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On June 16, 2008, Melanie Chansky presented the AHRQ Preventable Hospitalization Costs and Mapping Tool on a Web conference. This is the transcript of
the event's presentation.
June 16, 2008
1:00-2:15 p.m. ET
Jeff Gallagher: Good morning and good afternoon,
ladies and gentlemen. Thank you so much for taking time to join us
on the call: Preventable Hospitalization Costs and Mapping Tool. My name is Jeff, and I will be the host for this
event. I will be in the background answering any general and technical
Today's event is being recorded. However, we will make two options
available to you to be able to ask questions. You can type a question
in the lower right-hand corner of the screen where you see the Q&A
box; type your question in the smaller rectangular square and hit
the Send button. Also, we are going to pause a few times during the
call to have you ask a live question. To ask a live question, please
press star 1 on the telephone keypad, say your name clearly, and press
pound. You will then be introduced into the call where you will be
able to ask the live question.
Also today, we are going to have a few polling questions that will
appear on the right-hand side of the screen. Without further ado,
it is my pleasure to turn the call over to Margie Shofer. Margie,
the call is yours.
Margie Shofer: Thank you, Jeff. Hello, I am Margie
Shofer in the Office of Communications and Knowledge Transfer at the
Agency for Healthcare Research and Quality. Thank you for joining
us for this Webinar, Preventable Hospitalization Costs and Mapping Tool. This event is the final of three followup events featuring
AHRQ tools that were shared at two workshops held this past December
and January. We focused on the State Snapshots at the end of April
and on the Asthma Return on Investment Calculator last month. We see
all of these events as the first step in what we hope to be a series
of follow-on technical assistance opportunities. If, after learning
more about the mapping tool today, you are interested in further assistance
from AHRQ in using this tool, please let us know.
We are hosting this Webinar in response to interest in the County-Level
Mapping Tool expressed by participants from both workshops. We know
some of you attended one of the workshops, whereas others may have
had less time to interact with the tool. As such, we will spend some
time reviewing the basics of the tool and will then move on to a demonstration
of using the tool. We also will address questions about data raised
at the workshop and discuss interpreting results for a variety of
policy applications. We will conclude with a discussion of future
We would appreciate your active participation, as the primary purposes
of today's Webinar are to: further probe into underlying data sources
and other technical issues you may encounter in using the tool, explore
practical applications for the tool, collect suggestions for future
tool enhancements or modifications, and learn how you envision using
the tool and for what purposes. Again, related to this last point,
we really hope you'll tell us about the types of technical assistance
you might need in order to make better or full use of AHRQ Preventable
Hospitalization Cost: A County-Level Mapping Tool.
Throughout the Webinar, as Jeff mentioned, we will conduct a few
polls. Shortly, you will see the first poll asking whether you have
used the county-level mapping tool. We will mention the polling questions
as we move along the presentation and would greatly appreciate if
you would answer all polling questions. This is a neat feature, so
we really love having it and it would be great if you could all answer
Today's presentation will be given by Melanie Chansky, a health research
scientist at the Battelle Centers for Public Health Research and Evaluation.
Melanie is an anthropologist with a focus on medical anthropology
and community health. She is a project manager for the team that supports
and develops the AHRQ Quality Indicators.
The other person's name on the introduction slide, Marybeth Farquhar,
is my colleague here at AHRQ. She is a QI Senior Advisor in the Center
for Delivery, Outcomes, and Markets. Her office oversees the county-level
mapping tool. Unfortunately, Marybeth was unable to attend at the
last minute, but I wanted to acknowledge her hard work on the tool.
Without further delay, I will hand this over to Melanie.
Melanie Chansky: Thanks, Margie. Hi, everyone.
I am Melanie Chansky, and I am going to be conducting most of the
presentation today. I really appreciate you all being here. To get
started, I wanted to let you have an overview of what we are going
to be talking about today.
Margie did go over that a little bit. I am going to start off by
giving a very broad overview of the mapping tool, just some background
on the tool. Then, I am going to also do an actual demonstration of
the tool, which is pretty cool. I am going to let you guys see it
on my desktop. I will work with it using a few data sets that I have
for testing. Then, for the rest of the presentation, I am going to
focus on the data, both the data that are underlying the tool as well
as the data that are produced by the tool, and the data that you need
to work with the tool. I am going to talk a little bit about how to
interpret results and how to use the results, just given our own experiences
in testing and developing the tool. Finally, I am going to let you
have a bit of an introduction to the plans that we have and I hope
to get some suggestions from you. If any of you have used the tool
and already have ideas, this will be a great time to speak up. If
you haven't used the tool, and anything occurs to you from what I
present today, it will be really great to let us know. I can take
the suggestions back to our development team.
