**Transcript of Cyberseminar**

**HERC Cost Effectiveness Analysis Course**

**VA Costs: HERC versus DSS**

**Presenter: Jean Yoon, PhD; Todd Wagner, PhD**

**May 7, 2014**

*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**herc@va.gov.*

*Moderator:*We are at the top of the hour now, so I would like to introduce our speakers for today.  We have Dr. Todd Wagner.  He is a health economist at VA Palo Alto working with HERC, and we also have Jean Yoon, a health economist also at the Health Economics Resource Center, known as HERC.

Jean, I will turn it over to you now.

*Jean Yoon:*Thank you, Molly.  Todd and I will be talking about two of the main sources of cost data in the VA.  We'll be talking about the Decision Support System and HERC average costs.  The outline for our presentation today is that I will be talking about the DSS national data abstracts.  I'll talk about how DSS gets costs, I'll be talking about several different files, and then I'll talk about the advantages of using DSS data.

I'll then turn things over to Todd, who will discuss the HERC average cost system.  He'll talk about the methods for developing these HERC-created files, and he'll then describe several different cost files.  We'll also discuss reasons why you might use HERC or DSS data, and we'll then describe different data resources.

First, we wanted to understand how familiar the audience was with using DSS and average cost data, and we wanted to know for what different purposes you might choose to use DSS cost data versus average cost data for.  So if you could please select the ones that you think, for these different examples, why you might choose to use DSS over average cost data.  You can pick more than one answer.

For the first example, which for this case what you want to do is look at health care costs of patients who are receiving two different interventions.  You want to look at whether one intervention is associated with higher costs compared to another intervention.  These patients are being seen at multiple VA medical centers.

In the second example, you want to conduct a budget-impact analysis for patients who enrolled in a new primary-care program, and these patients are enrolled in one VA medical center.  Your goal is to estimate costs as accurately as possible in order to give that information to the medical center to help them with their budget planning.

The third example, you're trying to measure the prescription drug costs for patients who are filling VA prescriptions.  In the last example, you want to measure the health care costs of patients who are being seen in two different VA medical centers.  Let's just take patients with heart failure, for example.  You want the costs to represent whether patients might have more admissions at one medical center than another as opposed to how expensive the input for that inpatient admission is.

I'll just give you another few seconds.

*Moderator:*It looks like the responses have stropped streaming in.  We have a pretty varied audience.  About 48 percent selected option one, about 64 percent selected option two, about 32 percent said option three, and 45 percent went with option four.

*Jean Yoon:*Okay, great.  It looks like mostly people thought that people should use DSS for a budget-impact analysis.  In the other examples, they would use average cost data.  As we go through our presentation, the reasons will be more clear for the benefits of using DSS versus average cost data for different purposes and the different types of question you're trying to answer.

I'll next talk about the DSS national data extracts.  Where does DSS get its costs from?  DSS pulls information from various data sources.  It pulls workload and clinical information from the VISTA system, which is the electronic medical record system for the VA.  It pulls financial data from FMS, which is the VA accounting system, and from PAID, which is the VA payroll system.

This information gets combined with information from each medical center on time allocation and relative values.  The relative values are the relative resources that go into producing different types of encounters and inpatient stays in the VA.  All this information gets combined into the DSS VISN level production databases.  These databases have a lot of information, and they're not commonly used for research.

Some of this information gets extracted into the national data extract of DSS, which is what we commonly use for both research and operational projects.  In order for DSS to determine the cost of an inpatient stay or an encounter, it first needs to determine the cost of a product, and products are the components of an encounter.  An example of an product might be an X-ray or a ten-minute clinic visit.

First, all the costs in the VA medical center are assigned to different costs centers or departments in the medical center, and this is done through staff labor mapping and financial data in the medical center.  The cost of all overhead is distributed to direct-care departments.  These are departments that provide direct patient care.  In each department, the total products are tabulated.  For example, a primary care clinic would tabulate the total number of primary care visits that they provide.  Each product is associated with a relative value.  The relative value can represent, for example, the provider and staff time that go into producing a ten-minute clinic visit.

Using the cost of these departments, the total products that they're producing, and the relative value of each these products, we can determine the cost of each product.  Then to get the cost of the encounter, it's just the sum of all the products that occur during the encounter times their cost.

I'll next talk about several different DSS national data extract files.  I'll talk about inpatient, outpatient, and pharmacy files.  There are many other DSS data extracts that are available.  For example, there's the intermediate product department, which has more detail than the inpatient and outpatient files that I'll be talking about.  There's aggregate data at the account level and budget cost center level.  There's also clinical data extracts.  I won't be talking about these last three, but there are plenty of resources, which I'll talk about at the end of the presentation.

