Measuring Veterans Health Services- 11 -Department of Veterans Affairs

Denise Hynes

**Department of Veterans Affairs**

**Measuring Veterans Health Services Use in VA and Medicare (Part 2)**

**VIReC  Database and Methods Cyberseminar**

**Denise M. Hynes, PhD, MPH, RN**

Margaret:  Welcome to VIReC’s Database and Methods cyber seminar titled Measuring Veterans Health Service Use in VA and Medicare part 2.  Thank you to CIDER for providing technical and promotional support for this series.  Today’s speaker is Denise Hynes, director of VIReC and research career sciences at the HSR&D Center of Excellence here at Hynes VA hospital.  Dr. Hynes holds a joint position at the University of Illinois, Chicago as Professor of Public Health and as Director of the Biomedical Informatix core of the University’s Center for Clinical and Translational Sciences.

Questions will be monitored during the talk and the Q&A portion of Go-to-Webinar and I will present them to Dr. Hynes after each section of her talk.  A brief evaluation questionnaire will pop up when you close Webinar.  We would appreciate if you take a few moments to complete it.

I’m pleased to welcome today’s speaker, Dr.  Denise Hynes.  Denise are you there?

[Organizational discussion]

Denise M. Hynes:  Great.  Thank you.  Well, hopefully that little bit of time gave everybody an opportunity to go get slides if you really wanted to download them.  If not, you can do that later.  So thanks for your patience.  What we’re going to do is, Margaret, can you get to the objectives for today.   Good.  So we’re going to do a little bit of an overview from our last lecture, but before we get into sort of the main content, we just wanted to see some answers to these questions.

One is whether you were in our first session which was held in December on using Medicare claims.  And if you can indicate yes or no.  And then oh—great—

Moderator:  Each poll will be separate.  So—

Denise M. Hynes:  Okay.

Moderator:  It will take me just a second to get through each one.

Denise M. Hynes:  Okay.  Very good.

Moderator:  If responses are coming in, I’ll just wait for them to slow down and then I will show the results on the screen here.

Denise M. Hynes:  So hopefully you each can plug one of these answers in.  It will give us a better idea of how much time to spend on the first set of slides.  And whether to encourage you to take a look at those if you didn’t attend.

It looks like the majority of folks did not attend the December session.  This is important to know.  I’m glad that we included the overview.  We also have a second question that asks, have you ever used any Medicare claims data other than the outpatient and inpatient standard analytical files in your research?

Specifically the files that we’re going to talk about today are the home health agency files, hospice services, skilled nursing facility, durable medical equipment.  So particularly interested in those of you who might have some experience working with these claims data.

Exactly the same stats as the first question, 77% have not.  All right.  So we have a relatively new group of people and our last question.  This will be the test of internal validity here.  How would you rate your overall knowledge of Medicare claims data with one being no knowledge and five being expert level knowledge.  If you would plug in an answer there.  We’ll wait just a moment for the tally to show.  Okay.  So the results are showing that a third of you have no knowledge or no experience with Medicare claims data.  Forty four percent are in the number two category, sixteen percent in the middle, seven percent at a number four and no one would rank themselves as experts.  Hopefully our lecture will satisfy everybody.  You don’t have to know how to use claims data.  This is definitely a beginner type of a lecture and really to just introduce you to the concept of Medicare data, how they can be useful in your research and some strategies for evaluating Medicare/healthcare use and since we have a couple of examples of VA studies using some of the Medicare claims data, specifically the ones highlighted here, and then we’ll also highlight where to go for more help.

We’re going to begin, first with some overview of Medicare claims data.  Now some of this we covered in the December lecture and certainly in much more detail than you’re going to see here.  But it should introduce you to some main concepts and I just want to make sure that we have a basic understanding of as we go through the concepts for today.

Next slide, please.  So just to emphasize why Medicare claims are important, many veterans who use VA healthcare also obtain care outside VA—and in particular those eligible for Medicare are receiving potentially care from VA both inpatient and outpatient and other healthcare as well as potentially outside the VA under their Medicare coverage.  Researchers need to get a sense of their health services use; you might need to capture a larger picture beyond just VA healthcare in order to draw accurate conclusions.  In fact, there’s estimates that more than half of veterans enrolled in VHA are also enrolled in Medicare.  Some estimates are even higher than that.

