HERC Health Economics Seminar- 17 -Department of Veteran Affairs

**Department of Veteran Affairs**

**HERC Health Economics Seminar**

**"Cost of Readmission in the VHA: Implications for Reimbursement Policies"**

Todd Wagner:  Hi, my name is Todd Wagner.  I just wanted to welcome everybody to this month's cyberseminar.  Today we have Jason Hockenberry, who received his PhD in economics from Lehigh.  I don't know, Jason, maybe you were watching a little bit of March Madness this past weekend, as they upset Missouri, then went on to lose unfortunately.  It was a fun game to watch.  After Lehigh, he was an assistant professor at the University of Iowa—

Jason Hockenberry:  —actually to clarify, that was Duke.

Todd Wagner:  Oh, that's right, it was Duke that you guys beat, that was even better.

Jason Hockenberry:  Sorry.

Todd Wagner:  Thank you.  I appreciate the clarification.  It was Norfolk State that beat Missouri, that's right.  So after Lehigh, he was an assistant professor at the University of Iowa and collaborated with a number of folks at the Iowa City VA.  Recently, he joined the faculty of the Department of Health Policy and Management at Emory at their School of Public Health there and he has an interest in human capital effects on quality of care, with a particular focus on surgical care.  So Jason has been working on some very fascinating research that he's going to present today and perhaps, more importantly, he is one of the wittiest health economists I know and he also just sort of leaves me in stitches laughing.  So I'm very pleased to introduce him today, the title of his talk is: The cost of Readmissions: Implications for Reimbursement Policies.  This relates to work that he has been doing with Jim Burgess, so Jim is also on the call and can hopefully answer questions if we need.  Thanks so much and, Jason, take it away.

Jason Hockenberry:  Thanks, Todd.  I appreciate it.  This work, as you're going to see in a second, is part of a larger grant, funded by VA HSR&D and Jim and I have been working closely on this, other collaborators include Justin Glasgow, Mary Vaughan Sarrazin and Peter Kaboli, who have been integral to this work.  To be completely clear, what we're going to talk about today in no way reflects the position or opinion of the VHA and I will take credit for all errors and I will give glory to all my collaborators.

So the background here, as we all know, hospitalization is costly.  It represents Medicare's second largest expenditure and trails only home health and it accounts for about thirty percent of Medicare's expenditure and currently readmission to the hospital is prevalent.  Readmissions within thirty days of discharge—there are various statistics out there, but they're in the range of 18 to 20 percent or so and this has been a topic for a couple of decades of interest to clinicians and policymakers and health services researchers like myself, going back to the late '80s there was concern over movement of perspective payment and whether this would lead to discharging quicker and quicker and higher rates of readmission and that has carried through.

Recent work by Jenks, Williams and Coleman in 2009 in the New England Journal of Medicine sort of brought this back to the forefront of the discussion and there was a lot of policy stuff in the 2000s in general, discussing whether the rates were too—quote, unquote—'high' and whether potentially addressing readmissions would reduce expenditures.

So national policy is moving toward penalizing hospitals for higher than expected readmission, beginning October 1st of 2012, which is the start of Fiscal Year 2013, the hospital readmission reduction program, which was part of the ACA legislation will lower payment rates for all Medicare discharges, if acute care hospitals experience higher than expected readmission rates for three conditions in particular.  This is acute myocardial infarction, community acquired pneumonia and CHF, AMI, CAP and congestive heart failure, CHF, respectively.

In addition to this, there is a large national discussion about bundling payments for all forms of care or potentially just for hospitalization and as someone who teaches quite a bit about health insurance, this is analogous to the idea of capitation for certain services and based around an episode of care, which the episode has a variety of definitions.  Currently, the discussion is somewhere between a 30 day and 90 day bundle and the services to be included in that bundle are still up in the air, as I understand it, but there may be some of you on the line who know better than that.

So the motivation for our work is thinking about this from an economics perspective and maybe even thinking about a production model of producing patients in an inpatient setting who are then healthy enough to not have to come back to the hospital.  So simply we're thinking about the fact that targeting readmissions to reduce healthcare costs raises a couple of questions.  Conceptually at the facility level, targeting this may be problematic because there may be limited ability on the part of facilities to actually reduce their readmission rate and we'll talk about some of that as we go on today.  

On the quality issue, currently the supposition is that a large proportion of these readmissions are potentially preventable.  In reality, as an economist, I think about this in the sense that readmission can be an appropriate input to help production, rather than reflecting poor quality of care during the initial or index admission.

