**Transcript of Cyberseminar**

**VIReC Database and Methods Seminar**

**Assessing Inpatient and Outpatient VA Healthcare Use**

**Presenter: Denise Hynes, PhD, RN**

**December 2, 2013**

*This is an unedited transcript of this session. As such, it may contain omissions or errors due to sound quality or misinterpretation. For clarification or verification of any points in the transcript, please refer to the audio version posted at www.hsrd.research.va.gov/cyberseminars/catalog-archive.cfm or contact the VIReC Helpdesk at virec@va.gov.*

*Moderator:*Good afternoon and welcome to VIReC’s Database and Methods Cyber Seminar entitled “Assessing Inpatient and Outpatient VA Healthcare Use.”  Thank you to CIDER for providing technical and promotional support for this series.

Today’s speaker is Denise Hynes.  Dr. Hynes is Director of the VA Information Resource Center, VIReC, and Research Career Scientist at the HSRD Center of Excellence at Edward Hines, Jr. VA Hospital in Hines, Illinois.  Dr. Hynes holds a joint position at the University of Illinois, Chicago, as Professor of Public Health and as Director of the Biomedical Informatics Core of the University’s Center for Clinical and Translational Sciences.

Questions will be monitored during the talk and will be presented to Dr. Hynes at the end of the session.  A brief evaluation questionnaire will pop up when we close the session.  If possible, please stay until the very end and take a few moments to complete it.  I am pleased to welcome today’s speaker, Denise Hynes.

*Dr. Hynes*Thank you, everybody.  I’ll count on Arika and Heidi to tell me if the volume needs to be adjusted.  Since I have you on speakerphone, periodically, again, I’m in Chicago.  You’ll hear the L in the background powering by.  Hopefully, we won’t get a lot of other noise.  Okay.  Arika, so do I need—are you going to flip these slides, or do I need to do something with these?

*Moderator:*If you can flip them, that’d be good.  You see in the lower left hand, but if you prefer I do it, I can do it as well.

*Dr. Hynes*I’ll go ahead and forward them.

*Moderator:*Okay.

*Dr. Hynes*Thank you.  Thanks, everybody, for being on.  We have a large crowd today, so I’ll try to make sure that we have some pauses along the way for questions.

*Moderator 2:*That was me.  I’m just bringing up your whiteboard there.

*Dr. Hynes*Very good.  Thank you.  One of the questions that we have to get us started and to get you all thinking about how you might use inpatient and outpatient data in your research is tell us a little bit about what particular aspects of health care use are you interested in measuring?  If I remember right, we can write on this whiteboard.  Right?

*Moderator 2:*We can write on this whiteboard for the audience.  At the top of your screen, there are some annotation tools.  There is a capital T.  If you click on that capital T and then go down to the screen, you will be able to write in your text right on the screen there.

*Dr. Hynes*I’m just giving an example here, and I’m typing in a couple of things that I’ve looked at with inpatient and outpatient data.  I would encourage those of you who are manning the computer, if you’re in a group or individually at your computer, to just use some of the tools at the top.  I find it easiest to use the text, and then you can just type.  If you’re really good at scripts, I guess you could use a little pencil, but I don’t think I would be very good at that.

We’re seeing some things like—hopefully, you can all see this, but I’ll just say it out loud to encourage those who haven’t written anything else.  Mental health service use, cancer treatments, outpatient and home‐based use, hospital readmissions and ED visits, psychiatric appointments, encounters by type of provider, readmission rates for specific diagnoses, different types of utilization.  Let’s see.  Mental health visits, diagnoses, prescriptions, pain diagnoses, and prescriptions.  Okay.  This is great.  We could even do one of those little word puzzles.  I don’t know if things show up on the screen, Heidi, in particular ways, but it’s nice that it doesn’t overlap all the time.  We’re going to cover some of these today.

The inpatient and outpatient health care use files in the VA are going to be the anchor of what we talk about.  What we won’t be talking about today are some of the examples that I saw written in there, and that’s about prescription use.  We do have a separate lecture that talks about some of the data sources that are much better at addressing prescription use for people seeking health care in the VA.  The data sets that we’re going to talk about today and some of the examples will not address that.  We will be talking about anything that—events that occur in the inpatient setting, events that occur in the outpatient setting, mostly around visits and inpatient stays.  Some of the information that you can capture, that goes along with that, we’ll highlight today.  Prescription use will not be among them.

Okay.  Let’s move on to the next one.  That gets us thinking.  This is our agenda for today, if you will.  I’ll just introduce a couple of examples of how health care use has been measured in VA studies.  We’ll talk about two examples at the end when we get to examples of VA studies where we actually highlight two papers.  We’re going to talk about some examples of using the—Medical SAS datasets is what you’ll hear me refer to them a lot.