As Margie said, you will have an opportunity to ask questions during
the Webinar. I am going to try my best to answer all of the questions
that you have. If there is anything that I can't answer today, you
will be given information you need to get questions to me in other
ways. And Marybeth Farquhar, who was going to be here, would have
also had a perspective I would have liked, so that's something that
might be missing. Regardless, we'll make sure all your questions get
I am going to move forward. To start off, I am going to give the
overview of the mapping tool. I assume that most of you are probably
familiar with AHRQ's Quality Indicators, which is the overall project
that the mapping tool is part of. As Margie said, I am part of the
team that develops and maintains the Quality Indicators (QIs).
This slide gives you a basic overview that the QIs use existing hospital
discharge data and have several severity adjustment methods that we
actually incorporate into creating the rates. There are now five modules:
inpatient, patient safety, prevention, pediatric, and neonatal indicators.
If you're not familiar with the QIs, and you want more detail than
I just gave, you can go to our Web site:
Now, let's go to the point of today's presentation, the actual mapping
tool itself. This is a relatively new software tool to support the
QI project. This one focuses on helping users to better understand
the geographical patterns of potentially preventable hospital admission
rates for selected health problems. "Selected health problem" is
just a way we refer to the indicators you can use with the tool. There
is also a cost component as part of this tool, as you can tell from
the name. So we also hope that users can use this tool to allocate
resources more effectively by calculating the potential cost savings
that would occur if admission rates were actually reduced.
The mapping tool has three main functions. The first, which I believe
is fairly obvious, is it creates maps. They are State-level maps that
show the rates of hospital admissions for the various QIs that you
select on a county-by-county basis within your State. Another primary
feature is the calculation of potential cost savings that may occur
if the number of hospital admissions for those health problems are
reduced in each county. Finally, there is the ability to place additional
information about your local populations onto maps, which will help
you to indicate the number of people who are at greatest risk for
those health problems in each county.
I will go into more detail about all of these aspects of the tool
in just a little bit, as well as showing you what this means in the
real world, in the actual use of the tool.
I mentioned all of the different modules of the QIs before. However,
the mapping tool does not actually process all of the QIs at this
time. As I said before, this is a relatively new tool. We only released
it on October 31st of last year. Currently, what we have are all of
the prevention quality indicators, as well as all of the area-level
pediatric indicators. Right now, we do not have the inpatient quality
indicators, the patient safety indicators, or the neonatal indicators.
That was my overview of the tool. The next thing I want to do is
go into a demonstration of the mapping tool. Before I start that,
Jeff, is there a way for me to see the results of the poll?
Jeff Gallagher: Absolutely. Let me share them with
you right now.
Melanie Chansky: Great. Oh, great, o.k. The results
here show that 78 percent of you have not reviewed or used the mapping
tool in the past, so I think that this will be great. This will be
a first opportunity for you to see what the tool is like and what
it can do. It is very simple, so I am going to go ahead and share
my desktop with all of you.
Jeff Gallagher: Folks, you should see the screen
changing just a little bit. That is the normal application of the
WebEx product. We are seeing your desktop. Please proceed.
Melanie Chansky: Thanks, Jeff. I have set aside
a couple of icons here that are for me to use for the demonstration.
I will open up the mapping tool. It has an AHRQ icon, like any other
software tool would have for the QI project. What you should be seeing
now is the basic Overview screen that the tool starts out with whenever
you open it up. This is a very simple tool. Right now it is especially
very simple now that we are in the early stages of developing it.
This Overview screen doesn't have any of the functions of the tool
included, but it is kind of like an in-program help screen. You can
scroll down and get some basic information about the tool and what
is going to happen; it even shows sample maps. I will not go over
that because that is why I am doing the demonstration today in addition
to showing you how to use the tool. But this is a resource for you.
As you're using the tool, you can come back to the screen at any time.
We also have several help documents located on the QI Web site, and
you will get the link later to where you can download this tool for
The way you navigate through the tool is by using the series of four
buttons you should see on the left side of the screen. We are currently
on the screen called "Overview." If you click the next
button down, it says "Specified Discharge Data Set." You
will come to a screen where this is where you are going to actually
put in the data that you are going to use to run the tool. Just like
the last screen, there is, at the top, some in-program help about
the file format you need to use to make the tool work, the variables
that are required, etc. Since that's another thing I am going to be
going into, I will not show you that too specifically at this point.
What you have to do, you just need to find your test data set, which
I set a couple aside. I will use test data that I have from California
2001. You have to actually go and select the State and the year before
you move forward. That is really all you do here. You're just telling
the program where to find your data. The real "meat" of
the tool comes in the last two buttons.