By the way, as I go on, I'll talk about the index admission, which is the first admission for the hospital with no trailing admissions in a prior period and then the readmission is something that happens within 30 days afterwards, which is Medicare's current definition and on these prediction points on the slide, the assumption under the penalty approach is that the facility readmission rate can somehow predict readmission of individual patients in the current or subsequent period and in reality we know from the literature that readmission is notoriously difficult to predict, particularly for these conditions.  So Joe Ross and colleagues have a 2008 Archives of Internal Medicine article on this, Hassan and Kaboli who is one of the collaborators on this have a 2010 General Journal of Internal Medicine article on this and Hamlin and colleagues recently published in circulation a 2011 paper where they tried to introduce more than just the type of data that would be available from claims in terms of clinical severity and whatnot and they didn't find much improvement in their prediction model.

Interestingly—and I'll bring this up again towards the end— Allaudeen and colleagues out at UCSF actually asked physicians and other people involved in the care of patients and their index admission to subjectively determine [ex ante] whether or not the person would be readmitted within a period following the index admission and it turns out that they didn't do much better than a coin flip in predicting that and so even the physicians who are treating these patients as they discharge them have a hard time pointing to the patient who they would think would be readmitted.

Again the penalty approach here suggests that facilities with higher readmission rates may have a higher cost for each individual patient, right?  So you would penalize those facilities thinking that you would create an incentive for them to lower their readmission rate and on the basis of the idea that the overall episode cost of care for their patients was somehow higher than other facilities' patients on a risk adjusted basis.  I'll tell you we're not really able to find evidence of this in what we do with the VHA data as you'll see today. 

Another assumption underlying the current proposals and this is noted in an RWJ legal notes brief is that hospitals somehow have an incentive to readmit patients, right?  They can discharge sicker and quicker, again going back to that late '80s, early '90's literature knowing that if a patient comes back for non iatrogenic reasons after 24 hours, they can get another DRG payment.  This assumes implicitly—and maybe people have stated this explicitly that when the patient is readmitted that they are somehow of lower cost and that the margin would be higher by discharging them from that index admission and then readmitting them later.  It's possible—and we're going to talk about this as we look at the data from the VHA that those who are readmitted actually could be more costly and from what we were able to find in the literature, there's not a lot of description of this.

Todd, at this point, setting up the conceptualization, we may want to see if there are any questions?  Are you fielding any at this point?

Todd Wagner:  I haven't seen any yet.

Jason Hockenberry:  Okay.  Great.  So moving to our contribution, we're going to examine acute myocardial infarction, community acquired pneumonia and congestive heart failure patient admissions in the VHA from 2005 to 2009 and the questions we're going to ask from an empirical perspective, using some regression modeling is whether historical facility readmission rates can predict current patient readmission.  So do facilities who have a lower rate of readmission in the prior period have patients who are at lower probability and vice versa, right?  Facilities that have higher rates of readmission—do their patients at an individual level—are they more likely to be readmitted within a 30 day window after the discharge from the index admission?  

The second question we're going to ask is whether historic facility readmission rates affect individual patient costs in the contemporaneous period?  Are patients just somehow—those patients treated at facilities with higher readmission rates—are there episodes of care—these bundled episodes, somehow more expensive systematically?  Is that tied to the historic facility readmission rate?

Then, finally, we're going to take a look at the difference in hospital episode costs—again these are going to be 30 day episodes of care per patients readmitted within 30 days of discharge.  Again the motivation for focusing on these three conditions, just to anchor us back to the first couple slides, is the fact that these are the conditions for which Medicare is going to start targeting the admission rates, starting this October.  

Our setting is the Veterans Health Administration acute care hospitals and I'll discuss the number of hospitals for each condition that we include in our sample.  We are going to focus on  those hospitals that average at least 3 admissions per calendar quarter for a given condition and there's a dual purpose to this: The first one is hospitals that have really low rates of admissions for these conditions—they do look a lot different in terms of the types of hospitals that they are, No. 1 and No. 2 if a hospital only has one or two admissions per period, a single admission would move them from a zero readmission rate to a 50 percent or higher readmission rate for a given period.  So just to avoid those issues of small counts, we're going to stick to those that have at least 3 admissions per quarter in every quarter that we observe from 2005 to 2009.