Next slide, please.  Just to make sure we have an understanding of some basic terms that you’re going to hear us talk about throughout today’s lecture.  Healthcare providers and health equipment suppliers basically submit claims, that is bills, to Medicare—formal name listed here—for reimbursement for service and products.  So the data that constitutes those submitted claims is what we refer to as claims data.  These are collected by CMS and entered into data sets for analysis based on the type of the billing form or the type of claims used to gather the original information and the type of provider.  This is really important to think about.  In the VA you might be accustomed to a lot of the clinical data—a lot of the--what we often refer to as administrative data or discharge, outpatient data.  Keep in mind that the Medicare data are really billing data.  It’s not typically what we are working with at the VA.  Although, there are some similarities.

Next slide, please.  It’s important to understand these divisions of the type of billing form and the type of provider when you’re considering working with Medicare claims data because the way Medicare organizes their data products, if you will it’s organized by these two dimensions and it’s not always intuitive where you might find the data—and where you request data it’s important to understand these particular aspects.  So type of billing information forms.  There’s an institutional form, and there’s something called a non-institutional form.  Institutional forms generally refer to healthcare facilities, so for examples hospitals, skilled nursing facilities, SNF’s, home health agencies, hospice care agency and non institutional providers would be physicians, nurse practitioners might be considered providers, social workers, suppliers, such as suppliers of oxygen equipment or suppliers of services so it could be either equipment or services.  This is important to just understand because the claims could show up on an institutional claim form—what’s referred to as a particular type of billing form that someone in a hospital would complete.  Or it could be something that is recorded on a non-institutional—what’ called a CMS1500 from there.  So you could imagine that there are some services that are provided in hospitals that are delivered by physicians.  There could be claims that occur in both the institutional category, reported on an institutional form, and there could also be information that’s recorded on a non-institutional form by a specific physician or a provider.  That does not mean that they’re duplicative.  They could be very complementary.  So if you’re looking at services provided by physicians for services delivered in hospitals, you might need to request both information that is collected from the CMS1450/UB-04 as well as information that’s collected on the CMS1500.  This will become more important as we talk about the details of the data sets and data products themselves.

Next slide, please.  So institutional files.  Here are some examples.  In our last lecture we talked about the outpatient and inpatient files.  Today we’re going to talk about Home Health Agency, Hospice and Skilled Nursing Facilities.  These are institutional files.  Non-institutional files.  Last time we talked about the Carrier file, also referred to sometime as the physician/supplier  file.  Today we’re going to talk about durable medical equipment.  This is considered non-institutional data.

And another file we’re going to talk a little bit about today which kind of is a hybrid, if you will, we’ll talk about it specifically.  It’s called an institutional stay level file.  It kind of combines elements from both of these, so it’s a little bit of a unique aspect.  And we’ll talk a little bit about why that type of a file might be useful, today.

Next slide, please.  So let’s just review a little bit about the relationship between a claim and services or healthcare.  You need to keep in mind when using claims data there’s not a simple one to one correspondence between a claim for services or a bill and an episode.  A single claim might include one service, one product or one procedure, such as a physician office visit.  But it could also refer—it might also be more than one service, product or procedure, such as an inpatient hospital stay that goes over time.  It really depends on what specific data set you’re looking at but also the type of service that you’re looking at.  There are some commonalities about how services might be recorded as a claim.  Becoming familiar with the type of episode or the type of care that you’re interested in will require some basic getting up to speed on how these services are recorded in claims data.  Sometimes multiple claims might be submitted for long inpatient stays, or for procedures that might involve multiple physicians or providers.

Next slide, please.  The benefits of Medicare claims data that we try to highlight, in particular, when studying veterans, we have set things up in the VA so that a researcher can link Medicare data with VA data using specific types of patient identifiers.  This was established through agreement between the VA and senate for Medicare and Medicaid services to enable VIReC basically to provide this service to researchers.  These data also directly related to billing are likely to be accurate for specific aspects that are related to billing.  For example, data that record the claim from and thru dates, charge and payment amounts.  Coding is particularly dependant.  Billing is dependent on diagnosis and procedure codes and provider numbers.  These fields are particularly found to be accurate in claims data.  And can be useful when using these type of data when you’re trying to track these specific aspects.