I will move to the third button and select the QIs to process. Here,
you can see there is not really a need for much help at the top of
the screen because this is fairly self-explanatory. You can see that
it lists all of the PQIs, prevention quality indicators, in this tab.
It lists the PDIs on this tab. You can select one, you can select
all of them, whatever you want to do. It can run all of them at once
if that is what you want it to do.
I am going to just select one. I am going to select one PDI, asthma
admission rate. At this point I am going to stop here and I am going
to run the tool. The fourth button is optional. I am not going to
do that yet. I will click Submit. The bottom of the screen goes through
a few progress bars, telling you what it is doing at that point. The
last step writes the data to Excel, and it puts up a box that tells
you that the data processing is complete. It also tells you where
to find the outputs for the tool. I click o.k. I am going to close
down the tool and show you what came out of it.
I selected one PDI, asthma admission. The map is called PDI 14, named
after the actual indicator that you selected. Let me increase the
size on this a little. At the top of the screen, you will see the
name of the indicator. You will see the counties within the State;
they are actually labeled. The numbers you see on the map are the
labels for the counties. Those are the county FIPS codes that come
from the U.S. Census. It is just a way to link to the actual counties.
Down in the lower left-hand corner, you will see the risk-adjusted
rate for 10,000 people. This map presents the rates, divided into
quintiles, not based on any national benchmark, just based on the
data within your State. It goes from the green, which is always the
best rates, whatever that is for that particular indicator, to the
dark red, which always represents the worst rates for that particular
indicator. You can see on the map which ones are doing relatively
well for asthma admission and which ones are doing relatively poorly
for asthma admission.
This is the basic map. I will close this down because I think we
have seen what there is to see there. The data that were in that map
were also made into this Excel spreadsheet called PDI. What you see
in this is a list of all of the counties in the State, as well as
the State rate at the top. It is the only benchmark that is really
included in the tool. This is what you could use for doing further
analyses of the data.
Besides the Excel spreadsheet, the mapping tool produces a CSV (comma-separated
values) data file, which might be something you would rather use for
actually running analyses. But the Excel spreadsheet looks nicer.
It is formatted really nicely. I am going to go into a little bit
more detail about what is in all of these columns later. There is
nothing very earth shattering here. You have the numerator (the number
of cases), the denominator, the rate per person, the risk-adjusted
rate per person, the standard error, the difference in the overall
risk-adjusted rates—this column actually compares back to the
State rate, and it only says something in this column if you are significantly
higher or significantly lower than the State rate—and finally,
the cost savings given a 10 percent reduction in numerator cases.
You can see the State rate, which is a lot higher than all of the
county rates. If any of you see numbers that seem quite low or just
out of whack in some way, it is because this is a test data set and
these are not real numbers for any of the counties. You can use those
cost savings in presentations or however you would like.
Let me close this down because I would also like to show you one
other function of the tool: adding in the fourth box. I am going to
go back and do the exact same indicator so that you can see the comparison.
I am going to focus on the map this time rather than the Excel outputs:
California 2001. I will do PDI 14, asthma admission rates, and then
map display options. This adds a feature to the map that shows you
the population at risk in each county for each indicator. I think
that will be a lot easier to explain when I show it to you. However,
in order to do this, you have to have a separate data set. I will
give you more details about that later.
This in-program help screen will tell you everything you need to
know to set up the population data set. I am going to click Plot Secondary
Population Onto Map. Then it also prompts you to tell where your data
set is. After I do that, I click Submit, and it runs through again.
It is really doing the same things, and it tells you the same thing
as it did before, where everything is saved. It always saves your
files in the folder where your data set was located, so that was in
the test data folder.
Here is my map. As you can see, this one adds a feature onto the
map. It looks a little busy here because there are a lot of counties
in California. It adds a stick figure onto each map that, in relative
size, shows you the population at risk within a county. In the lower
left-hand corner, you can see that the population at risk is defined
for this indicator because it is a pediatric indicator. The population
at risk is persons aged 0 to 17. Unfortunately, you can't define it
beyond that in the current use of the mapping tool. There are certain
defined age groups that are considered populations at risk for certain
indicators. That is an intrinsic part of the way the tool is set up
right now. So this map is showing you pediatric populations within
The numbers are divided into three groups, as you can see in the
legend in the lower left-hand corner. The numbers are very low—they
are actually meant to be population numbers, which would be the population
of people aged 0 to 17 within that county. The reason the numbers
are very low is that the data are not real. Otherwise, it would be
a lot bigger because California is a big State. You can see what the
gist of it is. Essentially, if you saw a county that was coated in
bright red and had a large stick figure in it, you would know right
off the bat that county had a lot of people at risk for this and had
bad outcomes for this indicator. That might be a county you want to
look at further. On the flip side, you might see a county that is
coated bright green that does really well with the indicator and has
a large stick figure in it. That can lead to other questions such
as: What are they doing right? What are they doing to make the rates
so low in comparison?