Here’s an example of—from Susan Frayne and colleagues—this is an article that was published in *Journal of Rehabilitation Research & Development* in 2010.  They looked at how an algorithm about looking at mental illness‐related disparities and length of stay could be used.  The algorithm was focused on choice, and they looked at data from 2002.  They tried to track inpatient use in the subsequent year after they identified their cohort.  They specifically used the VA inpatient Medical SAS datasets, a major topic for today.  They also looked at some other datasets.  Their particular health care use constructs that they looked at were inpatient events, the fact that it occurred, and then also the length of stay.

Another example is one by Steve Luther and colleagues.  They took a little different approach.  They were looking at breast cancer surgery, specifically, breast‐conserving surgery for women treated at the VA.  They looked at—their study focused on looking at the rates of use of this particular type of surgical procedure.  It was a retrospective study, and they looked at data in 2000 to 2006.  They specifically looked at data sources that included VA inpatient and outpatient Medical SAS datasets.  They also looked at the VA Cancer Registry and some other data sets that we’re not going to talk about today.

Both the inpatient and the outpatient SAS datasets are an anchor for our lecture today.  They specifically looked at an event that I’m calling surgical procedures.  A particular type of surgical procedure.  That just gives you an idea of the range of possibilities that you use the Medical SAS datasets for.

Let me just dive in a little bit, and let’s talk about the Medical SAS datasets in particular.  I want to start with another poll because I’d like to get an idea, again, of your own experience with using the Medical SAS datasets.  The poll is posted here.  Goes from one to five.  One being you’ve never used the Medical SAS datasets.  You don’t even know what they are for sure.  Two being somebody who would be in a category of a five, a frequent user.  You’re very familiar with it.  If you’re not sure what it is, you can always do the no vote.

It looks like we have a lot of novices in today’s lecture.  One is never used.  We have 60‐some percent of people who have never used the VA Medical SAS datasets, which I personally find surprising because it’s the bread and butter.  Hopefully, we can fill you in on all this information.  Then, all the other categories.  Two, there’s about 15 percent who would rank themselves as a two, 10 percent as a three, 5‐1/2 percent as a four, and 8 percent as a frequent user.  Great.  It looks like we have some novices in here, and that’s exactly what we like to see.

Let’s move this along here.  I’m going to try and stop for questions after my next section.  As questions come up, if you want to put them in the Q and A, we can use that as a way to queue up some questions.

I want to just give you an overview of the Medical SAS datasets, and it might be that we’ve educated so many folks in using some of the other data sources that the MedSAS datasets are ones that folks feared were going to be gone by this time.  They’re still here.

The inpatient and outpatient datasets are comprehensive datasets for national VHA health care delivery.  These are the inpatient discharge summary information and the outpatient summary information.  They’re hosted on a mainframe computer at the Austin Information Technology Center in Austin, Texas.  They’re often referred to as the inpatient and outpatient datasets.  They are created from information in VistA.  They are available on a quarterly basis.

Researchers are advised to use the annual closed out datasets.  There are some datasets that are available on a quarterly basis, but the final end of year dataset is what has been shown to have, if you will, the best and final in terms of accurate data for a full year.

My throat’s a little scratchy today, so you’ll hear me drinking water probably too.  Hopefully, it’ll suppress any cough.

A common element in these datasets is a patient identifier.  We often refer to it as a scrambled SSN, and it is common across many of the VA datasets that are summary datasets like this.  That’s a particular advantage because it allows you to link datasets across patients, so whether they’re an inpatient event or outpatient event—and some of the other datasets will talk over the course of the year—this unique patient identifier allows you to, if you will, string up records across these datasets.

It’s important to note that for those of you who aren’t familiar with some of the data transitions that are going on in VA, the National Patient Care Database—I’ll refer to this a little bit later—but it’s the relational database that produces the outpatient data.  It has been going through some transitions.  The plan was that it is going to phase out and end at the end of fiscal year 14, which is what we’re in now, and that the FY15 outpatient utilization data will only be available in SQL format.  It will be generated now by the new data warehouse called the Corporate Data Warehouse or the CDW.  This is important to take note.  We actually thought it was going to end this year and thought we were preparing folks for it to be basically end of FY13, but that hasn’t proven to be the case.  I guess that next year is where I would strongly suggest, given that we’re now in an extended year, if you will, it does seem like this will happen next year.

If you’re planning a new study, you should take note and try to anticipate that you might have to use data from multiple sources.  If you’re really digging in and wanting to use the MedSAS datasets for the outpatient, you’re going to be getting it from a little different format next year.  We are working hard to make sure that to the extent possible, these datasets can be reproduced, if you will, in the same format that the MedSAS datasets are from the Corporate Data Warehouse, but I expect that they will be a little bit different.

Currently, the VA data flow to the Medical SAS datasets I refer to the VistA system.  For those of you who are new, we often refer to the VistA or CPRS to refer to the electronic medical record that the VA uses.  I might switch back and forth between those terms.  Be that as it may, it originates in the local electronic medical record.  These data are brought up to the Austin Information Technology Center in particular routines.  They put together data in this relational database format called the NPCD data.  It’s managed by National Data Systems, NDS.