I'll first talk about the inpatient discharge file.  This file records the care of patients who are discharged in each fiscal year, and there's basically one record for each discharge.  It may include costs if they're incurred in a prior fiscal year.  For example, if an inpatient stay crossed two fiscal years, it would report the cost for the total stay.  The data that are only in the discharge file are the discharge stay, the day that the patient left the hospital, the total days of stay, and the discharging bed section.

These are three made-up examples of discharge records.  These are the records for the same patient, and you can see that these are three separate hospital stays.  The first hospital stay began on September 24th, 2005, the second began on October 31st, and the third stay began on October 4th.  Each of the costs you'll see in the last column is the cost for that total hospital stay.

There is another inpatient file.  It's called the inpatient treating specialty file, and this is basically a monthly record.  It's one record per treating specialty per month.  There can be more than one record in a month if there was more than one treating specialty in a month.  What this file does is it reports all care thus provided during that fiscal year, and it includes stays that are not yet over.

The data that are only in the treating specialty file would be the treating specialty, a census indicator for whether or not the patient left the hospital, date of entry and exit from the treating specialty.  There is no discharge date from the hospital.  There's also the treating specialty length of stay, but there's no total length of stay across treating specialties.

Again, here are three made-up examples of treating specialty file records.  These are for the same patient.  The first record is for one hospital stay, and the second two records are for another hospital stay.  You can see from the first record that the patient entered and left the treating specialty 15 on October 1st, and the cost that you see is for that one day that they were there in that treating specialty.

For the second and third record, you can see that they were seen in the same treating specialty, treating specialty 15.  They entered it on October 31st and then left on November 11th.  There's one record for October, and then there is a separate record for November, and the costs are broken out for each month.

Basically, a treating specialty file has many more records than the discharge file, and it can be more complicated to work with, but as you can see, there's more information.  So if you need information on what treating specialty the patient received care from, you want to use the treating specialty file as opposed to the discharge file.  The data that are in both inpatient files is the admit day to the hospital, the admitting diagnosis-related group, and the principal and admitting diagnosis.

There's another file for outpatient care, and so this file records—it has one record per patient per day per clinic stop.  This is slightly different from the National Patient Care Database because this allows for more than one record per clinic stop per day.  DSS also includes care that's not in the NPCD events file such as prosthetics care.  There's limited clinical information.  There is the primary diagnosis, and there are CPT codes.  If you want more clinical information, you'll need to merge these records with records from NPCD.

The data that are only in the outpatient file is the date of the encounter, a DSS identifier, which is also called the clinic stop.  DSS uses a pseudo-stop code for things like prosthetics and pharmacy.  There's also a flag variable which identifies the data source, so whether the data came from the NPCD, pharmacy, prosthetics, the VAST CBOC file.

Here are three made-up examples of outpatient records.  This is for the same patient.  You can see VIZDAY.  It's for the same visit day, and there are three different clinic stops.  There's a separate cost for each of the clinic stops where the patient was seen.

The cost variables that are in all of these files are fixed direct cost, fixed indirect, variable direct, variable supply, and total cost.  There's also another category for variable labor category 4 and 5 for provider labor.  Costs that are only in inpatient files are subtotals for lab, nursing, pharmacy, radiology, surgery, and a category for all other costs.  These costs are further broken down into variable, fixed direct, fixed indirect, and supply cost.  These costs are not in the outpatient file.

There's a separate extract file for pharmacy.  There's inpatient and outpatient files.  For the outpatient file, there is one record per prescription or supply per person per day.  In the inpatient pharmacy file, there is one record per person per day.  DSS will sometimes group two prescriptions into one record if they are for the same NDC and the same person on the same day.

This is some of the information that's in the DSS pharmacy file.  There is medication information such as the name of the drug; the NDC, which is the drug code; there's a formulary indicator; and the VA drug class.  There's dispensing information such as when the prescription was filled, what the quantity was, and the days supplied.  There's limited patient information including the patient's scrambled SSN, date of birth, gender, and age.  There is ordering provider information such as the ID and the provider treating specialty.  This information can be linked to clinical information on patient stays and visits using the patient's scrambled.

For the cost information, the VA cost includes direct labor cost, indirect costs of the pharmacy department, and supplies.  If you want to get the total costs for a VA prescription, you'll need to add the acquisition cost plus the dispensing cost.  When you use a DSS pharmacy file, you'll sometimes see negative costs, and the reason for this, for example, can be like a return to pharmacy.