This map is a nice graphical representation of relative populations
at risk, which could lead you to ask more questions and maybe want
to go back and look in more detail at certain counties and the exact
reasons certain counties may see different results. I think we're
coming up to a Q&A period, but I want to do one more thing with
the tool to show you what will happen if you have something wrong
with your data set.
This data set relies on having your variables named correctly. We
get a lot of questions about things going wrong with the tool, and
it often has to do with variables not being correctly named. This
time, I am going to use a file that I purposely have an error in,
this New York data set. I am going to select New York 2003 and pick
any indicator. It immediately knows that my data set is wrong. As
it turns out, it could not find one of the variables. It comes up
with an error message on the screen that tells you exactly which variable
it couldn't find. You know to look in your data set and either find
what was meant to be that variable but might have a different name
in what you have or add that in if it needs to be added. I found that
to be helpful because I have done demos of this before and had that
exact error in my data. This was very helpful in finding that. I am
going to stop there with the demo.
I believe we go to a Q&A session right now. If there are any
Margie Shofer: This is Margie. As Melanie said,
we are going to stop and take some questions that you might have on
the presentation thus far. You can submit your questions two ways.
We can either hear from you on the phone, and we love hearing your
voice, so we encourage you to do that. If you would like to do that,
you can press star 1, record your name, and hit pound. You also have
the option of submitting a question via the Web by going to the Q&A
section that you see on the right-hand side and submitting a question
that way. While we're waiting for questions, I will ask you a question,
Melanie Chansky: Sure.
Margie Shofer: From what year are the underlying
data and how often are they updated?
Melanie Chansky: For the underlying data, we update
this tool once a year. It is updated around the exact same time that
we update all of the quality indicators. Currently, that takes place
each year in February and March. When this tool is updated, every
part of it is brought up to date to the current indicator specifications
and current census data (if there are any new census data to include).
Right now, for instance, all of the QIs are the March 2008 versions
of the QIs. I don't remember if there were any census data changes
we needed to make this year. The main part of the data that changes
is really the QI specifications, so the data are updated yearly. We
may change our schedule for updating the QIs to fall at the beginning
of fiscal years, so you may see another update to this in October
Margie Shofer: I have two questions from Eric on
the Web. First he asks: Please tell us what format and software are
needed for data input.
Melanie Chansky: I haven't tested all of the possibilities,
but any comma-separated-values file should work. The ones I have been
using for testing are in Excel, but if you have something in a simple
text editor, that should work as well. It just needs to be a.csv file.
Also, I am going to ask about this later with a polling question,
but if you have ever used our Windows Quality Indicator software,
you have files that are right there ready to go into this.
Margie Shofer: The second question from Eric says:
Our data analysis staff worries that our county populations are too
small to make this tool useful. Can we lump county data together to
get numbers with statistical significance?
Melanie Chansky: I definitely hear what you are
saying. Right now, the tool is very simple. It is exactly what was
presented to you. At this point, there is not a way for us to do that.
It has a lot to do with the cost of the mapping part of this for us
and what sort of programming sophistication it would take to allow
that. However, that's not to say that you couldn't take the outputs
of this tool and aggregate them yourself. It is just that we don't
currently have that offered as a feature of the tool. It is definitely
a concern that I could see happening in a lot of States, particularly
given how rare some of these indicators are and, for some States,
how many counties they have and how small the counties are.
Margie Shofer: I am going to ask the operator if
we have any questions via the phone.
Operator: No, Ma'am, we have no questions at this
Margie Shofer: I will go back to a couple of questions
via the Web. Wei asks: Are the cost savings data based on charges
Melanie Chansky: It is based on total charges. I
will be giving a little bit more information about exactly what that
will mean for you in the next segment of the presentation.
Margie Shofer: Teresa asks: Can the tool only be
used by HCUP partners?
Melanie Chansky: Well, no. Anyone can use the tool
as long as they have the basic required data elements, which I will
go over in the next segment. The one really important piece is that
currently the cost part can only be used by HCUP partners, which is
probably the worst part about the tool. The reason is the cost-to-charge
ratios that are used that underlie how the tool calculates the cost.