They break out these data into these two categories.  Outpatient datasets.  There are four datasets that comprise that.  Acute care, extended care, observation care, and non‐VA care.  Then the outpatient datasets that also have subdatasets within them.  The visit event and inpatient encounters.  I should note that within the inpatient datasets, they’re all asterisks because they also contain, within each of those categories, main, bed section, procedure, and surgery within each.

A little bit about what those four datasets within each of those categories contain.  The main datasets are basically a summary of the entire stay.  There’s an episode of care.  It includes demographic information.  The bed section datasets are segments of stay defined by a specialty of the physician managing patient’s care.  It’s not necessarily a physical location of the bed section like Four East or Seven North, but it corresponds to a particular specialty of physician care.  Procedure datasets are information on up to only five procedures on a given day.  If there’s ten procedures entered, the only thing that you’re going to see in this procedure dataset is the five.  Surgery datasets are information given on up to five surgeries on a given day.  Same caveat applies.  For acute care, the datasets often are named as follows on this format here.  I’m not going to try to read that.  The two‐letter reference code below corresponds to the particular file.  The main is referred to as PM, bed section PB, etc.

You can see the dates are different for the legacy of these data.  The oldest dataset is the main dataset.  It goes back to 1970 and forward, whereas the more recent dataset, the procedure dataset, was established in 1988, and it goes forward.  All the datasets go forward.  How far back you go depends on how much—it’s really the less detail is in the main, and that’s information that goes back the farthest.

Some more information about the outpatient datasets.  These are the three types of files that you find for the SAS outpatient datasets.  The information in the visit file is services provided to a patient in a 24‐hour period at a single facility.  Sometimes emergency room visits can show up in the visit dataset, as well as visits to a clinic.  Event datasets provide information about individual outpatient encounters.  Every encounter a patient has.  Inpatient encounters.  This is not such a self‐explanatory title for the file name.  It actually provides information about professional services received during inpatient stay.  We like to sometimes think of it as consults.  If a patient has an Infectious Disease consult while they’re an inpatient, it would ideally be recorded in what’s called the inpatient encounter dataset.

Again, here’s the format for the files within the outpatient dataset.  Visit file is SF, event SE, inpatient encounter IE.  Again, visit file goes back to 1980, whereas the more recent dataset that was constructed, the inpatient encounters, is only since 2005 and forward.  These kind of aspects are important when you’re thinking about especially longitudinal studies.  I haven’t seen any recent studies that have gone back as far as the datasets go, but I’m sure some creative minds out there might think of ways to use some of the more historical data.  It probably is historical now.  Historic data.

Some things to think about.  Whether distinctions between the types of files.  The visit and the event file are both types of outpatient Medical SAS datasets.  The visit file includes information about outpatient clinic stops, whereas the event file provides information during a 24‐hour period during a facility visit.  The event file would include the primary care clinic stop, the ophthalmology clinic stop, the physical therapy stop.  Any stops the patient has at one facility in a day, whereas the visit file will have one record per stop.

Those are important things to think about, depending upon the type of study that you have.  You might want to use one file versus the other.  Clinics are identified using what’s called clinic stop codes.  For those of you who are new to the VA and you’re still getting up to speed on some of the acronyms, these are equivalent to the Decision Support System or DSS identifiers.  Clinic stop codes equals DSS identifiers.

Some important definitions.  The primary clinic stop code, annotated CL, identifies production units or revenue centers for outpatient care, whereas the secondary clinic stop code, the CLC, specifies the team, the service, and the funding.  The combination is very useful because it gives you information, as shown here on this slide.  For example, in this first one, it’s 216203, 204, and 210.  The last three digits in this label here tell you the specific clinic stops where the CLC is happening, whereas the first digits are telling you which—sorry—which primary clinic stop code it refers to.  The combination is important when you’re trying to identify very specific clinic stops.

Let’s talk a little bit more specifically about the outpatient visit file, the SF.  Again, it has one record per visit.  We just put some information here from the end of fiscal year 12 so you can get an idea of the kind of information that’s there.  Twenty percent of the first 3,000,000 records that we just queried were for laboratory.  Twelve percent were for primary care medicine.  Five percent were for telephone primary care, and so on.  You can just see the kinds of clinic stops that are occurring here.  Again, up to 15 primary clinic stops per visit at a given facility are recorded here.  It does not include diagnosis or procedure information, however.

The event file is one record per clinic stop.  Again, shown here in this table.  Gives you some idea of what kind of events in the secondary clinic stops are occurring in FY12.  In the first 3,000,000 records, again, 63 percent had no secondary clinic stops.  If that’s a field that you depend on, this is something you should take note of.  Eight percent were for nursing.  Four percent were for social work service and so on.