Again, conceptually, what we're looking at here is the VHA is potentially a good setting to examine readmissions because the global budget system sets up incentives for the large part that cause physicians to deliver as efficient care as possible that's still going to be clinically effective.  Physicians have very little incentive to readmit patients beyond their clinical need in the VA as opposed to out in community hospitals and the costs of care are not confounded by competitive behaviors between VA hospitals necessarily, which is a large driver when you start analyzing costs of care, using Medicare costs and charge ratios and things like that in community hospitals.

So just to create a little bit of a baseline to get a sense of what readmission rates are like in the VA as opposed to the Medicare population, 30 day readmission rates for these conditions, as you can see in the left-hand columns, from the Medicare fee for service population, AMI is 19.8%, CAP is 18.4% and CHF is 24.8%.  The VA rates are slightly lower, this could be partially due to the fact that as some people have suggested in the literature, the VA provides more coordinated care, has better primary care followups and those sorts of issues that might reduce their rates of readmission.  The other possibility is that for those familiar with the VA, we know that dual use is an issue.  So we may not—with these data be able to observe all readmissions.  Some patients may actually be getting readmitted to community hospitals.

Again, as time goes on, we're going to be able to address this in later research when we start making the Medicare claims to veterans, but for the time being, we're using just strictly the VA data.

Todd Wagner:  Can I ask a question: How you're defining readmission because in VA, unlike Medicare, one can easily transfer between settings.  One can go from the medical-surgical, so you can have a surgical, you can then move into your surgical ICU, you could then get just transferred to the SNF.

Jason Hockenberry:  Right.  So we're looking at being—and I'll talk about this a little bit when we get to the models, but now's as good a time as any.  We're looking at those that are discharged, so the non transfer readmissions.

Todd Wagner:  Okay.  So you get discharged home—

Jason Hockenberry:  Discharged to the community—yes.

Todd Wagner:  Right.  Okay.  And then you get readmitted.  Okay.  Thank you.

Jason Hockenberry:  And our 30 day window, to be clear, Todd, is—so the clock starts ticking on the day of discharge and if you're readmitted—

Jim Burgess:  I wasn't going to chime in till the end, but on that point, too, I would also chime in just for other people that are trying to do this and actually I'm involved in at least one other attempt to try to sort of define these readmission things that there are some additional—as Todd's mentioning—challenges in the VA about how we do things around things like substance use clinics and [domiciliaries] and all sorts of other things that are special [ops].  So the measure—as Jason says—the measure that we do here, we're trying to get some consistency with the private sector, but as anybody who's looked at this data knows, there are a lot of decisions one has to make to try to make this work and different people doing it slightly different ways that find slightly different answers.

Jason Hockenberry:  Yeah.  Again, Todd, and for the rest of the audience, specifically, we're going to look at—index admissions are going to be defined as those admissions—and I'm going to move to the next slide because we're going to talk about this here, they're defined as index admissions are admissions that are not preceded by an admission to an acute care facility in the previous 30 days.  Those who died, or transferred or were admitted for less than six hours are excluded from the analysis and we are using all cause readmission because currently the final role of Medicare hasn't teased out the idea of preventable—or potentially preventable readmission—and because we're sort of doing this, using the current policy structure—that's the analysis, the underpinning of the analysis—we're using the Medicare definitions as much as possible within the VA data.

So we have patient level health data on hospital admissions.  We also have in the VA data this extra data on socioeconomic and demographic characteristics, which we will also include for risk adjustment purposes and our cost data comes from the VHA decision support system, the DSS costing data.  Jim and I had a long conversation at the beginning of this project in terms of when we could actually start to rely on that being well costed and that's why we're looking at 2005 forward to 2009 and our subsequent analysis later is going to bring in more recent data because that seems to be the most reliable in terms of the costed data.  Todd, do we have any additional questions before I move into the preliminary data?

Todd Wagner:  I don't see any yet.

Jason Hockenberry:  Okay.  Great.

Todd Wagner:  Ah, one sec.  Which data did you use to calculate readmission rates?  I guess one question there would be: You're using the PTS is my guess.

Jason Hockenberry:  Right.

Todd Wagner:  But one could also look at utilization, using the DSS impact file—inpatients have those largely agree, but I'm guessing they just want to know if you're using the PTS.

Jason Hockenberry:  We're using the PTS and then the DSS costs are merged in.

Todd Wagner:  Okay.  So you're not using, for example, the [Vasquith] data?