Next slide, please.  From simple examples of limitations of the Medicare claims data and I’m sure you can think of others are that these data, because they are billing data and that is the primary reason they are constructed, the data that also are included in these data sets that are not necessarily related to billing or reimbursement may be less accurate.  For example, there are demographic data along with the claims data, but the specific age of a recipient or their education or income level is not necessarily a factor that affects billing or reimbursement.  So it might not be as accurate as other places.  There may be high levels of missing.  Clinical data may also not be even available in some cases in claims data.  There are some limited lab results that are tied specifically to billing, specifically some hemoglobin levels are tied to titration of specific medication and certain other lab values that might be related to reimbursement, but generally speaking, lab results, vital signs, symptoms are not usually part of claims data.  Another aspect is that services that are not itemized simply because of the type of health plan, a Medicare beneficiary might be part of—specifically if they’re part of the Medicare advantage or some sort of managed care program, those claims are not itemized.  So therefore, you might not see all of the details about experiences patients have if they’re part of a managed care program.

Some of the detail also might not be apparent if some of the information or the claim is part of a bundled or a prospective payment system such as in patient events.  It’s billed on a per admission/discharge basis.  Therefore you might not see a lot of detail about the specific admission, about procedures that occur during the admission that have no bearing on the ultimate reimbursement for the inpatient stay.  That’s not to say there is not procedure data listed, but it may not be as accurate or as complete if it were dependant—if it affected the billing.

Next slide, please.  Another term to just make sure we’re all understanding the same thing, this is just a basic explanation of the prospective payment system which can also—you’ve probably heard this term with some bundling, but historically, CMS has done some prospective reimbursement for certain kind of services either for hospital stays or for certain episodes of care.  Especially for inpatient stays and for certain kinds of care there are some special categories of care that are governed under prospective payments.  You need to become familiar with specific aspects that you’re studying.  If you’re studying inpatient care; if you’re studying certain chronic diseases—if prospective payment is an aspect to what you’re studying, it’s an area that you need to make sure that you’re aware of and how it might affect how care might be reimbursed. It’s particularly important if your outcome variables are cost or health cervices use, because it might affect how that kind of detail is itemized in claims data.

Next slide, please.  Some basic aspects about data access.  First of all, what we’re going to highlight here today is the availability of CMS Medicare claims data through the VA.  let me just put a caveat on this whole thing and that is that researchers across the country can request Medicare claims data directly from CMS through contractor they have known as the Research Data Assistance Center based at the University of Minnesota.  Generally speaking, all researchers can go there.  In the VA; however, we set up a special program, if you will, to enable VA researchers to utilize the data through the mechanisms that are described here on this slide.  To be eligible to request data through the VA, basically through VIReC—the VA Information Resource Center that is the steward for these data for research is that you must have the project is a VA—that you are a VA researcher.  Your project has VA research and development committee and IRB approval, and that makes you eligible to request the data through VIReC.  VIReC manages all use of the CMS data for VA research.  In the VA we also have some collaborations with the assistant deputy undersecretary of health Medicare Analysis Center to coordinate how these data are acquired from CMS and also coordinate any use with operations side.  These data are available in the VA now for both research purposes and operations purposes.  And for those of you who might be outside of research and have an interest in using these data for operations purposes please don’t hesitate to contact VIReC.  We work very closely with MAC and will have some of those health slides at the very end.  If you’re not sure if your project is under VA auspices, we can also help with sorting that out.  More information is available on VIReC’s website and the urls are listed here.  In particular we list the specific data that are currently available.  We try to keep that up to date and the request process and the information that you can—that are required for you to provide in order to utilize these data.

Next slide, please.  I’m going to pause here and see if there are any questions.  I know you have slide control and checking out the questions.  So—I’ll let you tell me what we have.

Margaret:  There’s one question here, Denise.  What is the difference in the Medicare claims between charge and payment?

Denise M. Hynes:  Okay.  Thank you.  Charge and payment are both terms that are—you’re going to see on any particular Medicare claim, there’s going to be information about several revenue fields, if you will.  There’s going to be charge fields, there’s going to be payment fields.  There’s also going to be specific breakdowns of components to which it was assigned.  And both of those pieces of information are there.  Which piece of information is best for you to use in your research project really depends on your research question.  Any other questions, Margaret?

Margaret:  Sorry, Denise—I muted myself.  No.  There are not.  Next slide.