The VA charges some copayments for prescriptions, and it just depends on income and disability percentages for the veteran.  These rules and eligibility levels change year to year, so you can check the VA copayment amounts on the VA intranet.  These payments are not shown in DSS records.  They only show VA's expense.  The medical care cost recovery file could show reimbursement from private insurance if it was collected by the VA.

This applies to all the records, all the files that I talked about.  You will sometimes see cost outliers.  You should look for cost estimates that are unexpectedly high given the characteristics of care.  There can be sometimes a mismatch of cost and utilization that can result in unit costs that are very high cost or can also be negative.  There are DSS quality assurance efforts, which is extremely high outliers are looked at when DSS national data extracts are built.  But you should be aware of the presence of outliers and look for it as you use it for different projects.

There are several different advantages of using DSS data.  DSS costs are meant to reflect facility differences in productivity, efficiencies, and economies of scale between different medical centers.  DSS has pharmacy data, unlike HERC average cost data.  DSS previously had community nursing home stays.  It no longer has community nursing home stays I believe as of 2010.  It does include state nursing home stays.  Since DSS is an activity-based method, it's considered more accurate than other methods such as HERC average cost.

I want to pause and see if there are any questions before I turn it over to Todd to talk about HERC average costs.

*Jean Yoon:*You do have one question, Jean.  For the DSS outpatient file, will the data include multiple CPT codes for each visit or lump them all together?

*Jean Yoon:*There can be multiple CPT codes in a DSS outpatient record.  I don't remember the total number that there can be.  I think it's something like 10 or 11 different CPT codes.

*Jean Yoon:*Well, we should clarify that the DSS record, the outpatient file itself doesn't have any CPT codes.  It's an activity-based accounting system.  We frequently merge it with the SE file, which has all the CPT codes.  You can easily link the two, but just keep in mind that the DSS isn't based on the CPT code.  It's based on activity-based costing.

*Jean Yoon:*Okay.  Thanks, Todd.

*Jean Yoon:*Then another question is: Do you know does DSS determine its relative values?

*Jean Yoon:*The VA has a relative—it has a standard list of relative values for different products in the VA, but I believe that VA medical centers can modify those relative values to reflect what's happening in their individual medical center.  I would have to check with the managerial cost accounting office.  I believe it is available to VA employees on their website.

*Jean Yoon:*That's it for right now.  We have some more questions when we get towards the end.

*Jean Yoon:*Okay.  Well, I'll turn things over to Todd then to talk about HERC average costs.

*Jean Yoon:*In the meantime, if people have more questions about DSS, people can easily type them in.  Actually, Jean, we have one more question that came up.  Does DSS have any patient-based summary costs for inpatient-outpatient pharmacy per veteran per year analogous to HERC?  I will say, no, they do not, and we've been creating some of those.  Let me talk about the average cost data sets, and then we can tell you a little bit about those data sets.  Good question, though.

Again, this is Todd, and welcome to the second part where I'll be talking about the HERC average cost data sets.  Just to distinguish between the DSS and—as Jean mentioned, the DSS are based on an activity-based costing methodology.  Some people refer to this as a bottom-up approach.  Why do we mean bottom-up?  Well, if you think about each activity—and it could be, for example, a couple minutes of time, a couple sutures, drugs—you're taking all of those activities and you put cost associated with all of them, and then you sum them all together.

The top-down approach, which is what HERC uses, we take relative value units, and somebody already raised the question of CPT codes.  Well, CPT codes connect to RBRVSes, the resource-based relative value units from CMS, and you can use these top-down approaches to estimate the average costs of an encounter.

Just to keep in mind, we'll often refer to the HERC as sort of a top-down and a bottom-up, and we have different ways of valuing these data sets.  I think of the top-down as a national approach, and it's experience-based, experience because of the use of these CPT codes or things like length of stay.  DSS is very much a local approach and activity-based.

Let me just walk you through a little bit about the methods on how we calculate these data sets for HERC.  We're in the process of doing it right now for the last fiscal year.  For acute medical surgical stays, what we're trying to do is estimate what it would cost to have a stay in a Medicare hospital, and we use a regression model.

We take the Medicare data, we look at the stays in Medicare, and we estimate their cost based on things that we observe.  We create a regression model.  We use that regression model then to impute the cost in VA.  Largely, what we're seeing as important in that regression model are things like the DRG, the diagnostic-related group, as well as length of stay.  We have papers that describe this as well.