Jason Hockenberry:  I'd have to go back and double-check that decision was made quite a bit of time ago, but I don't think so.  Jim, do you remember when we had that original discussion?

Jim Burgess:  Not exactly.  If people want to get back to us on that particular question, we could certainly work it out.   

Jason Hockenberry:  Yeah.  But that decision was made quite a bit ago.  Thanks for that, Todd.  Okay.  So the summary characteristics for those interested in seeing what the patients look like, the number of patients for AMI is quite a bit lower than CAP and CHF and part of that is just due to the nature of the VA, a lot of AMI patients who would be experiencing very acute symptoms—if they lived in an area where the VA did not have an emergency room or didn't necessarily have a cath lab, they may be going directly to a community hospital, if they were coming via an ambulance or whatever the case may be.  So that's just an artifact of the VA system versus community hospitals.  That's why the number of facilities that meet that criterion of at least 3 admissions per quarter is much lower for AMI as opposed to CAP and CHF.

Total episode cost for AMI, as you will see, too, they are quite a bit larger because of the rather intensive nature of treating AMI initially.  We're only looking—to be clear—at hospitalization costs, we are not considering inpatient costs in between the index and the readmission, we're not considering any other costs, these are strictly inpatient costs and the way we're defining this is there's the initial index admission and then any admissions that occur within a 30 day window subsequent to the discharge.

Some people have asked us previously about whether—for lack of a better term—we're daisy-chaining this—whether there's the index admission, the first readmission, then a second, then a third, within the 30 day window that is actually very rare even in these data, as you might imagine.  So we're just looking at the first three admissions.

The index hospitalization cost, as you can see is obviously lower right because it's not the episode cost and the estimates for CHF and community acquired pneumonia of the costs from the DSS data are very similar to what we've seen published in the academic literature and also industry literature related to particularly the cost of congestive heart failure admission.  So if we're comparing this to Medicare patients being treated in the community, the costs look similar to what we've been able to find, using publicly available sources.

The readmission hospitalization cost that looks really low when you first look at it, but when we calculated the readmission costs, we're counting the number of index admissions as the denominator and so there are a lot of people who were not readmitted, so their readmission costs would be zero.  Right?  Conditional upon being readmitted, as you're going to see later in the presentation, those readmission costs, once you are readmitted, are similar, if not higher for the readmission visit.  Readmission rates, as you can see, are similar to those that we described in an earlier slide and just to give you a sense of what the index visit length of stay looks like for these various patients, it's somewhere between 5 1/3 and almost 6 days, depending on which condition we're looking at.  From a demographic standpoint—

Todd Wagner:  Sorry, Jason.

Jason Hockenberry:  Yeah.  Go ahead, Todd.

Todd Wagner:  Can I go back one slide?

Jason Hockenberry:  Sure.

Todd Wagner:  Can you speak a little bit about how you define the population because I think this will come into play later.  I know that there is query groups, especially with CHF who have been looking at this a lot, so do you say that this person, for example, when you say that this is their first CHF surgical admission, do you go back in time and make sure that they haven't had anything like in a 6 month or 1 year window prior?

Jason Hockenberry:  No.  These aren't strictly surgical admissions for CHF first off.

Todd Wagner:  Sorry.

Jason Hockenberry:  Second off we look at an admission to a VHA facility and then we look back 30 days, if we don't see an admission, then that's considered an index.  So some of these patients can show up in the data multiple times, with new index visits.  

Jim Burgess:  But, Todd, again the issue there is what we're trying to do is not have things both be an index admission and a readmission, so that's why the 30 days insures that you don't have that.

Todd Wagner:  Yeah.  I was just—in my head, I was just trying to make sure I understood that.  Thank you. 

Jason Hockenberry:  That's good because when I present this in person I get a lot of questions related to that, so I'm sure some of our audience is wondering and the other reason for defining it that way, just from a policy analysis standpoint, that's how Medicare is going to define this.  So that's how we're defining it and so looking at demographic characteristics—as anyone who is familiar with the VA, knows we have very low rates of female admissions for these because the VA largely serves a male population.  So we'll have to keep that in mind as we try and extrapolate conclusions for non VA hospitals from this research.  

Interestingly, even though we didn't set a minimum age to be in this data, the profile in terms of age looks similar to the Medicare population, maybe a year or two younger, when you compare it to other people's research, who have just used the Medicare files to look at those Medicare beneficiaries.  

Then there are these other characteristics here that generally look like what we know about the rest of the population of Medicare—obviously we don't have marital status in a lot of these analyses, but we usually do have race.

