happen. So, the
API has been established. There's a prototype, and it uses the cloud in ways that I could
not describe to you, but maybe Jonas can -- and it really allows the patient and their families
to capture the information in a diverse and effective way. At least that's the goal to
use the surgeon general's tool is at least one of the ways to do this in a standardized
way.
So Jonas, if you have a second, do you want to elaborate on what I said? Because I'm sure
I didn't do it justice.
Jonas Almeida: Sure. So, the social media world has been
developing and maturing, and now we actually have something we call social computing, so
there is an infrastructure we can use, you know, our applications, and families [unintelligible]
or genomic history for that matter -- [unintelligible] to this sort of architecture. So, the -- for
those likely technical details, it's called Open Authentication. There was a first version
that was very awkward we explored last time we met, and since then this protocol has enabled
a different -- and a much more abstract use of social computing.
So, to give you an example, for instance, if you have relatives that like you, you can
ask them to fill their part of your medical history in the same way that you fill their
part of their medical history. So -- and you can imagine the same thing for the way a family
history would interact with the genomic core [spelled phonetically] facility. So, the genome
is somewhere, which requires quite a bit of storage. And, again, this external entity
could be a partner that is incorporating this social computing. So, what happens is that
at the center of this network of dependencies has always full control and awareness of which
web services are engaging or storing the data that describes the medical history.
Geoff Ginsburg: Thank you. I mean, to me, this sounds like
a very cool idea, one that is certainly going to take advantage of our -- of the networks
that we are already developing. Maybe we're not as networked with our families as this
type of strategy would require, but you never know. And I think -- and we're going to talk
about it, I think, a little bit later tonight.
So, the third idea and the last idea, really, that I wanted to talk to you about was really
had a -- think about a family health history intervention that measures certain outcomes.
And, again, I mentioned this before, but we really want to think about a project that
would optimize how we collect family health history data and how we bring it to the point
of decision with the provider predominantly in the context of NEHR [spelled phonetically],
although that's not a formal requirement, and to measure and demonstrate that the -- that
they're improved outcomes, and we can define what those outcomes might be as a result of
this intervention at various stakeholder levels. And the stakeholders we're thinking about
are the patient, provider, and the system.
So, we talked about a number of potential environments in which this could take place,
and primary care was certainly top on our list. But, one interesting idea was also to
think about how family health history might actually influence decision making in emergency
room, in an emergency department situation, particularly in the context, for example,
of whether somebody might be having a thromboembolic event or a myocardial infarction or something
of that nature. So, there's a lot more discussion to think about, but that would be a pretty
interesting and unusual area to explore. The notion of bringing it to other environments,
such as rural practices, underserved environments as well, and even to help the next generation
of physicians really learn the value of family health history to bring it to the practices
where residents and interns and other providers are being trained. And to do it in the sense
of -- to understand whether this is really working in the real world, does the intervention
work under usual conditions, I think, is the question that was asked, and the kind of study
design that we had envisioned was something that is called the pragmatic cluster randomized
trial, the idea being that, first of all, pragmatic meaning it's in the usual care environment,
clustered meaning that some practice environments might have access to the intervention, and
others would not. That would be called usual care. And do that in some kind of randomized
fashion that we can discuss later.
And while we were discussing this, this paper came out, which was, I think, if I'm not mistaken,
and I could be wrong -- the first publication of any outcomes research on family history.
I don't know if anybody wants to differ with that statement, but this came from Nadine
Kareshi [spelled phonetically] at the University of Nottingham, and what they had done was
essentially what we were talking about at the same time, a pragmatic cluster randomized
trial of 24 primary care practices that received family health history information about cardiovascular
disease and the goal was to determine whether they could identify individuals at risk for
cardiovascular disease more so using the family health history intervention compared to the
usual practices of the providers and those groups.
So, the salient features of that was, as I said, the trial design, about 750 individuals,
none of whom had previously diagnosed cardiovascular disease. It was done in 24 primary care practices.
This was not an electronic intervention. It was all done on paper. And they found 4.3
percent compared to 0.3 percent having risk of cardiovascular disease in the study, which
means if you -- a typical primary care doc might see -- primary care doc might see 10,000
patients a year -- or even a practice might see 10,000 patients a year. But, nonetheless,
that means 500 patients were identified out of that 10,000 that otherwise would not have
been using the family health care -- family health history intervention, which is not
a non-trivial amount when you think about it in those terms. But, they hadn't -- they
didn't take it the next step. They didn't follow these patients long enough, of course,
to see whether those individuals, if they had an intervention and response to their
risk factors had any changes in outcomes. Of course, that would take quite a long time
for this disease.