There are other types of inpatient care, things like intermediate medicine, nursing home care, and in those types of situations, we just use length of stay.  It's just purely a function of length of stay.  If a patient stayed twice as long as another patient, you're going to see a cost estimate that's twice as long—twice as high

For the outpatient care, we use—we create what we think of as this hypothetical Medicare payment based on the procedure codes, these CPT codes that someone raised, and assign it to each visit.  Yes, people can have more than one CPT code, and we sum those all up.  That glosses over some of the details, but if you're interested in the details, I can connect you with all the papers, and we can walk you through it.

Like I said, we have this cost regression.  Length of stay, the days of intensive care, and the diagnostic-related group, these are really key variables.  When you estimate this regression on Medicare cost, you end up with an R-squared that's around .70, so about 70 percent of the variance is accounted for.  That's not perfect.  There is attenuation.  We're not doing a great job with estimating the extremely low-cost cases or the extremely high-cost cases.

We identify the medical/surgical components of stays in the VA patient treatment files, and we're consistent with non-VA hospital definitions.  For example, if someone goes into medicine and then is transferred into a different bed section that's related to surgery and then gets transferred back to medicine, we sum that all together into a single record consistent with what Medicare would think about as a single medical/surgical stay.

Then we apply these regression parameters.  It's the same regression parameters that are used in the non-VA.  We apply them to the VA.  We can impute the cost.  Then we take these estimated costs, *[clears throat]* excuse me, in VA, and we adjust them to reflect the actual expenditures from DSS.  If you were to sum up the HERC costs, they would sum up to the same as the DSS cost.

There are other types of inpatient stays, and this is where we use just the length of stay.  Let me list them for you here.  It's rehab; blind rehab; spinal cord injury; psychiatry; substance use; intermediate medicine; domiciliary; psychosocial residential rehab, which is a less-intensive form of psychiatric and substance-use care; and then long-term care.  Each of these types of care, we just assume—we take the total dollars for VA, divide it by the total days of care from VA, and we end up with an average daily estimate.  Then if some patient has a seven-day stay, we take that average daily estimate times their seven.  That's how we estimate their cost.  It's not incredibly precise.

We then create a file which we think of as the HERC inpatient discharge data.  One of the goals here is to have a file that can be easily merged to the PTF main file.  There's a one-to-one correspondence between what's in the main file, the PTF main, and what's in the HERC discharge files.

Just to be clear, the PTF has this definition.  It's reflecting stays that ended in a discharge in the fiscal year, and then we exclude and we don't estimate cost for stays that began before fiscal year 1998.  Just to keep in mind, there are a very small number of these records, but there are some veterans in the system who have been in the system since the '60s and '70s.  We just don't know how to accurately estimate the cost for their full stays before fiscal year '98.

In our files, you will also see subtotals.  One of the reasons that people like using the HERC files is they'll get a total cost estimate, and then you also see a subtotal for medicine and surgery for blind rehab and so forth.  That's sometimes helpful for folks.

For the outpatient costs, we use the CPT codes and HCPCS codes, and there's up to 20 per visit.  If you look on the SE file, you'll see that there are all these CPT codes.  We link them to their relative value units.

We assume that the facility reimbursement rates from Medicare—there's two different types of reimbursement rates.  One is a clinic reimbursement rate, and one is a facility reimbursement rate.  We assume that we're using the facility reimbursement rate.  Again, we reflect these to—adjust these to reflect the expenditures in all of DSS costs for outpatient care.  Again the sum of our estimates for the HERC equal the sum for the DSS.

We also create this other file, which is a person-level annual cost file.  For example, for many people, they're interested in just estimating what's the cost of a patient in the VA this year, and we have a one-person-per-record file.  In fiscal year '13, there were about 5.8 million veterans who used VA.  You're going to see 5.8 million records in this file.  You'll see the total HERC costs as well as five subtotals for inpatient care and five subtotals for outpatient care, and then a length of stay for inpatient care.  It's a handy file if you're just trying to quickly estimate people's annual costs.

We also include the DSS outpatient pharmacy.  It's something that we can't estimate, and as Jean said, DSS do a very good job estimating the pharmacy cost, so we just pull that in as a pharmacy category.

Then if a patient has a stay that splits fiscal years, so imagine, for example, that a patient entered in September and was discharged in late October, we would assign the cost to each fiscal year in proportion to the days in the fiscal year just to try to split it up and make sure it's accurately reflected in the year in which the services were provided.

