As
the industry lurches toward automation spurred on by the HITECH Act, reading
the tea leaves on meaningful use is becoming all important. And while the HIT
Policy Committee has issued a fairly extensive matrix
on the subject, many of the apparently clear details dissolve into fuzziness
upon closer examination. To tackle these issues, and provide a snapshot of
where the industry stands, HIMSS Analytics has released a report on the
subject. Recently, HCI Editor-in-Chief Anthony Guerra had a chance to talk with
the author, EVP Mike Davis, about healthcare’s all-consuming quest to collect
the stimulus funds.
(Part
I)
GUERRA: In
your report,
you talk about the importance of storing data in structured formats, such as
LOINC. Can you talk about that a bit?
DAVIS: If
hospitals have laboratory data and they want to share that data, the best way is
to put that data in the LOINC format. Now LOINC is part of SNOMED. So it’s the
ability to encode all of that data, and to actually capture more data and
discreet data, that allows us to do
a lot of this. And so when you start to look at this, most of the solutions out
there have some kind of proprietary data format. The challenge will be how many
of those have actually started to use SNOMED for the encoding of that, how many
people want to convert to that. So it’s just an area they need to look at and
be careful with because, as you start to read through those measurements, one
of the areas that they keep talking about is that summary report they want to see
shared between all the care entities. So that’s going to include things like
your problem lists and progress notes and medication lists and allergy lists
and laboratory results. So what we’ll have
to do is create that summary document using standard data formats and, of
course, what they’re going to use is the CCD transaction standard at some point
in time. That’s where it’s all morphing to.
So there are some challenges with controlled
medical vocabularies. As you start to bring data into those clinical data
repositories, how do you normalize it? Not everybody is using the same clinical
department solutions from the same vendor. So if you have Sunquest for labs and
you’re using Eclipsys for your EMR and you’re using maybe Siemens for your radiology and you start to bring all that
information together, how do you normalize it so that, first of all, you can
share it with people and, second of all, your clinical decisions support systems can understand when
they should fire.
GUERRA: Where
do you think most hospitals stand with that? It’s hard to imagine smaller
hospitals and critical access hospitals having a sophisticated data management
group.
DAVIS: Well,
I think part of that is probably going to be resolved by the Regional Extension
Centers. That may be where they get a lot of guidance and direction. There will
also be consultants. I think most of the consulting companies out there understand
these issues and have dealt with them.
GUERRA: You
wrote that hospitals must move forward in the area of nursing protocols because
of all the measurements that will be required. So how do organizations get from
that basic level of a simple drug-drug interaction check to where you’re saying
they need to go?
DAVIS: Well,
first of all, there are some vendors out there that have very good clinical
support systems, Epic and Eclipsys and Cerner and people like that, McKesson
has done some of that work and Siemens. Most of them set up these user panels,
where the client is starting to share and exchange information about how
they’re setting up those clinical decision support protocols.
In terms of adoption, if you don’t do this right
and your physicians start getting all these alerts that really aren’t
meaningful to them, they start ignoring them. That’s not a good thing.
GUERRA: Sometimes
we talk about all these things – EBM, CDS, the creation of order sets – as if
they are distinct. But isn’t this all really under the umbrella of CPOE,
bringing everything to bear in that one moment for the physician?
DAVIS: It
should be. Now remember, alerts are going to be little boxes that pop up as
they start to enter data on that screen for a number of different tags, dosages,
forms of drugs, stuff like that. Where we really want this to go, and I think
this is what the government is trying to get to, is they want the physicians
documenting. When you start to get to clinical documentation where the
physicians are documenting their history and physical, their progress notes,
the discharge summary, all those things, it’s putting guidance in there so you
get evidence-based medicine. So are you using protocols, let’s say, like APACHE
in ICU or InterQual?
Now, long
term, what ONC is driving to, and this is very important, the reason they want
to capture all this discreet data – which you’re starting to see in 2011 and
you see more of in 2013 – is they are going to establish these evidence-based
medicine protocols because they’re going to have a ton of data.
What the government is getting to is they’re going
to have this huge repository that people start to look at and say, “Here’s best
practice, here’s evidence-based medicine.” You’ll have a huge repository and a
lot of really smart people looking at it and trying to figure all this stuff
out. Then they can actually take that
data and create a standard transaction for clinical decision support that can
be implemented in all these systems.
GUERRA: I
can just picture Deborah
Peel reading that.
DAVIS: But
you can still do this. You can still de-identify the data and do all this
research.
I understand the issues of privacy, believe me,
but I think the thing is you can still submit that data as de-identified data and
come up with the protocols and the guidelines and best practices. So to me,
that really doesn’t fit into the privacy issue. The privacy challenge to me is
once you get this data, the summary data, and you start sharing it, that’s
where you have to make sure it’s encrypted. You have to make sure you’ve got
the appropriate people, processes and guidelines in place for exchanging the
information.
GUERRA: Let’s
talk about structured versus unstructured text. Since the physician narrative
probably isn’t going away, how do you deal with the important information that
might be trapped in unstructured text?
DAVIS: Well,
I think you can look at this from a couple of different perspectives. First of all, there’s a movement out there called Health Story. Health Story says that
what physicians are trained to do and what they’re very good at is called
dictation transcription. The problem with that is it can take more than 24
hours to get the information which is then put into a text log, so it’s hard to
extract information. Health Story is trying to do two things.
The first step is that part of the CCD standard is
the document formats. So when you create a document, it actually creates the
formats for certain data. So the first step of Health Story is to let
physicians do the dictation and transcription and create these structured
documents so that when people are looking at those documents they know exactly
where to go to find the information they want. So it’s an incremental step of improvement, and the nice thing is
physicians are okay with it because it doesn’t disrupt their workflow, they
understand it, and so the adoption should be pretty high.
One thing you can do to that data is called
Natural Language Processing technology. That’s an engine that looks at text to
extract discreet data. So retrospectively, you can take those documents around
the naturalized processing and extract discreet data for reporting or research
or whatever you want to do. What you really want to get to is, as the physician
dictates, speech recognition is involved. They can see the text being created. You
can actually dictate discreet data into
certain fields to be captured and interacted with clinical decision support
during that process.
Health Story already has implementation guidelines
for several different documents. There is an ideal model in the future where
the physicians would dictate, speech recognition would take over, would be able
to capture discreet data, would be interacted with clinical systems support. In
that scenario, you’d apply evidence-based medicine protocols and clinical
decision support while the physician is documenting.
Part III
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