As I said, we set out to look at three different things, the first model was to see if historical readmission rate predicted current patient readmission.  So in this model, the outcome variable R is an indicator equal to 1, if the patient was readmitted within 30 days of the discharge from the index admission.  Small r is the condition-specific readmission rate from the previous quarter.  iLOS—not like iPad or whatever, this is the index length of stay, so there's some literature out there that suggests that a longer length of stay for an index visit is indicative of more severe underlying health and would predict readmission and we're going to show it actually does.  So we've included that in the model and then X is the vector of patient characteristics, including their health condition, demographic and socioeconomic characteristics.  For those that are interested in how we deal with co-morbid condition adjustments, we're using the Clona-L 2010 algorithm, which is based on the Charleston and Alex Howe’s Risk Adjustment models and has been updated for differences in epidemiologic trends.

Some people may ask: "Why aren't you using this additional VHA clinical data?"  This would be available in the VA files and again there are some people who have tried to create better risk adjustment models, using more detailed clinical data in predicting the admission and our first response would be those have not largely been successful in showing that that additional clinical information actually improves model fit to any large extent and the second comment is those type of data are not readily available for CMS and in some sense, you can imagine to collect the type of data that the VA has, you would need some sort of national electronic health record or health information technology system and we know that that's not going to happen right away.  So we're just going—I don't want to call it naive, but the basic model that Medicare would have available to them.

Finally, these [C-Js] are hospital fixed effects and we're talking about fixed effects in the way that economists think of them, so for anybody in the audience who's used to talking about this in terms of biostats, it's a little bit more like a random effect in biostatistics.  Jim, you can correct me if I've just totally gotten the language wrong there, but I think that's how the biostaticians think about these.

Okay.  So the results of this model, when we look at the lag facility readmission rate, the point estimate is quite small and these are not statistically significant.  So, again, what we suggest this is telling us is that facility readmission rate in the previous quarter does not predict patient readmission in the current quarter.  I've done some sensitivity analysis of this, looking at contemporary [inaudible] readmission rates, looking at lag year on current year and measuring different time periods, we largely don't find a different effect in terms of the magnitude of the statistical significance for any of these conditions.

The other thing that I should make clear—in the last slide I talked about choosing fixed over random effects, that was largely driven by the results of the [house-in] test which suggested that the random effects models were inconsistent.  However, we have run the random effects models and I can tell you that the qualitative implications are not different than what we have here.  The point estimates of the effects are a little bit larger and start to get marginally significant in like the p value of 8 to 20 range, but when you think about it from a clinical perspective, they're still not big enough to really be telling us anything about a facility predicting readmission.

For those that are curious about—does the fixed effect model leave enough variation in hospitals to actually make meaningful predictions, the within hospital variation is almost as high as the between hospital variation in our data in terms of the readmission rate quarter to quarter.  So in a sense—I was pressed a couple weeks ago about where this variation is coming from.  Some of it's coming from seasonal variation in readmissions and in admissions and some of it may be coming from initiatives at the VA enacted in the mid 2000s like the fixed initiative and some of these other things that were designed to improve quality and may have had differential effects on readmission rates, but we'd need to spend some time in the future, investigating some of where that variation is coming from, admittedly.

So the next question we talked about was episode cost modeling and whether or not facility readmission rates had an effect on individual patient episode costs?  Again, we're not going to just be talking about the index costs, we're now bundling any hospitalizations that are within 30 days of one another together in terms of calculating these costs.  

Again, the point in estimating this model is to assess whether facilities with higher readmission rates in recent periods are more costly across all of their patients.  So what we do is we take the log of episode costs, and, again, for those that are interested in [econometric] modeling of healthcare costs, we don't have people who are non zero, right?  Because they have an admission, so we don't have the excess zeros problem that you see in other models of medical care utilization.  However, these data are skewed, there are some people that are very costly in the data and so we're taking a log of that to smooth it out a little bit of the episode cost and we're basically running the same model as the previous model, with the exception of the term beta 2 e length of stay, we're looking at the total episode length of stay and our reason for this is just potential differences in patterns of how long physicians or hospitals may be keeping people as inpatients during the episode and so what we want as economists is the marginal cost over and above sort of just being in the hospital, attributable to this readmission rate, and so that's why we're adjusting for each day spent in the hospital during the episode.  Again, this includes the hospital fixed effects and the individual patient characteristics and demographics.