For those of you not from HCUP States, that is something we are working
on. We are looking at fiscal year 2009 for a fix for that. You can
use all of the other features of the tool right now, just not the
cost savings part yet.
Margie Shofer: Hanten asks: Do you have the same
tool for the Inpatient Quality Indicators?
Melanie Chansky: No, we do not. I think that's a
good suggestion and we should try to do more to incorporate the other
modules in the future. Right now, as I said, we only have the Prevention
Quality Indicators and the pediatric ones. The reason for that is
all the PQIs are area based, and the PDIs we included are also area
based. Right now, we do not have the capability to map the provider-level
or hospital-level quality indicators. However, there are a couple
of important points. There are inpatient indicators, and there are
patient safety indicators that are area based. If we were going to
expand to anything, I think you would see us go there first to the
other area-based indicators. We are trying to improve the tool and
make it more sophisticated. I think we are all thinking in that direction
in the future. We have only had the tool out for a short period of
time, so we are still working on improving it.
Margie Shofer: Hanten also wants to know if you
need to buy extra mapping software to use the tool.
Melanie Chansky: Not to use the tool, no. If you
want to do anything more sophisticated, I suppose you could buy extra
mapping software. However, I do not have any on my computer at all,
and it works just fine.
Margie Shofer: Do we have time for one more question?
Melanie Chansky: Why don't we take one more and
I will move on.
Margie Shofer: Bill says that he used the PQI software
but receives the following error when trying to use the cost savings
tool: Index and length must refer to a location within the string.
Melanie Chansky: O.k.
Margie Shofer: Any suggestions on that or should
we save that for later?
Melanie Chansky: That is a question that probably
needs to go to our tech support line because we can troubleshoot more
specifically what your problem is.
Margie Shofer: O.k.
Melanie Chansky: I am going to move forward with
the presentation now. We will have another chance for people to ask
questions later. I will also provide you with the information you
need to get more questions to the QI team in the future. So let me
The next question will be about an overview of the data that go into
the tool. Before I move into it, we have a polling question that I
would like you to respond to. It is about whether you have ever reviewed
or used the Windows Quality Indicators software. I would definitely
appreciate it if you could look at that question and respond to it.
I will come back to that in a minute. Now you can see the question
on your screen.
Moving forward (I did get into this a little bit before), I want
to tell you about what data are underlying the tool and what the tool
can produce. The main thing this tool really needs to run is the current
indicator specifications about the year that the data come from (as
Margie asked me to address this question from one of the participants).
We currently have the tool populated with 2008 indicator specifications.
You could actually find all of these in the technical specifications
document that is located on the QI Web site. It has things such as
the ICD-9 codes that go into the numerator and things like that. The
really detailed definitions are found on the QI Web site.
The next important piece is the cost-to-charge ratio. This is for
the cost part of the tool only. We use the variable total charges
to do our cost calculations. The cost-to-charge ratio allows us to
translate the charges into actual costs. This is why only HCUP partners
can currently do the cost calculations. We have the cost-to-charge
ratio set up where it is linked to an HCUP hospital ID. So if your
State does not have HCUP hospital IDs, you cannot use this right now.
However, the fix that we are working on actually involves using data
from Medicare cost reports. I do not have many details right now because
this is something where we are just getting into learning about the
implications. That fix is coming in fiscal year '09.
The next piece of underlying data is census data, which is how we
have those maps and county codes in the software. If there is new
census data, we update it at that time. However, the kind of census
data we have in the tool is pretty stable. The FIPS codes do not change
very often, but if they do, we make a point of looking for that.
What kinds of data do you need to provide to use the tool? Beyond
the underlying data I just mentioned, you need to put in your own
data set. There are certain required data elements and then there
are some optional data elements related to the cost savings and the
population data. You can see from the way that I went through the
tool earlier that the population data were not required to run it.
I went back in and ran it a second time adding that, but it is not
necessary at all.
I can see the results of the poll right now. It looks like most of
you have not used the Windows Quality Indicators software. That is
our more sophisticated software tool. It is the one where you can
do analyses with your own data sets using all of the quality indicators.
One of the reasons that I wanted to ask if you used it before is the
variables that I am going to be going into on the next slide. The
Windows Quality Indicators software has this very nice wizard that
runs through your data set and helps you to map whatever variables
you have to the variable names that we look for in the software. For
the 11 percent of you who have used the Windows Quality Indicators
software, you are probably familiar with the wizard. Unfortunately,
we do not have this in the mapping tool right now, so you really have
to have all of your variables named correctly for this to work. But
that is one of the easiest things to do. It is just nice to have that
option of leaving your data set the way you have it and telling the
software how to map it to the appropriate variables.
Here are the variables you have to have for the software to run.