There’s one secondary clinic stop per record available.  There’s no limit on the number of records per day that a patient can have in the SE file, and it does combine diagnostic and procedural information into one dataset.  In the event file, it includes ICD‐9 codes, up to ten diagnoses per record.  It includes CPT‐4 codes.

Until 2004 there were 15 procedures, and there were no repeats allowed.  Since 2005 they’re upped the number of procedures to 20, and repetition is allowed.  Since FY2003 an encounter ID was added, which allows you to link the event dataset with cost data provided in the Health Economics Resource Center, HERC, outpatient average cost dataset.  Those of you who are interested in some economic outcomes.

Some details about the inpatient encounter file, the IE.  This is the one that is for consults, for example, that occur during an inpatient stay.  It excludes services in the outpatient SE file.  If something is recorded in the outpatient SE file, it should not occur in the outpatient encounter file.  A choice is made where things should occur.  Data available is beginning in 2005.

Again, here you can see we did a query of the first 3,000,000 records.  The top five primary clinic stops found in the IE file were for—9 percent were for x‐ray, 8 percent were for recreation therapy service, and so on.

Let me pause here for questions.  I’m not seeing any questions come up, but I’ll check with Arika to see if she’s seen any.

*Moderator:*We do have a couple.  One question is why are outpatient SAS datasets going away after FY14, and what suggestions do you have in accessing if SQL programming is limited? 

*Dr. Hynes*One of the reasons that the datasets are going away is because it’s inefficient to have redundancy with information systems.  The goal is to generate an outpatient MedSAS‐like dataset from the new system.  That remains to be seen if that’s going to occur.  As far as planning studies, I think that if you’re getting going now and you want to get a head start and work with current data in FY13 and FY14, you should proceed in anticipation that the major data fields will be made available.

The thing to keep in mind is these Medical SAS datasets are datasets that are used not only by researchers.  Actually, it’s much more frequently used by the business side of VHA.  Researchers, as well as operations, the business side, are using these data.  There’s a lot of stakeholders involved.

I’m very positive that information that we rely on heavily is going to be available in whatever format will be available soon.  Whether the datasets will be provided in SAS or whether they’ll be provided in SQL is less certain.  We are certainly advocating for the new datasets to be provided in SAS because it just makes more sense.  If you’re going to do any statistical analysis, you need to get it out of SQL anyway.  There are some particular uses of SQL that do enable particular types of analysis, so it’s also useful for other kinds of work.  That said, if you want to become more educated about SQL, I think that’s a great idea.

There’s lots of educational opportunities out there, both in the VA and outside the VA.  If you’re interested in something very specific, send us an e‐mail at virec@va.gov, and we can let you know what we’ve done with some of our staff and what has been done in the VA as well for SQL training.

*Moderator:*Great.  How about one other question, since we’re midway?  Can you speak to the pros and cons of using scrambled SSN versus patient ICN?

*Dr. Hynes*Wow.  I think you’ve stumped me.  I’m not sure what patient ICN is.  It’s not something that I’m aware of that is retained in the Medical SAS datasets at all.  If you’re referring to an individual Social Security number, that is something that, by virtue of using the scrambled Social Security number, is at least some measure of additional privacy protection when we’re working with all these data.  It is unique.  It goes across.  You’d have to educate us about what an ICN is.

*Moderator:*Okay.  There are more questions.  Let’s take them towards the end.

*Dr. Hynes*Okay.  Thanks, Arika.  Finding information in the Medical SAS datasets.  Obviously, I’ve done a very superficial introduction of what’s in the Medical SAS datasets.  For further study on this topic, I’m going to refer you to some of the documentation that has been generated over the last several years.

Here, on this screen, we highlighted the Research User Guides.  We also turned that into an acronym called RUGs that’s on the VIReC intranet website.  You should keep in mind that the RUGs are something that we actually produce at VIReC, but it’s definitely in consultation and coordination with data owners.  We really work hard to try and make these as accurate and up to date as possible.

That said, I would really like to see another whiteboard experience here, and I would pose the question to you of where do you usually go to find information about VA data?  Do you go to the VIReC website?  Do you go to the Internet VIReC website?  The intranet VIReC website?  Do you go to National Data Systems website?  Do I have to do something so that we can work on our whiteboard here?

*Moderator 2:*There you go.

*Dr. Hynes*Thank you.

*Moderator 2:*Pulling it up now.

*Dr. Hynes*Thank you.  Anything, places that you go to find out information.  Do you talk to your colleague who has already printed the RUG out and it’s sitting on their desk?  We’ve actually heard people tell us that.  Do you go to the VINCI website or the new VHA data portal that links you to actually some of the same information?  I’m seeing a lot of different things.  I guess it—of course, it depends on what datasets you’re using.  The VSSE is very useful if you’re using VSSE datasets.  But the VSSE will probably not give you good documentation about the MedSAS datasets.  This is very helpful.  Good.  No big surprises here.  I will encourage you, in this lecture, anyway, to take a look at the VIReC website, and we’ll have the URL in different places in the lecture.