Getting back to—we've given you this top-down versus bottom-approach, and so one of the questions we often get is: DSS versus HERC, and/or?  Let me address that question.  People often come to us and say, "Which should we use?  Should we use the DSS, or should we use the HERC?"

We have a couple criteria that we go through.  One is, is the costing method that we use to estimate these costs consistent with your study goals?  Then there's the question of precision versus accuracy, and Jean's already sort of tipped her hat about some of our ideas there.  Let me walk you through them.

For example, if you were studying the cost-effectiveness for the U.S.  health care system, and you were interested in whether a treatment was more cost-effective than another treatment, you could choose the HERC.  We use these national cost estimates.  You could also use DSS, but we generally say that the HERC is something that we use—in this case, it uses non-VA relative value units.  There are some researchers who like being able to talk about the generalizability of these costs and say—you could say, "Well, they're based on CPT codes and non-VA relative value units."

We like to think that the HERC costs are more like costs typical of non-VA health care settings, but one of the areas where I think HERC costs definitely do not work well is determining efficiency.  The HERC costs at two different facilities—if a patient has the same usage at two different facilities, you'll see the same cost there, the same national cost.

In DSS, you'll see that the relative value units can change at those facilities, as can their activity-based cost estimates.  The DSS cost estimates reflect really different productivity inputs, efficiencies, and economies of scale at the local level.  Many times, you have to make strong assumption to make the HERC work in that case, and we often feel that that—that those aren't valid.  So if you're using a study on efficiency, we'd often recommend DSS.

Keep in mind that we would say that the bottom-up approaches such as consults can provide very precise estimates.  The goal in the DSS, as Jean talked about, is trying to track all of the different input parameters, assign a cost to them, and this can result in an extremely precise estimate.  We've done some work, for example, on CABG surgery where in a clinical trial we tracked different amounts of time spent in the OR, the different blood products used, and those correlate well with the DSS cost estimate in ways that you would expect.

In the HERC data, as a comparison, everybody who had the same DRG, so the same CABG as a single DRG, they were all assigned the same cost as long as they have the same length of stay.  There's no difference there, so in that case the DSS gave a very precise estimate.

If you use the DSS data, the thing that you need to keep in mind is that you need to control for geographic wage differentials.  Just to keep in mind, what does that mean?  In San Francisco and Palo Alto, where we are, wages are about 60 percent higher than the national average just based on the cost of living and the market wage for clinicians.  In that activity-based costing estimate where they're actually tracking clinician salaries, those dollars flow right into the care provided in Palo Alto and San Francisco.  Comparing Palo Alto and Houston or Kansas City, you wouldn't want to say, for example, that Palo Alto is being inefficient unless you control for the site differences and the wage inputs.  We're just a lot more expensive for labor than those two sites.

In terms of accuracy, one of the things that I would say is that bottom-up approaches, in their desire to be incredibly precise, can also lead to these rare irregularities.  Jean talked a little bit about mismatches.  One area where you might see a mismatch is pharmacy, and another one is anesthesia.  The reason for those mismatches there is that you often have quantities and dosing information, and if you're off on quantities—you mean milliliters and you put liters or vice versa, and you have your prices that are set for liters versus milliliters—you can end up with some very odd multiplications that result in some very off estimates.

One of the ways that we get around this, and one of the things that we often do, is we often use both.  In our programming, a lot of our programmers are trained just to pull both cost estimates, and then we use one as the primary analysis—let's say we're often using DSS—and then we use one as a sensitivity.  Or you can try to figure out are these data—how consistent are they?

Mike Chapko and some folks up in Seattle compared across a broad number of cases the DSS and the HERC average cost data to say a little bit about the estimates of these two systems, and found that they were often very similarly matched.  But in our work, we touch every piece of VA data.  There is no doubt that there are irregularities out there.  They're rare, but this is one method of identifying and trying to fix those.

I would say it's less important if you're working with the entire national data.  Often your mean might be still accurate if you only have one or two inconsistencies.  But if you're working with a very small trial and you happen to have one of those inconsistencies in there, it could have huge leverage on your trial results.

The rest of the slides are data resources.  We should probably jump in and address some of the questions that people came up with.

*Jean Yoon:*The first question actually goes back to DSS.  This is the DSS mainframe does have CPT codes for patient encounters, but they mostly are not costed.  I believe the CPT codes were removed in *[audio cuts 1 second*.  Prior to that, there was the primary CPT code.  That's right that DSS costs the encounter.  It doesn't cost a single CPT code.  It's the same for HERC average cost.  It's the cost of the entire encounter.  There is not a cost for each CPT code in that encounter.