Again, the coefficient on the hospital readmission rate from the previous period is rather small and not statistically significant.  Remember cost for index visits is somewhere in the 10- to $15,000 range as well as the episode costs are pretty large, going back to that summary statistic slide.  So what do we have here: negative $12.00 to $31.00 is a really, really small effect.  The index hospitalization length of stay, so an increase of a day length of stay—and this should be episode not index on this slide—apologies.  An additional day is about $2800 for AMI, all the way down to $2453 for CHF in the bottom left corner of this slide and those are statistically significant effects.  One of the questions again I got pressed on a couple weeks is if we took out the episode length of stay and just looked at whether hospital readmission rates were different in terms of the point estimate of coefficient and it doesn't really impact the hospital readmission rate effect on costs.

So then the third question and to us this is the one that we would argue is probably the most policy important that the VA can inform some of the national policy is the difference in the average risk adjusted bundle hospital episode cost for patients that are actually readmitted, so that last model did not include an indicator for whether the patient we were looking at was they themselves readmitted within 30 days of their index visit in the current period and so we defined the covariates all the same way with the exception of the term following beta4 is that indicator of whether or not the patient was readmitted.  So the coefficient that we're going to be interested in in this model is that of beta4.  When a patient is readmitted, how much does that increase their episode cost of care?

So what we have is again very small point estimates of the lag hospital readmission rate, but the beta4 indicator, when we transform the variable back to dollars, we can see that it is large and statistically significant, so patients that come in and are readmitted, it appears as if their readmission costs almost, if not as much as the index admission and these are effects of the conditional mean and we'll talk about the distribution of costs in a couple of minutes when we get to our quantile regression modeling and again the episode hospitalization length of stay an additional day raises the cost somewhere between $2400 and $2800.  Todd, at that point, maybe it's a good time to stop and ask a couple of questions, if there are some.

Todd Wagner:  Yeah.  We actually have one that's going to take you back to my last question: "When you say there's not an admission 30 days prior, do you mean that there's not a same cohort index admission?"

Jason Hockenberry:  So we're looking at the individual patient and so they would not have had an admission in the 30 days trailing.

Todd Wagner:  Okay.

Jason Hockenberry:  So they had no hospitalization in the 30 days leading up to the hospitalization that we're counting to get them into the cohort.

Todd Wagner:  For VHA is their readmission rate calculated based on whether the patient was readmitted to the same facility or readmitted to any facility within VHA?

Jason Hockenberry:  Any facility within VHA that was an acute care hospital, so we're not counting SNF admission.  Right?  Again that goes back to the issue that we talked about earlier and Jim chimed in on—you may have somebody who gets discharged and there's a small number of people, there's this weird little one or two day lag and then we see them show up in the SNF care or something like this.  These are people who are being readmitted back to a VHA hospital.

Todd Wagner:  This does not include emergency department admission that did not result in an overnight stay?

Jason Hockenberry:  That's right.  Actually it doesn't include those who might have been admitted and only stayed for four hours.  One of the things we have in the VA is that sort of weird what gets defined as an admission.  So we cut anybody six hours or less, so they were not readmitted, so they could have been parked in observation or something like that and we would not count them as readmitted.  Clear?

Todd Wagner:  Sounds great.  There's a question about this last slide, which is when you look at the $2700 versus the $12,00 and if you think about a clinician's choice about whether to keep the patient in the hospital longer versus discharging them with the potential chance of getting a readmission, that you can do the math sort of ballpark it at that $2700 if you knew that your readmissions were less than 25 percent, it's beneficial to get them out earlier.  

Jason Hockenberry:  Right.  So that's part of what I was saying at the very beginning and I didn't want to spend too much time because we do have limited time, but it's a great point to talk about this.  One of the things we're doing as sort of a policy group nationally is we're having this discussion about readmission rates being too high and I guess I say you put those in quotes and you say "too high" because there is sort of the concept that you could discharge everybody a half-day earlier and save some money and with some increased probability more of them are going to show back up for readmission, but that may actually be the cost saving strategy, whereas other facilities may decide, well, we're going to keep everybody a half-day longer and that will reduce the likelihood of readmission.

We'd be attributing a lot of coordination even at the hospital level to implementing such a strategy.  As we know, physicians are making these decisions day by day as patients conditions change during their inpatient admission, whether they're ready to be discharged or not, but there could be this idea of some physicians may discharge earlier full well knowing that they're potentially raising the probability some small percent that that patient will wind up coming back for readmission, but if a large number of their patients don't show up, then they may have saved money.