If have you this minimum set, the software will run fine. I am not
going to run through all of these because you can read them, I am
sure. I just want to point out a couple of things.
One is the ageday variable. I should have placed an asterisk next
to that because it is not required for everyone; it is required for
people under 1 year old, but if you do not have it, the tool will
run just fine.
For the primary diagnosis and primary procedure codes, you will probably
have many others in your data set, such as secondary diagnoses and
procedures. It will not be using those for this tool, but it does
not matter if your data set has more than what is listed here. It
will ignore those variables.
All of these variables are also required if you used the Windows
Quality Indicators tool because these are the basics of what we need
to get indicator rates. That's why we really have to have these in
the data, in your data set.
The next slide is about optional variables. These are all about the
cost savings piece, so there are just two: the total charges variable
and the hospital ID. If you include both variables, a cost savings
number will be calculated. As I mentioned before, we are going to
make a fix for this so any State can use it. Right now, there are
also some confidentiality issues associated with HCUP States, in that
cost-to-charge ratios are not data that can be publicly available
for everyone. There are some fixes in the tool I am not going to go
into, but it calculates cost differently depending on whether your
particular H CUP State allowed us to use that CCR number. If you want
to know more about that, you can talk to me offline or contact our
technical support for more of the details about what the calculations
Let me share my desktop with you again to show you what an input
data file needs to look like. I am going to pull a file I used to
run the program before. As you can see, all of the variable names
are in the first row. I think that beyond that, this is a CSV file
and you are looking at it in an Excel format, so it is in columns.
If you had a CSV file, all you would really need between the numbers
are commas and it will know how to work with that. As you can see,
you can see some of the required variables: PSTCO, patient, county
of patient residence, age, ageday, admission source, admission type,
etc. (They do have more diagnosis codes than we used, but those would
be used if you were using the quality indicators software.) Hospital
ID, MDCs, procedure codes, and then the total charge information at
the end. This is all the information you will need to make the program
work. I think you may be able to set it up in a variety of different
ways, but we can always help you troubleshoot if you have issues with
Let me bring you back to the presentation now.
For the optional population data set that goes into this, this was
the one that lets you make the stick figures on the maps. It has to
be a separate file from your main data set. There is just no way to
make it one file at this point in time. So you have to have your main
data set and then have this separate data set that is just going to
have a few variables. You have the county, which is always the State
FIPS code followed by the county FIPS code. State is either one or
two digits. County codes are three, so you will have either a four-
or five-digit number there. Then you have sex, coded one for male,
two for female. And age, which is really age groups. You can see how
the coding works right there. Then it is population, and it is population
by sex and age cell.
I am going to share my desktop again to show you what exactly this
means. The data set will look very simple. I think I accidentally
stuck that in the recycle bin, so I will pull that out. There we go.
You can see it is a simple file. It is also CSV, so it does not have
to look like this in Excel. Each county will essentially have six
cells, like you see here. This is County 001, which has six cells.
It is basically just because of the age groups. The males are one;
the females are two, and then each of the age groups and the population
that corresponds with each of those. It is like I said, very simple.
And it will allow the stick figures to be placed on your map.
Let me go to the next slide. In terms of data problems that we find
from users, most of the problems are typically related to the user
data sets that go into the tool. Before this Webinar, I did some review
of what kinds of questions people have been asking us. It is really
about the kind of data sets that go into the tool. The problems can
vary a lot, but we do have a whole team that can provide you with
technical assistance on your data set. If the information I just provided
does not result in a data set that works with the tool, I think we
have a pretty good team who can help you troubleshoot whatever problems
you might have.
I am not going to address specific problems because I find they do
vary by each user. I could not find one problem that came up enough
for me to spend time on it. It is a lot of little things, but we always
are here to help.
I think I will speed it up a little because I am a little behind
where I want to be.
I will quickly go over the outputs that come out of the tool. All
of the outputs that are created by the tool, which I showed you before,
are automatically placed in the folder where your data set is located.
You get a CSV file of your results, an Excel file of your results,
and the maps themselves. I went through what the CSV and Excel files
include before, so I think you know that information and I won't read
it to you again.
There will be a separate map created for every QI that you select,
and it will be named after the QI. In the demonstrations, I only selected
one QI at a time so I only got one map. Had I selected multiple indicators,
I would have gotten as many maps as indicators. You cannot put more
than one indicator on a map. You can open the maps using any graphics
program or picture viewer. I opened them using what they were default
imported into on my computer, but I have also opened them in other
programs depending on what I needed to do with them. You should have
some flexibility there.
Now I have come back to where I wanted to be.