Also, a new data portal, really a website. We call it a VHA data portal because it has information in one place about a lot of the national datasets, and it’s not geared just to researchers.  It’s geared towards a broader audience.  It can take you to the same information.  Sometimes, depending upon the research projects that you’re engaged in, especially if you’re partnering with colleagues who are on the business side or operations side of VA, you may also be interested in some of the types of information that is provided by some of the other stakeholders, such as the data quality office.  Great.  Thank you, Heidi.

Thank you, everybody, for participating in that.  It’s very helpful to know where you get your information from.  Let me move on here.

Finding information on the VIReC website.  I’m just highlighting here—this is a screen shot from when you go to the RUGs.  If you do that link that I introduced you to, it takes you to the most, our catalogue of RUGs.  You can see some other RUGs on here, but the MedSAS ones are here.  Our most recent release is from 2011.  We usually do them every two years.  We probably won’t do one this year for 2013.  We’ve been hearing different things about how useful the RUGs are and are trying to produce, especially pieces of the RUGs, as opposed to waiting for different, all the components to come together.

This is what it looks like as well.  It provides information about the specific variables and their dataset locations so you can see the list of variables in this table format.  We also try to highlight information about the different types of datasets that I introduced you to that correspond to the subdatasets within them because some of the data, the variables, are consistent throughout the different files, and some are not.  Then the variable name.  We try to produce detailed information about the variable name and remarks about some changes in naming convention or a little more information about where it came from.

For example, here you have DXLSF, which is one that sometimes can be confusing for folks.  We try to emphasize that this is distinctive from the principal diagnosis, DXPRIME.  We try to put a little additional information in there that speaks to using the particular variables and when you’re trying to make some decisions about constructing your analytic datasets, things to be looking out for.  Definitely take a look at the RUGs.  We hope that those are useful to you and some of the variable and information about the variable details.

Some other things you should know about finding information, the inpatient MedSAS datasets, with regard to admission and discharge.  All the inpatient datasets include the following variables.  It’s in all the files.  That’s really important when you want to do any kind of research that links across not only the patient ID but the particular types of information.  If you’re only interested in veterans’ health care use, I added these couple of slides here because we had a lot of questions lately.  You should be aware that in the MedSAS datasets, these are operational datasets indicating the types of health care events that are occurring on the inpatient and the outpatient side.

VHA serves employees who are veterans, employees who are nonveterans, and sometimes family members of veterans who might also be using the VA.  If you want to exclude nonveterans from your study, these are particular facts you should take note of.  There are a small number of patients, only 1 percent to 2 percent, who receive health care in VHA who are not veterans.  I just mentioned some examples.  In the Medical SAS datasets, there are some flags in there for whether somebody is a veteran.  In 2006 the Health Economic Resource Center published a technical report as a guide to identifying nonveteran records.  Of course, this is something that you need to keep in mind.  You might need to use other data sources to best identify veterans or nonveterans, whatever you’re interested in.  I include here—and I’m not going to go into detail in the interest of time—HERC’s methods.  I would also encourage you to take a look at the VA vital status file to identify nonveterans as well.

There is some information here.  I do want to highlight some specific aspects of some of the key variables that folks rely on in the next couple of slides.  Bed section is one that sometimes can be confusing.  It identifies specialty of the physician managing patient’s care.  It’s found in the bed section and the procedure datasets.  It contains something called the treating specialty code.  There’s only inpatient stay.  It may have many bed section stays.  There’s some aspects about this that you really need to keep in mind if you’re going to use this particular variable.  If you’re looking for it in the main file, it won’t be there.  It’s only in the bed section and the procedure datasets.  It can give you a lot more detail if you’re interested in that.

DXLSF is the primary diagnosis for admission.  This is in the inpatient Medical SAS datasets.  If you’re interested in assessing diagnosis at admission, that’s a good diagnosis to use.  If you’re interested in the condition that is determined to be chiefly responsible for the admission to hospital, that’s DXPRIME.  You should really take a look at this.  Often DXPRIME is the preferred one.  The codes are assigned by the professional coders.  This is the one that leads to the calculation of the diagnosis‐related group that’s consistent with what’s used in the HERC datasets, the DRG for calculating costs.  It’s consistent with what’s used for Medicare datasets, for DRGs.  There are obviously some reasons that DXLSF might be chosen for your particular research study.  Just know that there’s a distinction between these two, and you should be very careful about which one you choose, depending upon what your research question is.

Also, other information about diagnosis.  There are DXF2 through 13.  Secondary diagnoses.  These are ICD‐9 diagnosis codes for the full hospital stay.  These are only in the main dataset.  The number of secondary diagnosis codes change from 9 up until 12 in 2005.  If you’re looking at longitudinal data—2002 through 2007, for example—the earlier datasets are only going to have up to nine coded, where the more recent datasets will have 12.  That’s again assuming that the full range of secondary diagnoses are used.  They can go up that high, but they often do not.  DXLSB is a diagnosis related to a bed section stay.  This is only in the bed section dataset.  They can have up to five.