So, but, this was really an important proof of concept that this could be done for us,
and I think it really paves the way for studies like this across the breadth of topics that
we're been talking about today, I think. So, what we have been thinking about -- this is
a very busy slide, but I just kind of call your attention just to the center part, the
colored part, which is just a -- you know, it's just a schematic of what a family health
history intervention trial might look like, collecting information at the outset as well
as educating patients, all done via the Internet or web-based -- a web-based design using MeTree
or other family health history intervention tools that provide a risk assessment, clinical
decision support, so that at the time that the patient is actually interacting with their
provider, it's not about what is the information, it's about what the treatment plan, what the
actions are. So, it really, really jumpstarts the ability to do this without a lot of the
cost of the interaction that takes place right now if you do a family history at the time
of the visit. And you can either go into -- if you're not at risk, have routine screening,
whatever that is. If you're at higher risk, you might have a prevention strategy implemented
or a screening strategy that could include genetic measures or other measures.
And the boxes that you find hard to read here really are about some of the outcome measures
and information, both on the process side as well as on the clinical side, process measures
as well as clinical outcomes that would be ascertained throughout this process, throughout
this workflow. And again this is also probably too much information for the -- for you to
really read on this slide, but as I mentioned at the outset this type of trial would really
seek to look at patient, provider, and systems measures. And, if you think about it, most
of the academic types of studies that we do are really focused on the clinical measures
in the patient. But, and so we heard from the payers and others, maybe there are other
things we want to measure that have nothing really -- or very little to do with those
clinical measures. Maybe it's the financial metrics of the system or whether they're -- we're
really retaining our doctors because they're much more satisfied with the way that they
interact with patients. And, so, they're really happy and they want to see more patients.
I mean, these are just hypothetical, but you can see there's -- there's a myriad of ways
that we can really think about outcomes besides the box that we're normally programmed to
think in.
So, this is my last slide. So, the next steps would be tonight we're going to gather round
and discuss some of these opportunities and perhaps more. We thought at the end of our
last call that we would -- for these three ideas that we would develop three subgroups,
like the genetics sequencing group had done, but maybe, as Rex said, maybe we'll find that
that is not the way to go and we'll all come back together. We'll see. We hope that we
will respond in some way using family history as a demonstration project for the RFA that
we discussed this morning, and we should really think about how to link this to some of the
other working groups as the -- you know, certainly to the sequencing working group makes a lot
of sense. And as Maron [spelled phonetically] and I were discussing earlier today, putting
sequence information in the context of family history could be quite powerful and really
be informative about how to narrow the scope of where you should really be looking in the
genome as opposed to looking at everything, which I think was a topic that we discussed
at several points during the course of the day. And then, hopefully, we can get some
feedback and advice from all of you and our invited guests, the other stakeholders. Thank
you.
Dan Roden: Gene, and then Mark.
Gene Passamani: Do you have data on the two instruments as
to the completeness of ascertainment across socio-economic class, educational achievement,
absent fathers, that sort of stuff?
Geoff Ginsburg: I can't speak to the Intermountain Healthcare
tool on that. For the Duke system, we have had a diversity of socio-economic groups and
educational groups use this intervention with a high degree of success in capturing the
information. Whether it's accurate or not still remains to be seen, but I don't think
that we've -- I think we still are fairly in a narrow scope. We would hope to explore
that in broader populations going forward.
Gene Passamani: And one follow on, as I mentioned to you earlier,
I wonder if there is a way of finding out how one can really identify families that
you need to look into, sort of a cage approach to whether you should take a family history,
a complete family history.
Geoff Ginsburg: I mean, what's the trigger, you know, why
would you do this in the first place, and are there a subset of people that we should
be really focusing on versus all comers [spelled phonetically], and I don't know if Maron or
Marc or other people that have spent more time in family history than I have want to
address that.