Denise M. Hynes:  So we’ll go on to some more details about the type of data from the select Medicare files.  These are the ones, just as a reminder that we’ll highlight today.  Now keep in mind—you can go to the next slide Margaret—

Keep in mind we will just give you you know let’s face it—two slide introduction on these concepts.  I would encourage you, especially since we have a lot of new people and folks who are inexperienced in using claims data, if you’re really considering using claims data in your research project, I would strongly encourage you to take advantage of some of the in depth educational opportunities that are offered by the Research Data Assistance Center and others to get both an in depth overview and they have educational sessions that last days.  Some hands on experience which can also last multiple days and different sessions.  There’s a whole range of classes that can be more introductory to actual hands on.  Hands on with specific types of data sets and information systems that Medicare and Medicaid data are now comprised.  Again, this will just be introductory to give you a flavor of the type of data that are inside these types of data sets that are very useful data research tool.  So beginning with the home health agency, standard analytic file—sorry—SAF.  You’re going to see here a lot.  It’s a standard analytical file.  And it’s just a term that refers to a standard template that you see in the Medicare claims data in terms of how it’s organized and some of the naming conventions for the variables.  So for example, you’ll see payment and charges show up on a lot of the data sets and it’s defined in similar ways.  And some of the fields are similar, although specific to the type of institutional or non-institutional file that it represents.

Home Health Care services are an important part of Medicare coverage.  It can be provided across a range of services, provided in the home.  It includes skilled nursing, physical, occupational and speech therapy and home health as well as medical supplies.  It’s important to understand how this care is captured on claims data in order to know what’s available to researchers on the data sets that Medicare builds and based on these claims.  Each type of care is billed up to sixty days of care on one claim.  So you can imagine that it’s not just one date at a time, its multiple dates over the course of a particular episode.  Here it can be up to sixty days on one particular claim.

Next slide, please.  In the Home Health Agency Standard Analytic File.  Each record is a claim.  An episode of care may require many claims.  In these data sets, the from and the thru dates are particularly important but they don’t necessarily indicate the dates of service  It might represent that sixty day period, of billing, but it might not represent the days of service.  You really need to pay attention to variable names in these data sets.  If the date refers to a date of service, if the date refers to a date of billing, you need to pay attention to those different categories of dates.  There are some details of the types of care provided and they’re available on what’s called revenue center variables.  There are a range of variables in these data sets that you need to do some lookups for to understand what the coding represents.  Just like with CPT codes, ICB-9 codes, diagnosis codes, there are also revenue center codes, as well.

Next slide, please.  In considering how you might use these home health agency data in terms of measuring healthcare use, you’re going to see some repetition in some of our slides.  You might use these claims data to represent healthcare use.  You might have to be mindful of how a claim is represented and how you count use.  You might count specific claims, but you must keep in mind a specific claim might represent different days of service.  You’ll have to take that into account.  It might be useful to look at length of treatment for home health agency care.  You might be able to look at number of treatments.  It also includes information on diagnosis so you might be able to look at the healthcare use in the particular diagnosis.  And again, it has information on both charges and payments in each claim.

Next slide, please. We’re going to move on to the Hospice Standard Analytic File.  It’s a little bit different than home health agency care.  It’s specific to hospice care.  Even though hospice care can also be provided in the home, it includes services provided when physician has certified life expectancy of six months or less.  It’s generally care in the home.  80-90%.  It also can be care provided as an inpatient.  So care that is listed in a hospice SAF file would be different than care that’s provided, listed in a claim for home health agency and different than care that would be listed in institutional provider.  You do need to look for specific aspects about the claim.  The care is billed on a single daily rate for each day a beneficiary is enrolled in hospice care.  Regardless of the amount or type of services furnished.  Hospice care is specifically looking at a daily component and depending on the types of Medicare covered care, just like we’re describing here, it can be billed differently.  What you’re seeing here is that hospice care is showing up in claims in a very different ways than what we described for home health agency care.  These are aspects that you just need to be aware of aw you’re embarking on using these different types of files.  It’s important in considering how you might be combining them, for example.  If you’re looking at episodes of care across the continuum, you need to be very mindful how each of these data sets are constructed and how Medicare treats each record.

Next slide, please.  In the Hospice file, billing is reflected at the claim-level data.  An episode of care might require coining many claims.  And most hospice claims are for less than 30 days of care.  Hospice is considered one of the bundled programs, so that reimbursement might be not necessarily on a per day of service even though some of the bills might list days of service.  Again, so you need to be aware of how Medicare actually covers services in this particular program, which may be reflected in the payment and charge fields and not necessarily restrict the amount of records or claims that are listed in the file.  