Finding information in the inpatient Medical SAS datasets to assess procedures.  Again, a type of inpatient or outpatient event but in this case, inpatient.  The procedure datasets contain procedures that are not performed in an operating room.  Things like a dialysis type.  A number of dialysis treatments is included there.  On the other hand, the surgery datasets contain information on procedures that are performed in an operating room.  The thing you need to keep in mind is if you’re really interested in surgeries or you’re really interested in procedures for your research project, I would strongly recommend that you tap both of these datasets because a procedure in one facility may not be considered “surgery” in another facility.  You should just check both.

The inpatient MedSAS datasets use ICD‐9 procedure codes.  Next year, when we’re talking about ICD‐10, we’ll have another conversation about how to match ICD‐9 and ICD‐10 procedure codes.  Like I said, that’s another lecture for another time.  These are things that you should be looking towards, especially if you’re doing research projects that go across years, which I’m imagining many of you are.

Finding information on length of stay.  Something to keep in mind.  Records are created at discharge for the full stay, even if the admission was in a prior year.  Exception to this is claims for non‐VA care included in a dataset for a year paid but not for a year of care.  The inpatient dataset includes length of stay as defined here.  The minimum value is one.  If you are finding—if you’re surprised that the most common metric that you’re seeing is one, that’s because of the way it’s calculated, even if it’s a partial stay.

Finding information about diagnoses.  Let’s talk about outpatient datasets now.  The outpatient event datasets go from—this is the SE—goes from 1997 to present.  It includes the DXLSF.  Sorry.  Also the IE file.  That’s 2005 to present.  We sometimes refer to it as the consult dataset.  It includes DXLSF, the primary diagnosis.  There’s no DXPRIME here because DXPRIME is an inpatient kind of variable.  It also includes DXF2 and DXF10, secondary diagnoses.  These are some of the variables, the values, from our inquiry for the 2012 SE file.  First 3,000,000 records we queried, the most common DXLSF was PTSD.  I guess not surprising, given our population and the needs we know they have.

Outpatient Medical SAS datasets regarding use of procedures.  Again, the SE file and the IE file.  It includes CPT codes.  Okay?  CPT1 through 20.  The number of CPT codes got bumped up, so they can actually enter up to 20—not necessarily meaning they use all those fields—in 2005, and they also use CPT4 codes.  Again, our query of the FY12 SE file showed that 8 percent of the CPT codes in the first 3,000,000 records that we queried were for telephone assessment by a nonphysician.  You can see what some of the other ones were.  Three percent were for complete blood count.  That’s a laboratory visit, if you will.

Again, this just gives you a flavor of the type of data that are there.  Provider types.  This is in the outpatient SE, outpatient event, and the inpatient encounter dataset.  There’s a variable that is for providers one through ten, and this gives you an example of the types of providers that showed up in the first 3,000,000 records in the query that we did on the FY12 dataset.  Eleven percent were for internal medicine, whereas only 4 percent were for an LPN, licensed practical nurse.

Let me stop there and see if we have any questions about finding information in the Medical SAS datasets.  We might have time for one question, Arika. 

*Moderator:*We have a number of questions.  Is there a key for primary and secondary stop codes?

*Dr. Hynes*A key?  The way that question is phrased concerns me that you might be thinking of a SQL dataset, which uses primary keys.  In the Medical SAS datasets, there is—I don’t recall which slide it was, but I referred to the naming convention that used the CLCLC for the primary and secondary stop codes.  That was in one of our slides I went over.  I can refer you to that separately if needed.  If you’re referring to the SQL datasets, that would be another conversation that we need to have.

*Moderator:*Are there central code books that tell us what is available in MedSAS datasets?

*Dr. Hynes*I think the best source for a central code book—there are the Research User Guides, the RUGs, that I mentioned.  Those probably provide the best information, especially for researchers or for those who are non-researchers who want to do a lot of analytics with the data.  There are technical guides that the data steward, National Data Systems, has put together, but I think they would even refer you to our Research User Guides.  They’ve looked at them.  They’ve gone over them.  Those provide a lot more detail than what you’re going to find in a technical code book.  User information, usability information, some information about data quality and where you can go for other information.  I would strongly recommend that you take a look at the RUGs, the Research User Guides, that are on both the VIReC website, and you can find them on the VHA data portal as well.  We’ll bring up a URL at the end that’ll show you that too.

*Moderator:*Great.

*Dr. Hynes*Let’s take some more questions at the end.  I’ll go through these examples pretty quickly, and then maybe some of the questions about finding information we can come back to.

This is a paper that was published by Eric Mortensen and colleagues in the *American Journal of Medicine* in 2010.  I thought we would go through just two brief examples to just show you how different research projects have used the Medical SAS datasets in their research.