Interpretation and use of results. I just wanted to tell you a little
bit about what we were thinking about as we created this tool and
what sorts of things we were envisioning people might use it for.
There are some possible uses for the mapping tool data. These are
some things we brainstormed when we were coming up with this. Since
the tool is new, we do not have a lot of examples of what people might
have done. You can use it for public reporting because it provides
county-level information, if you want to report at a lower level than
the State. Intervention targeting is a big thing because you could
look at which counties have the worst rates as well as the highest
populations of people at risk for certain conditions. If you have
to make decisions about where to do an intervention, we can see this
tool as being helpful for that.
The next bullet is related: tracking intervention impact. You can
use this tool over time to see if the rates are coming down, if the
county that was once bright red is moving into a better category.
Finally, the identification of best practices is another important
thing. What about the counties that are bright green?
Sorry. Excuse me for just a second.
Jeff Gallagher: Folks, we are going to pause for
a few short moments so she can take a sip of water or such. Remember
to go ahead and continue to type your questions in the Q&A area
you see in the lower right-hand corner. Also, when we come up to our
live questioning time, you can also press star 1 on the telephone
keypad. Remember that you need to speak your name clearly and then
press pound, and you will be able to ask your live question. We will
be back to the questions and answers in a few minutes. We will see
if she is able to continue.
Melanie Chansky: I think I can. I am sorry. This
happens whenever I get a cold and talk too much, so I apologize.
We find the data leave you with more questions than answers. Are
the rates reasonable? Do they represent significant quality concerns?
Then the Excel data produced really need to be manipulated to be more
appealing and more dynamic.
To address the first points about the data: Are the rates reasonable?
Are they actual concerns? Within the tool, the rates are benchmarked
by the State, but that's the only thing you have. If you consult
the PQI and PDI user guides, which are provided on the QI Web site,
you can get some information about national rates for the indicators.
HCUPnet is another source that presents the PQIs at the national
level. The National Healthcare Quality Report (NHQR) and the National
Healthcare Disparities Reports (NHDR), which are AHRQ products,
also present selected PQIs at the national level. These could help
you compare what you are seeing in your State with what is going
on nationally to see if what you are seeing is a quality concern
and if the rates are reasonable.
We really suggest focusing on the maps for presenting the data and
focusing on the Excel output to do more analyses or to use them as
a source for other graphics, especially if you have more advanced
graphics programs than what we provide you with. Another possibility
is using the data to create a concise narrative data summary that
shows what is going on within your State, or within certain Counties
that you are particularly interested in.
Future plans: I only have a little information on this right now.
We are looking for suggestions. Right now, we have a couple of thoughts
in various stages of reality. One thing that is very likely to happen
is that this mapping tool will be incorporated into our Windows Quality
Indicators software. You will get some of the benefits I mentioned
from the software, such as not needing your data sets to conform exactly
to our variable names, and things like that. You will have wizards
to help you map your variables onto the names that we want for our
Another thought we have is not quite in the programming stage yet.
We are still thinking about how to do this. It is allowing for mapping
below the county level. For instance, ZIP code level or any other
ideas people might have. We were thinking ZIP code and maybe showing
hospitals on the map as well. We have Q&A coming up, so if you
have any other ideas for ways we can improve the tool, you can go
ahead and submit them to the Q&A or any way you want to let us
know about that.
I am almost to the end of the presentation. We actually have two
versions of the mapping tool available. I showed you the Windows version.
We do have a SAS version as well. They are both available for download
at the link that is up on the screen right now. If you go there, you
will find both versions of the mapping tool, as well as the help documentation
that goes along with these tools.
At this point, we want to put up our final polling question for you
to answer about what kind of technical assistance you might be interested
in receiving with the County-Level Mapping Tool. This is really important.
This is one of the main reasons for this Webinar. If you could respond
to that, we would really appreciate it. You see Margie's contact information
up there. For a specific question about the tool—
Margie Shofer: Actually, Melanie, I will go over
that in my closing. If you are ready, we can go to more questions.
Melanie Chansky: I am sorry. That's fine. Sure.
Margie Shofer: We will give folks a little time
to answer the polling question. I have some questions that people
have been sending over time. If you want to ask questions and you
want to ask them over the phone, you can press star 1, and you will
record your name and hit pound. I think everybody has grasped how
to use the Q&A button. Let me pull up some of the questions.
We had questions from two different people, from Georgette and Diane,
both asking questions about if you are either in a low-population
State or if you have counties that do not have any hospitals, how
can you go about adjusting for that?