Marc Williams: Yeah. I think that conceptually it would be
relatively straightforward as people were entering or interacting with a tool that if
you define certain thresholds you could trigger ASK [spelled phonetically] drivers where you
would begin to drill down and prompt them with additional questions much as might happen
in the office where when you hear a particular piece of information from patient use, you
know, perk up, and then tie that perhaps into more -- so just to use the breast ovarian
cancer example that if you saw that the patient, you know, reached a threshold where they were
two patients under the age of 40 with breast cancer, then the tool would automatically
go into, say, a breast ovarian cancer risk app that would ask much more specific questions
about that and would probably then trigger, you know, a recommendation that would look
very different than people that would be interacting in a standard way. I think that's a reasonable
approach. It's one that has been thought about, but it's one also, I think, that needs to
be studied to see how it might work.
I wanted to just come back to the Kareshi [spelled phonetically] study to make two quick
points. One is that even though the study as not intended to nor was powered to detect
any differences, they did find that in the intervention group there was a statistically
significant difference in patients who either ceased smoking or reduced smoking, even in
the relatively small numbers at a 0.001 level, and that there was actually increased aspirin
compliance based on the risk and attract with the risk that they were presented. So, they
actually did detect some at least secondary outcome measures that have chains of evidence
to the primary outcome of incident cardiovascular disease, which was quite interesting.
The second point that I think was much more important was the accompanying editorial to
the paper, which was written by Al Berg [spelled phonetically], who, of course, chaired the
NIH State of the Science conference on family history, which fairly well trashed the current
level of evidence and importance of family who in print ate crow, which was very satisfying
to those of us that know Al, basically saying, "You know, I -- that this study has proved
me wrong, that first of all I didn't think a study like this could ever be done, but
second of all I didn't think it would actually show benefit." And, so, what he outlined in
his last paragraph, it says, "As a practicing physician, this is what I want." And what
Geoff articulated in terms of the synthesis of the -- the collection and synthesis of
family history in the clinical decision support that works in the electronic health record
is what Al asked for, and I say if Al asks for something, by God, we should give it to
him.
Geoff Ginsburg: We should give it to him. Right. Okay.
Female Speaker: I was just going to say that we have developed
a rather short tool, eight questions. I presented it last time. It's embedded in our electronic
health record at the VA, and it is a -- I would call it a screening tool for cancers,
family history of cancers, and the follow-up is just making a referral for genetic services.
So, it seems to be working pretty well.
Female Speaker: Yeah. What's the accuracy of self-reported
family history?
Marc Williams: It depends on the disease, and there's not
great data on that. For things like diabetes, heart disease, it's actually -- in the studies
that have been done, it's pretty good. For things like mental illness, and, in particular,
substance abuse as you might expect, it tends to fall off. Now, that being said, I think
it's also important to recognize that all the risk classifications that have been developed
have been based on self-reported family history. So, we're dealing with the usual empiric risk
that we're getting out of that. So, in some sense, if we have validated family history,
we're going to have to redo all our risk estimates because they're all off, and we've actually
done that in colorectal cancer, utilizing the Utah population database, which actually
shows that in colorectal cancer the risk estimates that are in use are reasonably good empiric
compared to the actual validated cases.
But the interesting opportunity is that, particularly to build on the social networking ideas, is
if you begin to connect family members, particularly within the context of either a single EHR
or in a health information exchange where you actually can identify the individual and
know what their diagnosed diseases are, you can actually not only validate the information,
you could precreate the family history if you have enough of that information. So, I
think that's another interesting area of exploration that I know at Intermountain they're intending
to pursue using some tools that they have access to.
Female Speaker: Actually it's a perfect follow on, and I am
not a friend of Marc's --
[laughter]
I've met him, and I am not impressed.
[laughter]
Male Speaker: You've been waiting all day to do that.
Female Speaker: Yeah. All day, yeah.
The question I have is particularly with the social networking. Has anybody looked at what
becomes the obligation beyond to the probeyond [spelled phonetically]? And it just seems
like there is a potential major ethical and care issues there.
Geoff Ginsburg: Well, I would agree. I mean, actually I was
thinking about you, and when I was presenting -- not all the times, but just then I was
presenting --
[laughter]
-- that, you know, the whole concept of social networking and also the, you know, how we
use health information in the context of Facebook-like applications, sort of has a series of questions
that probably you'll have a great time with.
But, no, seriously, I think there is a policy and ethics agenda that has to be thoughtfully
conceived at the time that we really put out there a social networking tool to capture
family history. But I certainly don’t have the answer to your specific question.