Probably the most important theme you’re going to see here is it’s rare to use these datasets alone.  You usually require some additional dataset to identify other aspects of care or other aspects that you might be studying.  We’ll just highlight how the Medical SAS datasets are being used.

This particular manuscript talks about assessing the frequency of diagnosis for pulmonary malignancy after hospitalization for pneumonia.  They’re looking at specific events after a hospitalization with a particular diagnosis code.  It was a retrospective cohort conducted 2002 to 2007.  They looked at patients who were 65 years and older.  They used the inpatient and outpatient MedSAS datasets, among others.  They looked at exclusion criteria at the date of admission.  Again, in the Medical SAS datasets, contrasted that against the patient’s age.  They looked at patients as having one outpatient clinic visit in the year preceding the index admission.  They’re using both the outpatient dataset and the inpatient dataset together.  They also looked at some other aspects.  Again, pharmacy data.  Outpatient medication within 90 days of admission.  Again, they had to use another dataset in order to just define their cohort.  They looked at hospitalizations during this period, fiscal year ’02 to ’07.  They also looked at discharge diagnosis in the Medical SAS datasets.

These datasets are pretty rich.  For those of you who’ve worked with me, you’ll know that I really don’t like referring to these datasets anymore as administrative datasets because they really contain a wealth of clinical information.  If you ever send me a paper to review, that will be a signature of me indicating that.

This is just an excerpt from one of the tables from our colleagues who published this paper.  They looked at post-hospitalization with pulmonary malignancy, and they also looked at their cohort for those who had no pulmonary malignancy and compared the two using variables that indicate whether the patients were—going to try and use my little arrows here—hospitalization ICU, whether the hospitalization included mechanical ventilation, and then different degrees of mortality, 30‐day, 90‐day, and length of stay.  You can see that they really had to rely on the Medical SAS datasets pretty extensively for construction of these variables.

Second research example.  Again, I just want to highlight how our colleagues are using these data.  This is one I introduced at the beginning.  Steve Luther and colleagues looking at breast‐conserving surgery for women treated in the VA.  They sought to—and this was just published in 2013 in the *AmArikan Journal of Surgery*.  They sought to look at use of breast‐conserving surgery, and they did some adjustments for stage of disease, which meant that they also had to use the VA’s Cancer Registry.  Again, a dataset separate from the MedSAS datasets.  You will not find cancer stage in the MedSAS datasets.  Again, just emphasizing the point that often you need to use additional datasets in our line of work.

It was a retrospective cohort of women veterans diagnosed or receiving their initial treatment for breast cancer.  They probably had to go through some of those issues that I mentioned earlier about trying to exclude nonveterans from their study as well.  They used inpatient and outpatient MedSAS datasets.  Here you can see that they were able to use these datasets and profile those patients from 2000 through 2006 who used breast‐conserving surgery and mastectomy in the VA.  I won’t go into the details of how they defined breast‐conserving surgery and mastectomy.  The important aspect is that they relied on the inpatient and outpatient datasets to construct these categories with the surgical data, the procedure data, and the diagnosis code data that they had available to them.

I’m going to just go to where to go for more help.  Then we can take questions.  The VIReC website is one I mentioned.  Research user guides can be found there, and it includes details on variable level information in those datasets.  I should add, for those of you who are concerned that some of the examples I showed show 2011 or 2009, you should know that we also publish a particular product that we call Historical Variables, and it shows the type of variables that have changed from year to year.  Even though we don’t publish a RUG, a full‐blown detailed variable description every year, we publish information every year about changes in the datasets for the variables.

What you’ll notice is there’s really very few changes.  Sometimes they expand or contract a particular variable.  As I mentioned in our lecture today, for example, the number of stop codes that are introduced or allowed or the number of fields that are allowed for diagnoses.  For the most part, the major data elements have been pretty consistent for quite a period of time.

We also have technical reports that are useful.  For those of you who are new—and I know we have many today—we do have a link on our website called Toolkit for New Users of VA Data.  In particular, these toolkits emphasize use of the MedSAS datasets.  We’re still working on the toolkit for the Corporate Data Warehouse, so you won’t find that, but you will find some information about that.  I would also recommend subscribing to our monthly Data Issue Briefs or looking at those on the Web.  Then there’s always our Help Desk.  If you can’t find something on our website, we would strongly encourage you to contact us by e‐mail.  That’s usually best.  We also have a phone number here too.

There’s also the HSRA data LISTSERV.  This is on VIReC’s intranet website.  This is behind the VA firewall.  We’ve learned over the years that it’s just easier to keep it behind the VA firewall.  Then you don’t have to remember what you can say and what you can’t say, although we do have some just general rules of behavior, if you will.

We do want this to be exchanging current information, ideas, questions, and answers about VA data, in particular, emphasis affecting VA research.  We do have, if you will, subscribers to the listserv that is way beyond researchers.  Data stewards, managers, other users.  Every once in a while, we get some folks who are a little less connected to the VA, but obviously, in order to be on this, you have to have some connection with the VA , a WOC, an IPA and a VA e‐mail.  Searchable archives of past discussions are available on the intranet site.  Now let’s go to any lingering questions, Arika.