Melanie Chansky: I might want more clarification
about what adjusting you want. Let me answer for the hospitals. If
you have counties without hospitals, it is not a problem with the
tool. The tool uses only the county of patient residence. One of the
reasons we did this was because if we had this calculated based on
hospitals, those counties with hospitals would appear to have artificially
high rates for the indicators. We do not use that in the tool at all;
we use just the county where the patient actually resides.
In terms of adjusting for low populations, I am probably not the
best person to answer this. I could take a stab at it, but I think
you want to talk to somebody who is much more comfortable with the
details of the calculations in the tool. I do not want to lead you
in the wrong direction. It is something we could definitely discuss
further offline. This is the kind of question we routinely get in
our technical support, and we have many staff who can help.
Margie Shofer: I have two people who are asking
questions about DRGs (diagnosis-related groups). If your hospital
billing isn't DRG based, is that a problem and can you still use this
Melanie Chansky: It is a problem right now because
DRG is one of the required variables. At the moment, if you do not
have DRGs, you cannot run the tool. However, it is a good point to
make. Maybe that is something that needs to be incorporated in the
future, some flexibility in whether that needs to be required. I am
trying to take notes as we talk today, so these questions will help
us in the future; we can make those kinds of changes. And I think
that is a really important thing to point out.
Margie Shofer: O.k. I am going to ask a question:
Does the tool allow for mapping at ZIP code or medical service area
Melanie Chansky: Right now, no. Right now, you can
only map within one State at the county level, so ZIP code and medical
service areas are exactly the kinds of things we want to move into
to try to make this tool more useful to users. We are looking into
that actively, but we are not to the point where I could say that
it is going to be implemented.
Margie Shofer: I am going to ask the operator, do
we have questions by phone?
Operator: Yes, Ma'am. You have one question coming
from the line of Julia Brown. You may proceed.
Julia Brown: Hi. I have two questions actually.
One question, you mentioned how the tool generates quartiles for one
State based on your State's data. So my assumption would be, if I
wanted to compare to another State, the quartile amounts may be different,
and I would have to explain that if I were demonstrating that?
Melanie Chansky: Yes, unfortunately that is the
way the tool is set up right now.
Julia Brown: O.k. The second question that I have,
if you have mapping software of your own, can we simply overlay your
data and use our own tool?
Melanie Chansky: This is a question I have gotten
in the past, especially from people who have invested in more sophisticated
mapping software. The Excel outputs contain all of the rates used
in our map. So you could take the outputs that are produced in Excel
and CSV format and simply use that to make more sophisticated maps.
Julia Brown: O.k. Thank you.
Melanie Chansky: Thank you.
Margie Shofer: A few of you have asked for us to
e-mail you the URL to the downloads. We will do that after the call.
All of the things we mentioned and if you asked questions, we will
make sure we get back to you after the call. I have a question from
Hanten. I am not quite sure I understand exactly, but she asks, can
you modify this to display IQIs by hospital locations just for display
purposes? I know you said you are not using IQIs with this tool right
Melanie Chansky: Can you repeat that one more time?
Margie Shofer: She asks, "Can you modify the
mapping tool to display IQIs by hospital locations?" And then
she wrote in parentheses, "just for display purposes."
Melanie Chansky: Right now, no. However, if I am
interpreting the question correctly, I think that is one of the things
we want the tool to be able to do in the future. So look for something
that incorporates the hospital locations in the future. Right now,
it does not have the capability.
Margie Shofer: Looks like I have another question
from Diane, who wants to know how the software handles missing data
in the file.
Melanie Chansky: That is a good question. It has
been a while since I tested that. Off the top of my head, I don't
remember exactly how it is handled. I do not want to say that it handles
it well and then be wrong about that. That is something I would suggest
you ask offline so I can test it to see what happens. We did all of
this when we were testing the tool prior to release, but I have not
done it recently enough to remember how we finally addressed that.
Margie Shofer: I think we are about out of time
for questions. I want to thank you all for your thoughtful questions
and your participation in today's event. We hope the discussion was
helpful to you. If you have any questions about follow-on technical
assistance opportunities, please do not hesitate to contact me, Margie
Shofer. As you see, my contact information is on the last slide.
If you have any questions or comments about the tool, or would like
to request a copy of the mapping tool, please send an e-mail, listed
in the second bullet. For more information about the suite of tools
we developed for this project, you can use the link listed on the
last bullet. Again, Melanie did ask if you have any thoughts or suggestions
about ways to improve the tool, so feel free to e-mail those to the
e-mail listed in the second bullet. Thanks again, and this concludes
our Webinar. We look forward to hearing from you.
Jeff Gallagher: Ladies and gentlemen, thank you
so much for taking time to join us today. You now may disconnect.
Please stop closed captioning now.
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