Next slide, please.  Measuring healthcare use in hospice data.  Again, similar aspects even though the benefits of hospice is very different than the benefit of home healthcare agency care under Medicare.  You can still use the same records, the claims to account for use.  You might have to do it in a little different way because you might have to take into account the days of hospice care.  Looking at the length of hospice use is certainly possible with this data.  The date fields are there to take this into account.  There are also diagnosis fields and similarly charge and payment fields.

Next slide, please.  Skilled Nursing Facility File.  This is an institutional file.  This includes services provided by a skilled nursing facility.  It can include inpatient care as well as rehabilitation care.  It is important to understand what Medicare covers as skilled nursing facility.  Specifically custodial care provides assistance in activities of daily living and is typically needed for a very long period of time.  If not the rest of a person’s life and this is not included in a skilled nursing facility standard analytical file claim.  The inpatient and rehab care that Medicare reimburses is generally only available for a short time following hospitalization.  It’s changed from the benefits that Medicare covers have changed over time.  Generally speaking it can be up to 100 days and it’s usually on a predetermined daily rate for each day of care.  Depending upon how far back in time you might be looking back, data from the skilled nursing facility whether it’s 100 days or 90 days could have some bearing depending upon the time frame that you’re looking at.

An important thing to keep in mind though is it is a very constrained benefit based on time.  And in fact, a consideration to think about with all of these particular data sets, i.e., programs within Medicare, how the claims are structured are also very tied to how Medicare coverage is structured as well. 

Next slide, please.  Billing in a skilled nursing facility data is reflected when a SNF stay—a Skilled Nursing Facility stay from admission to discharge requires submission of multiple claims, a researcher much combine the claim to measure healthcare use or cost for a single stay.  So what that means is that you really need to carefully think about how data are reflected in these institutional data and if you’re trying to count days of stay you need to be careful how you combine these various SNF stays.  Also the frequency of claim submission might be based on facility accounting or duration of stay.  If you’re going to work with a Skilled Nursing Facility data, you really need to look at a range of data.  Become familiar with it; again, guided by your research question, and make sure that if you’re looking at healthcare use or healthcare charges that you’re understanding the best way to combine records when you’re trying to summarize the information.

Next slide, please.  Same kinds of aspects that you can use these data for as was described with the previous files.  Again, what I would emphasize is the caveat that are specific to the benefits that Medicare provides and how these data sets are structured when considering combining it, looking at episodes of care and counting any kinds of claims and stays and being mindful of the different ways that event services, claims, can be compiled in these claims databases.

Next slide, please.  Durable medical equipment includes specifically common items that would come under the category of equipment, prosthetics, orthotics, supplies and services are generally found in the carrier,  the supplier file, but the equipment themselves are found under DME.  Oxygen and supplies that might be provided to the home; that would be different than some sort of therapy for or treatment.  Sometimes oxygen and supplies can also show up and some of these services can also show up in a carrier file as well.  Wheelchairs, hospital beds, enteral and parenteral nutrition, IV therapy, drugs administered through DME.  This is an area where if it sounds like there’s a service that might be administered concomitant with the particular equipment, oftentimes—if you want to get the whole picture, you might also need to look at the carrier file as well.  Generally speaking DME is limited to equipment and these types of supplies.

Next slide, please.  How care is billed in the DME files.  The reimbursement is based on another set of codes referred to as HCPCS codes.  We often pronounce them as Hic-pic codes.  And I’m going to forget exactly.  Healthcare Common Procedure Coding System.  It’s sort of like diagnosis codes.  This is another one that you have to become familiar with as you’re working through these data sets, knowing exactly which HCPCS codes you’re most interested in for your particular research project and it’s a particular standard by which reimbursement and pricing is based upon.

Actual fields in this data set could also include rental—some of this equipment might be rented by individuals.  So there are both purchase price, rental price and a claim might contain one or multiple products.  So again, you have to be careful and understand each claim in the dataset and how it might pertain to your particular research question.

I’ll be presenting in one of our examples today how one study used the DME file particularly for looking at medical supplies.