Todd Wagner:  I guess it's two big hammers and one could easily come up with an alternative intervention, for example, the whole [Project Red]—trying to be cheaper than either of those options and better.  It doesn't have to be either/or.

Jason Hockenberry:  That's right.  The other thing—some people ask and they say, "Well, then I should just keep my patients a little longer," and I need you to think back to the first model—remember staying in the hospital a little bit longer during the index stay actually raises your probability of readmission.  You could think about well, that may be due to exposure to infection or whatever, but it's also the fact that those who are staying a little longer just have underlying worse disease that we are not able to observe even with all this adjustment strategy.  So that's not an option necessarily to just start keeping people a little bit longer.

Jim Burgess:  I would just add what you really would want to do is have some kind of a day model where you're actually modeling—because the other thing that happens, of course, is the kind of things that you're doing with the patient day by day through a stay varies a lot, a lot of things happen early in a stay and then days further out are more rehabilitation oriented and so actually again this is a direction if people really wanted to develop some models that one probably could do in the VA with detailed electronic health records, you'd really want to sort of be building some kind of a day-by-day model and be assessing what the risks of days are.  Again, it's really important to keep emphasizing we're trying to make this a study that compares to Medicare and that always makes us make choices that somebody doing a pure VA study might not do.

Jason Hockenberry:  Thanks for that, Jim.  So the next thing we're looking at is the effects of being readmitted, so this last slide shows that at the conditional mean that second row—actually being readmitted a patient is quite [inaudible] means, but there might be some people at the low end of the cost distribution that are not costly and those at the other end of the distribution that could be quite costly.

One of the concerns one might have about penalizing for readmission is that those that maybe serve a disproportionately sicker part of the population that is sicker in ways that even our risk adjustment model can account for may also have patients that are just much more expensive when they're readmitted as well.

So we're going to go to some quantile regression models and this can sort of start to pick up obscure tail effects that we might find and we're going to look at the impact of readmission on episode costs at the .10th, .25th., .50th, .75th and the .90th percentile and one of the things I want to point out here is the models we are going to show you today, the results of the models we're going to show you, do not have the hospital fix effect in those models, while it is theoretically feasible to have those in there, Roger Tanker who is at, I believe, the University of Illinois, is the guy who has been writing about quantile regression for decades, he notes in his work that using longitudinal data to do quantile regression—these types of models represent what is called a pure location shift for the distribution.  So in order to get meaningful results you have to have a rather large number of observations within a given unit, so within a given quantile to have these be useful and credible for policy analysis purposes.  So, for us, this means we would actually have to further constrain the number of facilities to those with higher patient censuses.  If some small VA hospital only has two patients down at the fifth percentile, that's not going to be good to estimate these models essentially to create the illustration there and so rather than constrain these, we are basically just going to remove the hospital fix effects from these models for right now and later work will deal with trying to get the hospital fix effects in and the tension between that and large enough sample sizes.

So, for AMI, what you can see here in the first column is that's the effect—that's the increase in the number of dollars for a patient in the 10th percentile of cost who is readmitted.  So their index admission was for AMI and they come back within 30 days.  At the 10th percentile, it's $3369, and so in some sense that looks like what I think—the RWJ legal note and some of the people have written about this in policy journals are worried about is—hospitals could have these patients who are coming back, their condition is a little exacerbated and they're admitting them and they really only cost $3300 and the DRG is reimbursing $10000 or $12000 whatever the case may be and so this may be profitable for hospitals and creates bad incentives, but as we move out towards the 50th, 75th and then the 90th quantile, you can see that effect becomes an order of magnitude larger.  So in the 90th percentile a readmitted patient costs almost $25000 in the case of AMI.

The other thing that's interesting is when we go to quantile regression analysis, you can see that increasing readmission rates actually slightly reduces cost in the lower quantiles, but again those effects from a clinical standpoint or an economic standpoint are not really economically meaningful, they're quite small relative to the overall costs in these patients.

Again, you can see this for community acquired pneumonia, there is a similar pattern and the magnitude is very similar.  As you move out to the 75th percentile, these patients start to look like—their readmissions cost as much as an index admission and at the 90th percentile, we're talking an order of magnitude higher than those at the 10th percentile.