*Moderator:*Okay.  Great.  Are MedSAS datasets linkable to prescription datasets?

*Dr. Hynes*Indeed they are.  There are prescription datasets.  Both the PBM datasets and the DSS pharmacy datasets, which we’ll be talking about, I think, in late winter.  They also use the same unique identifier, the scrambled SSN, so you are able to link those.

*Moderator:*Can you clarify the type of patients in the MedSAS outpatient datasets inpatient encounters?

*Dr. Hynes*Okay.  The inpatient encounters dataset—generally speaking, they should all be people who have concomitant inpatient event.  If you were to look at the IE file and compare it to the MedSAS inpatient file, your patient should show up in one of the inpatient MedSAS files.  Generally speaking, that’s the case.  They’re supposed to be people who are having—they currently have an admission, and they’re having some kind of outpatient event.  It’s counterintuitive.  It’s recorded in what’s called the inpatient encounter dataset or inpatient—they’re really an inpatient when it’s occurring.

*Moderator:*Great.  On slide number 12, how are the five procedures or surgeries picked to appear in the procedure or surgery inpatient datasets?

*Dr. Hynes*That’s a good question.  I don’t have an answer for that.  The diagnosis codes—the only one that you can count on as a primary diagnosis, if you will, is that DXLSF and the DXPRIME.  Any other diagnosis codes that are listed are not in any particular order.  DXF2 or DXF10, depending upon which dataset, they are not in any kind of hierarchical order.  That said, should you treat them equally?  That’s how I do it in my research because there’s no weighting.  W E I G H T I N G.  They’re not weighted in any way.  It’s not going to change necessarily by the end of the year, even.  If there’s a code there in the secondary diagnosis codes, F2 is probably as important as the later ones.  What you’re going to find is there’s probably very few fields entered when you get to the later diagnosis fields.

*Moderator:*Great.  Where is home‐based primary care data located?

*Dr. Hynes*Oh, boy.  I’m going to answer that one offline so I don’t get it confused.  It’s not in the inpatient MedSAS.  There might be a clinic stop code in the outpatient datasets, but I’d have to double check.  We do have non‐VA care datasets and some other datasets.  Let me get back to you on that specific question.  We can send it out to this group.

*Moderator:*Okay.  Sounds good.  If there are only ten ICD‐9 codes linked to an event and a patient has more than ten, which ones are chosen?

*Dr. Hynes*I don’t have an answer for that.  Again, it’s just going to be the first ones listed.  Again, just because they’re listed first doesn’t mean necessarily that they’re more important.  The coders are probably going through the datasets—I’m sorry.  The electronic medical record—and looking at which ones to list.  Since the secondary diagnoses do not account for anything related to DRGs, you really can’t put a whole lot more weight on them, whether they’re in the F2field or the F10field.  The same thing for procedures as well.

*Moderator:*Great.  Let’s take one last question to make sure attendees had time to fill out the questionnaire.  What are the options for linking VA with non‐VA data to create a more comprehensive picture of service use?

*Dr. Hynes*We definitely have some procedures in place.  Topic of another lecture, I believe, in January, for linking VA data with Medicare and Medicaid data and USRDS data.  United States Renal Data System data.

If you wanted to link VA data with, say, your university hospital data or your public hospital data, that would require a lot more creativity.  You really are at the mercy of interinstitutional agreements.  The only way that you could really link the data is if you had some kind of other identifier that was common, such as a Social Security number.  Even nowadays, some other non‐VA institutions forego the Social Security number and use medical record numbers.  Medical record number, for example, at my university hospital would be meaningless if I wanted to link it with VA data because it’s not derived from anything that the VA uses.

If you’re doing a prospective study and you’ve consented patients and you can get that kind of detail from them, their medical record number, their Social Security number, and permission to do that linkage, will, then you’re in a different situation.  If it’s a non-consented project, it’s definitely a bigger challenge.

*Moderator:*Great.  Thank you, Dr. Hines, for presenting today’s lecture.

*Dr. Hynes*You’re welcome.

*Moderator:*If there are any additional questions that have not been answered, please contact the VIReC Help Desk at virec@va.gov.  Our next session is scheduled for Monday, January 6, from 1 to 2 p.m. Eastern and is entitled “Measuring Veterans’ Health Service Use in VA and Medicare” and will be presented by Kristin de Groot.  We hope that you can join us.

*Moderator 2:*If you want to click on it quick there, I have a Register Here link.  Feel free to click on that.  It will open the registration page in a new page.  I want to thank everyone for joining us for today’s HSRD Cyber Seminar.  We hope to see you at a future session.  As I close the meeting down here, you will be prompted with a feedback form.  We hope that you take a moment to fill that out.  Thank you, everyone, for joining us.

*[End of Audio]*

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