Next slide, please.  In the DME file, we have a little bit of a diversion here because some of the information here also includes rental.  Otherwise the information in how these data might be used in a research project is pretty similar to what we described already.  You can look at number of claims.  You might have to parse the claims, if it’s a—

[Beep]

Margaret:  Okay.  We just finished with durable medical equipment.  Denise, I think here’s the MedPAR—I think I’ll hold all questions and we’ll let the audience know that we’ll collect them and respond to them and Heidi will send out answers to the questions.

Denise M. Hynes:  Good.  Thank you.  Okay.  Sorry for our technical problems today.  I’m going to just highlight some aspects of this so that you can become familiar with these data specifically the Medicare MedPAR file is important because it actually might be an efficient way for you to work with data that is information that doesn’t require you to combine too many data sets.  The MedPAR file is a data set that basically claims are rolled up to the stay level and it contains information on both inpatient hospitals and skilled nursing facilities.  It’s an alternative to using both the inpatient standard analytic file and the skilled nursing facility standard analytic file.  We talked about the inpatient SAF in the first lecture and we talked about the SNF SAF in this lecture.  What you need to understand if you’re considering the MedPAR file that combines these too is that the claims are rolled up to the stay level.  If you wanted a lot more details then you’d be better off going back to the SAF files.  And but the information does contain information in both.  So it’s sort of a tradeoff.  Each record represents one stay or admission.  

Next slide.  Next slide, please.  I’m going to just let you take a look at this slide.  I won’t highlight a lot here.  I want to make sure we have some time for an example.  I mentioned a little bit about the details, if you’re interested in more details about SNF stays in particular, you’re probably better off working with the SAF file because it will provide more information about subcategories for total charges and more detail about diagnosis and procedure codes that would not be included in the MedPAR file.

Let’s jump to an example.  We’re going to jump to slide thirty, Margaret.  I’m probably going to only have time today to just give you some brief information about both of these projects that were culminated in peer reviewed publications.  The first one—the full citations are listed here.  We wanted to give you some examples of researchers actually working with these particular claims data sets and give you a sense of the kind of application they would be useful for in a research context.  So the first paper focuses on home healthcare and patterns of subsequent VA and Medicare healthcare use for veterans.  And the second paper focuses on specific assistive technology devices.  Both of these are pretty recent papers.  Let’s jump to the first one.  It looked at a pretty large cohort.  This is from Van Houtven and colleagues.  They published this in *The Gerontologist* in 2008.  And they basically were attempting to describe patterns of home healthcare use.  They had a cohort of over 24,000 VA home healthcare users and then more than 53,000 non-home healthcare users.

Next slide.  They looked at databases in the VA as listed here as well as the Medicare datasets.  Outpatient, inpatient, SNF and hospice files and they linked these datasets based on patient identifiers, so they can look at the complements of care between VA and Medicare for these patients.

Next slide.  We just brought a sample of some of the information presented in their manuscript and I would refer you to the manuscript for much more detail.  Basically what this table shows is the results of their statistical analysis which was a propensity score matched cohort and they found that they broke down the systems into Medicare and combined systems, VA or Medicare.  they looked at people who used VA or Medicare and they looked at folks who just used Medicare and if you just sort of focus on the line that lists nursing home in blue, we—you can see that they were able to compare those who used VA home healthcare with those who did not use VA home healthcare and how much care they actually ultimately used.  And you can see that those who were non-users in their original cohort actually used some nursing home care in Medicare and users of VA care also continued to use nursing home care under Medicare as well.  So basically this speaks to the importance of adding Medicare data to your research project in order to capture the full range of care.  And of course this can be done with any number of the data sets that we described.  

Next slide, please.  Just to highlight the second study—Winkler and colleagues.  This one was published in 2010.  They looked at use of assistive technology devices in both Medicare and VA.  They had a cohort of over 12,000 veterans post stroke that they had identified using the VA’s Functional Status and Outcome Database and VA Medical SAS datasets.

Next slide, please.  Again, they were able to link these datasets because of id’s that have been constructed to enable a linkage.  They had a two year study.  They looked retrospectively through their cohort and looked at the use of what they’re abbreviating ATD’s—assistive technology devices.  They use the HCPCS code to identify the data that the specific items in both the VA datasets and in the Medicare DME files.