Male Speaker: Yes. Just a few words. So, I also don't have
an answer to your question. The good news is that the way we treat governance is now
being objective mathematical treatment, and people publish papers with mathematical descriptions
of these dependencies, obligations, and what's called instantiations [spelled phonetically]
of user operator, so the relationship between the user or usage and the data entity.
So, the good news is that the level of the discourse is becoming more interesting, more
abstract. NW3C [spelled phonetically] is paying close attention to this worldwide web consortium,
so there are web standards that are emerging to address these issues.
Dan Roden: Did you have a question or a --
Female Speaker: Yeah. Geoff, I had a question about linking
up with Epic, and because within the pharmacogenomics network, we've also talked about, you know,
can we begin to work with these major EMR providers? And, so, I guess I'm just curious
whether that's sort of a hypothetical or you've actually had conversations and they're interested?
Geoff Ginsburg: Sorry. No, this is -- this was an idea that
was raised just a few weeks ago on our last call by Cathy McCarty [spelled phonetically].
I don't know whether she's had specific conversations on that. Rex, did you? Have you? Or others
that are working --
Rex Chisholm: Well, I think it's -- yeah. So, the Emerge
[spelled phonetically] network had not only Surner -- not only Epic but also Surner and
GE at steering committee meeting three times ago or something.
Female Speaker: Three sessions.
Rex Chisholm: Trying to engage them. And then I know that
some folks from Northwestern and I think some folks from Mount Sinai have been very actively
involved with their genomics working group in terms of moving forward. And there was
just a meeting last week in terms of actually thinking about putting data in the electronic
health record. So, there's already something of a relationship.
Geoff Ginsburg: And the thing that's really going to change
the landscape there is the meaningful use phase II criteria which actually articulates
that one of the meaningful use goals will be representation of family history and electronic
health record, and really the vendor community right now is wholly consumed with creating
products that are going to be able to hit the meaningful use category so that people
can get reimbursed for implementation -- meaningful implementation of electronic health records.
And so if that persists in phase II, we'll have a much better way to engage with the
vendor community on this.
Dan Roden: Okay. So, Erwin and Kate. Kate then Erwin.
Kate.
Erwin Bottinger: Yeah. I just wanted to comment on the Epic
question, and as Rex pointed out, we at Mount Sinai and folks at Northwestern have been
actively engaged in Epic. And, you know, from our experience, they're very receptive to
developing custom connections with some external tools. I think that's a very feasible proposition
with, you know, some payment that you make to them that are not excessive, but, you know,
certainly it is feasible, and certainly we've gone that path that is suddenly I think a
worthwhile investment. And I think that's something to consider.
Geoff Ginsburg: But the danger here is that if each -- if
there's a number of modifications to something like an Epic tool over time, it becomes a
unique tool to whatever system is being used in it and it doesn't communicate across networks
like we're trying to do. So I, my -- I guess my plea is that we try to have this as a coordinated
conversation. And, I think as customers of Epic, we probably are a powerful force and
could actually negotiate well.
Male Speaker: I'm also not sure if they qualify as a small
business. No, I think that's why it would be an STTR.
Female Speaker: So, as an Epic user, I actually pulled up
the family history tool in Epic while you were talking, because I can access it from
here, obviously. It doesn’t actually look -- I actually would like a little clarification
of how you would work with them be -- to improve their family history tool, because it's actually
very similar to what you're presenting. And, certainly, that's coming out in the sort of
tabular form if you look at it.
And, so, I was actually unclear as to what you were proposing with that [unintelligible].
Geoff Ginsburg: Yeah. Let me -- I'll just make -- well, the
specifics of the Epic proposal are really to think about what's missing, you know. So,
you just took a snapshot of it, but I think we might really want to consider in different
practice environments, different disease areas, to try to understand what the gaps are and
help fill in those gaps so we have something that we all at least have a consensus as the
right information to collect. And, of course, what Epic doesn’t do now is provide the
decision support downstream of the collection, which is something that I think is desperately
needed for these, for the information to be used.
Marc Williams: And the collection tool only allows you to
enter one brother, one sister, one grandparent, and it doesn't define sides of family, and
there's an infinite number of deficiencies in that particular web form and the number
of diseases that are represented as structure data elements is very, very small.
So if -- the improvement needs to be -- start over.
Dan Roden: I say we move on.
[end of transcript]