Another question goes back to merging DSS outpatient files with NPCD.  For VINCI users, do we merge with CDW outpatient tables, and is NPCD being retired?  Go ahead, Todd.

*Jean Yoon:*There is a learning curve for everybody with the VINCI table and the CDW table.  I think we're all very familiar with these MedSAS data sets.  There's less clarity for us about what's happening with the CDW.  We're struggling with that, and we're moving all of our HERC data sets over to VINCI so that people can use them.

The plan is, my understanding, and Jean, you can correct me if I'm wrong, is that there is going to be a replacement of the NPCD that's still in the works.  My understanding for the NPCD, the folks that are doing it is the VSSC folks.  The same with PTF.  Eventually it'll be retired, and I believe the IPAC is working on that.

*Jean Yoon:*Right, and we don't have a timeline for that yet.

*Jean Yoon:*Yeah.  I would say that one of the things that everybody is struggling with with the CDW tables is the business rules.  One of the nice things about the business rules for the MedSAS is they were at least consistent, if not well understood, and I think people are struggling with a little bit of lack of clarity with the CDW business rules.

When you're trying to merge all these data set and especially over time for research purposes, it can be a little bit more challenging.  I just don't think we have enough information to make concrete statements there yet.

*Jean Yoon:*The next question applies to HERC average costs.  It's a question about community living centers.  CLCs provide a level of care that is generally not available in community nursing homes.  How do you cost—how do you do the cost estimates for CLCs for comparison with community?

*Jean Yoon:*That's a great question.  There's two issues here.  One is that if you were to compare nursing homes in VA, the community living centers, and non-VA, our staffing ratios are very different.  We have a much higher rate of nurses than ancillary staff and assistants.  That leads to higher cost.  There are questions about the hospital facility overhead being proportionally allocated to the living—the community living centers.  I think you have to be careful in any way you do it when you compare a VA to non-VA for these differences.

In our HERC average cost files, what we do is we say the costs that are reported in the treating specialties for the community learning centers—living centers is correct, and we take the total cost for CLCs, divide it by the total number of days, and we estimate a daily cost.  Now if you estimate that daily cost, to my memory, it's extremely high.

If you were to compare that, for example, to—there's MetLife, the insurance company.  They're trying to sell long-term care insurance, so they often want to impress upon consumers the cost of staying in a nursing home.  They'll often report national averages around $400.00 per day in a nursing home.  I will say that the VA is over twice that.  Now I don't want to put any normative statements on quality or any of that.  I just want to say there's a lot going into those comparisons.

Do you want to add anything to that, Jean?

*Jean Yoon:*No.  The same person had a follow-up question, which I can't find right now.  I think it had to do with are different types of CLC stays costed the same, like general psych and other types of stays?

*Jean Yoon:*Yeah, that's a great question.  Yes, they are costed the same.  In the past, Wei Yu, who is an economist here, tried to use the MDS, the minimum data set, to estimate the relative resource use among different nursing home patients and assign that as a resource unit.  We stopped doing that in 2000 as there was a huge transfer and changes in systems for VA.

*Jean Yoon:*Okay.  Wei is actually asking for the HERC average cost estimate.  Do you exclude outliers in your regression equation?

*Jean Yoon:*We don't exclude outliers.  One of the reasons—there are many different cost models, and Wei, I can point you to the paper that we talk about.  We ended up not using GLM or logged cost models because it produced extremely high cost outliers, and we didn't know what to do with it.

In the end, for that regression approach, we used ordinary least squares, which resulted in very few outliers, if you had any outliers.  The problem with it is we ended up with some negative cost which we had to deal with.  That was more of a rare instance, though.  Great question.

*Jean Yoon:*Okay.  This question is regarding the HERC personal annual costs.  Can you search this file for all patients with a given diagnosis?  An example they give is stage III lung cancer.

*Jean Yoon:*Do you want to explain how you—so you've done some of that linking it to patients, Jean.  Do you want to talk about ...

*Jean Yoon:*Right.  What this patient level file basically has is the patient identifier and then their cost broken out—their total annual cost broken out into different categories for inpatient, outpatient, fee basis, and pharmacy care.  What I've done in my own work is gone into the medical staff files and, for example, identify patients with heart failure.  Then for those subset of patients, I can then merge them with HERC personal annual cost to get their total annual cost of care.  You will have to go into these clinical data sets to get clinical information, but it makes it a lot easier rather than working with the separate inpatient and outpatient cost files.