Next slide, please.  This table is just an extract of some of the information presented in their manuscript.  What’s most interesting; at least for purposes of this discussion is the complementarities of the VA and the non-VA, the Medicare datasets in actually revealing that they had very little overlap.  Less than 1% of the patients in their cohort had duplicative assistive technology devices, which is actually quite phenomenal given the potential for overlaps since these are completely separate benefit programs.  They did not see a lot of overlap with the kinds of devices that were provided by Medicare or the devices that were provided by VA provides a broader variety of assistive technology devices, and at a lower cost—a lower price than what Medicare is providing.  That’s probably largely due to our pre-negotiated rates.

The last bit they suggest is that a lot of these aspects are certainly can be studied.  There’s price and charges.  There’s also counts.  You can see that they have a lot of demographic data that can be combined as well.  Especially when you use both the VA and the Medicare data together.

Next slide, please.  So we just want to close today’s session to make sure that you know where to go for more help.  I’m sorry that we lost about five minutes in our slide set today.  To highlight more about the important research projects.  I would refer you directly to the manuscripts for more details, which provide a lot more detail than we would have been able to highlight even if we had the additional five minutes.

I want to make sure you know where to get more help if you’re again—the 77% of you who are appear to be quite new at and are just considering using Medicare data, I would strongly encourage you to contact VIReC—in particular about Medicare data because we do offer technical consultations to help guide you with information about the type of data that you might be considering using.  It’s always best if you know your research questions and your outcome variables.  We can better advise if you are that far along in your project.  We don’t do well in advising you how to do a research project.  We would certainly be willing to help refer you to other places, but we’re better at helping you carry—advice on how to carry it out.  These are some of the sources.  We would also encourage you—we have an HSRData Listserv.  It includes these days over 400 members, data stewards, managers, users, researchers, non-researchers.  Oftentimes members will pose a question and several members will come up with a response.  That’s another way to get some help from colleagues as well. 

Next slide.  We have some specific information about VA-CMS data for research projects on our website including the forms that you need to use to request the data along with a lot of detail about the datasets themselves, even some frequencies about specific datasets so you can get a sense of what kind of data are in these datasets.  If you’re interested in a particular variable and you know—you’re down to that level of information and you’re curious about whether that particular field has some good data in it.  You’d know whether there’s a high level of missing or not.  So there’s a lot of detail on our website.  The URL is here.  I would refer you here.  Our website might change a bit in the next few months, but we should be able to preserve these links so that if you save it as a favorite you should be able to come back. 

Next slide, please.  I want to make sure and close with—I mentioned earlier in our lecture the Research Data Assistance Center.  They offer an incredible amount of educational opportunities, workshops and I would encourage you to visit their site.  We collaborate with ResDAC a lot.  We try not to repeat information that ResDAC publishes on our VIReC website.  We have a lot of links as well.  We try to focus on the particular aspect of linking Medicare data with VA data and often will refer you to the ResDAC and CMS data documentation about the CMS files as standalones.

Next slide.  And for those of you who are new to understanding CMS and Medicare and Medicaid, I would add, this is where some basic information, especially if you’re not sure about coverage aspects which as I alluded to in my slides is a critical aspect that you need to be aware of when considering using claims data in your research.  You really need to understand coverage aspects and how that has changed over the years and in particular these days, there are a lot of aspects that have to do with increased services that are now covered under prospective payment systems or bundling systems or accountable care organizations and these are all aspects that you need to be mindful of when using Medicare data.  I thank you for your attention and I apologize for the technical glitches and hope that you’ll provide us questions offline.

Margaret:  Thank you, Denise.  I want to announce to people our next session, scheduled for Monday, March 5.  Same time.  1-2 p.m. eastern time.  It is entitled measuring outpatient pharmacy use in the VA using VA pharmacy data.  Thank you all.  Thank you for bearing with the technical difficulties and we hope to see you in a month.  Bye bye.

Denise M. Hynes:  And I hope that you can also if Heidi is going to put up any kind of feedback on today’s session.  We’d really appreciate it.  Even if you’re going to give us some negative feedback about the technical difficulties, it helps us improve for next time.

Heidi: Yes.  The feedback form will pop up as each person leaves the session.  Everyone should see that.

Margaret:  Thank you everyone—

Denise M. Hynes:  Thank you, Heidi.

Heidi:  Thank you everyone for joining us today and we hope to see you at next month’s VIReC’s database seminar.  Thank you.

Denise M. Hynes:  Thank you.

Margaret:  Thanks.

**[End of Recording]**