*Jean Yoon:*The person asked about stage III lung cancer.  So you could identify everybody with stage III lung cancer, and you could merge them to this file and say, "Here's all of these patients' total cost of care in the VA and fee basis for the year."  It doesn't tell you whether it was for stage III lung cancer.  Perhaps they had another chronic condition, and it was for that.  You're just going to see that total cost.

*Jean Yoon:*Right.

*Jean Yoon:*The next question is: When HERC files are moved to CDW, will they be accessible for research?  Yes, the plan is to make them accessible to research.  They are actually up on VINCI right now, but they have the scrambled Social Security number with them, and that makes it much harder for researchers to access.  We're in the process of stripping that and putting in, instead of the SSN or the scrambled SSN, we're putting in the SID so that researchers will have an easier time accessing these files.

*Jean Yoon:*It's my understanding that if you want to use the HERC file for research and you also have real SSN access, you can access the HERC files right now.  In the future, if you don't have real SSN access, you will be access the HERC files at CDW.

When are the HERC files expected to be routinely available through VINCI?  Todd, can you speak a little bit about what years are currently in VINCI?

*Jean Yoon:*Yes.  All the years are currently up on VINCI.  I think that they—the way that they have been put up there with our work and the NDS folks is that you have to have DSS data access rights to be able to access these files.  Those are the files that we're going to try to make so that they have an SID so that researchers have an easier time accessing them.  That's what's there right now.  We're in the process of making the FY13 data right now.

*Jean Yoon:*There's a question about what did you do with the negative cost?  I'm not quite sure.  I think this might apply to the HERC average cost.

*Jean Yoon:*Yeah, this applies to the inpatient cost where I said we use OLS.  We didn't have the high-cost outliers.  I see the person's name.  I'm happy to send Ron the paper.  Luckily, it wasn't a huge number, but it caused us great consternation about how to handle these folks.  In the end, we ended up with an average daily rate, knowing that it's hard to estimate with a regression the true cost of each person.

*Jean Yoon:*Okay.  The next question asks how do you merge cost files with the clinical files?  Do you merge them using their SSN?  I'm only going to speak about DSS and the MedSAS files.  I won't speak about data that's at CDW because I am not that familiar with it.  You would use the patient scrambled—for outpatient files, you can use the patient scrambled and their visit data and the clinic stop in order to merge in information from the NPCD file.  For the inpatient side, you would want to merge the patient scrambled, their admit date, and the station.

Do you want to say anything about merging the HERC average cost data with the clinical data?

*Jean Yoon:*Yeah.  In the past, for the inpatient average cost data, they merged one-to-one with the PTF main.  This is the HERC discharge file, and it's a very straightforward merge with a scrambled Social Security number, admit date, discharge date, *[inaudible]*, and admit time.  We just haven't done enough work with the CDW and the CDW tables to say enough about how is that merge going to happen.  But if you're using the MedSAS files on VINCI, that should happen the same way that we've used in the past.

*Jean Yoon:*Okay.  This person writes: Where are the HERC files located on the CDW?  If you email us, we can send you location of the files at CDW.

*Jean Yoon:*Then the next question is when are the FY13 data expected to be available?  We're in the process—we're about halfway through the inpatient data, and I know that Kieran is about halfway through the outpatient data.  My guess is in the next month or so.  Great question.

The next question is could you say more about those sources of diagnoses, inpatient/outpatient encounters?  The problem is how are these used, combined, or held separately at HERC and DSS?  What is known about sensitivity and specificity?  This is a large question here, so let's address the first one.

The sources of the diagnoses—the way I think about the patient treatment file, historically when VA was set up as a system—and this goes back to before I got here in '99.  There are professional coders for inpatient care.  The PTF is based on professional coders, and so that's what's translating into these diagnostic codes on the PTF.  Now PTF is just inpatient care.

When the desire came through in the mid-1990s to have outpatient care recorded and CPT codes recorded, they created the National Patient Care Database.  Now the NPCD relies on physicians or clinicians to code the records, and that's both in procedures and diagnoses.  There's no professional coders in the NPCD that's capturing that workload.  It's based on the clinicians themselves.

Keep those two separations in mind.  Eventually, the IE file was created.  The NPCD has the ability to capture all physician workload, whether it's inpatient or outpatient, and so they opened up the IE workload file.  Again, it's capturing CPT codes for inpatient encounters, but it's again relying on clinician coding.  NPCD is clinician coding; PTF is professional coders.  Hopefully, keep those separate.

Then how are these—the next part of this question is how are these used or combined or held separately in the HERC and the DSS?  I guess my answer to that, and I'm not sure it's the best answer, is we use the codes as if they were correct.  We'll take the coding from the PTF and work with them and estimate the inpatient data.  We'll also take the CPT codes from the SE file to estimate our pseudo-Medicare payments.

DSS uses a slightly different methodology just because it's not relying on these diagnostic codes or CPT codes.  They're using activity-based methods.  They're actually trying to track who is providing the care or how much time they were providing the care for each patient and build it up that way.  It's a slightly different method.

There's another part of this same question, which is what is known about the sensitivity and specificity of the diagnoses?  Then providers generally do not list all diagnoses on encounters.  David Bates presents that information in VA that only about ten percent of patients on heart failure had it on the problem list.

This person's name is Mary.  Mary, I don't have the answer here for you.  I apologize.  We will have to get back to you on that, and you may actually know more than I know.  Do you have any more on that, Jean?

*Jean Yoon:*No, I don't.

*Jean Yoon:*In general, we rely heavily on the VIReC to provide information on the clinical side whereas we focus more on the cost side.  But your point's well taken that we often take for—take as given what's in these records, and that may not always be the case.  One of the main or one of the strong interests in natural language processing is to pull out information in notes that may not make it to the diagnoses or procedure files.

*Jean Yoon:*The next question is actually a good segue into the data resources section, so I just want to talk a little bit about DSS data access.  If you have additional questions, we still have time at the end to answer more questions.  This should be relatively quick.

Access to DSS data should be requested through CDW/VINCI and the National Data Systems.  You can also get more information on MCA, which stands for Managerial Cost Accounting office.  It used to be called DSO.  This is a support office, and they have an intranet website also.  All DSS files were removed from the Austin information center in 2013, but there are files on CDW/VINCI from 2001 to 2012.

There are DSS national data extract files that are in SQL format, and it's available from fiscal year 2005 to the current year.  The CDW server is listed here.  There's also DSS data that's available from reports from the MCA intranet sites.  These reports have aggregate information, not record-level information, but they can be very useful if you're not interested in looking at the actual records themselves.

For HERC data access, you should also request access through VINCI and NDS.  All historical files are currently available from the Austin information system, and then we are making all HERC files available on VINCI to research and operations.  HERC has several guidebooks on DSS and HERC data sets.  We are currently in the process of updating them to incorporate information about CDW, so these guidebooks have not yet been updated.

As far DSS pharmacy data resources, VIReC has a pharmacy prescription data guide on their website that's listed here, and HERC also did a technical report that compared outpatient cost data in the DSS national pharmacy extract file with data that was in the pharmacy benefits management database.  It's available on the HERC website, and basically it found that it was actually a good match of records from PBM and DSS.

Here's just a listing of the next classes in the HERC cyber-court series.

*Jean Yoon:*Maybe we answered the question.  Patrick had asked, "I was confused by whether we said we have currently access to the HERC and DSS data.  Is it difficult to gain access?"  I have a hard time answering the question because the access is person-dependent on whether you're doing operational work for VA or research work.

Just to clarify, if you're not affiliated with VA, you can't access these data sets.  These are really just data sets for people who are employed by VA.  Let's say you're operations.  You should have easy access to them.  If you're research, you just have to go through the approval, the DART request for research, specify the right approval, then you should have access to them.

Any other questions?  If there are other folks that want the citations on the paper that we published, let us know, and I will email them to you.  I noticed that Ron Hayes is one of the people who wants them, so if there's others, I'm happy to get them to you.

*Jean Yoon:*If you have additional questions after the session ends, feel free to email one of us, and we'll respond to them.  Other than that, we hope you can join us for the next classes in the HERC series, and we hope that you will fill out this brief evaluation.  This is meant to improve the quality of the sessions in the future.

*Moderator:*Yes, thank you very much, Jean, for letting them know.  To our attendees, yes, please do fill this out.  It is your opinions that help improve the program and also decide which sessions we schedule.  I will be leaving it up even after we conclude the presentation, so take your time filling that out.  As always, please visit our upcoming sessions catalog to sign up for the next session in this course.

Thank you so much, Todd and Jean, for presenting for us today, and thank you to our attendees for joining us.  It looks like we will see you back here on the 14th.

*Jean Yoon:*Sounds great.  Thank you so much, Molly.

*Moderator:*Thank you.  Bye-bye.

*Jean Yoon:*Bye.

*[End of Audio]*

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