Finally, for CHF, we have a similar result and I'm going to give you a final slide that I think speaks volumes in terms of pictures.  So AMI is in blue, CHF is in red and CAP is in green and these are the quantile sets plotted against the actual cost of being readmitted, which is on the left axis.  So you can see this grows as we get further out into the cost distribution and this is based on the quantiles, the episode cost distribution.  So overall patients who are readmitted do cost a little more within each quantile than those that aren't, but that difference gets much larger out on the right-hand side in the higher quantiles.  

So basically to summarize our findings so that we have enough time for questions and discussion, recent hospital readmission rate again does not appear to predict individual patient readmission, that was the first model, sort of the take-home from there.  The second model take home is that risk adjusted episode costs do not appear to be impacted by hospitals' readmission rates and hospital episode costs—the third point, are higher for readmitted patients and this increased cost can be substantial as we've shown in these quantile regressions.

On the first two points, just to anticipate some questions that have come up in previous presentations of this work, of course, one can always argue that our prediction model is [inaudible] specified from econometric or biospecific standpoint and we actually would tend to agree with that concern.  We think there are a lot of unobservable factors about patients that drive readmissions that no model that we've seen is good at capturing, but the models that we're estimating here are analogous to those that are going to serve for the basis of the current readmission policies that are being discussed and implemented.  

On the third point, again, this is not a shocking result, right?  Patients come back to the hospital, they cost more money.  What may be shocking is the fact that a substantial portion, at least a quarter of these people—their readmission costs as much and maybe up to somewhere between 55 and 85 percent more than the index admission.  So if we're going to start penalizing for readmission based on the idea that hospitals are regularly readmitting low cost patients because they can make a high margin on them, that may be a little bit misguided.

There's the conclusions: Penalizing hospitals based on rates may be misguided, as I just said.  So this discussion of recent work that's come out, the July 24th issue of Archives of Internal Medicine looked at a couple of programs that were designed to try to reduce readmissions within particular settings and they actually found that the cost per patient of reducing readmissions is greater than the cost of the readmission itself and part of this may be because we are not good—largely speaking—as a healthcare delivery system, it's identifying those things that predict readmission with certainty, we basically have to treat these programs like vaccination, where you've got to treat everybody to actually reduce the rate and not everyone needs to be treated and so we would argue that before going to these penalties—I shouldn't attribute this to my coauthors, but I would argue before going to these penalties and penalizing hospitals that are potentially already operating on a thin margin and serve a disproportionately poorer population, we might think about trying to develop some models that can identify those who are more likely to be readmitted and try to intervene in a more targeted way, if we're actually going to try to use this to save costs.

Jim Burgess:  Can I chime in there, Jason?

Jason Hockenberry:  Yup.  Go right ahead.

Jim Burgess:  I certainly agree with everything you said and the thing that I've been trying to look at a bit and interestingly since Todd already mentioned Project Red that there's a lot of debate within the Project Red team on this, but I think one of the things that you can start to look at there—particular things like mental health, comorbidities and things like that and that's what I've been trying to dive in on.  Go and look for ways to segment out the population.

Interestingly, the Project Red people are not that interested, even though their own project work that I worked with them on shows that that's a big issues, where there's like three times the risk factors or three times the odds ratios, however you want to do it for mental health patients being readmitted that still they don't want to focus on that, at least not right now.  So it's an interesting problem and I think we're going to keep looking at this and that's where this work is going.  I think we're trying to contribute here a very specific set of results that I hope made sense to everybody.  

Todd Wagner:  We have a few questions that have come in.  One of the ones is you've really focused on this 30 day readmission, but one could argue—and the reason for doing that is because of Medicare's policies, but have you thought about longer periods, a year, for example?  How that might change the policy scenario?

Jason Hockenberry:  That may actually tilt those quantile regressions towards even more concern—I would actually be concerned that if we're going to look at readmission within 30 days, if we start including things that are more [distal] from the index admission, and attributing that to hospital quality and penalizing based on that, I would think it gets even more dicey.  I've been asked that question and other people have asked the exact opposite, so what if we shrink it down to 7, 8, 9, 10 days when it's conceptually probably a little easier to argue that part of the reason of being readmitted within those shorter time frames is due to the quality of care given [inaudible] admission.  Again, Todd, my comment on all of this is: We're not good at predicting readmission as health services researchers or clinical researchers.  There are not models that do this well from what I've seen.

Todd Wagner:  Right.  

Jason Hockenberry:  So if we're not good at predicting it within 30 days, how much better are we going to be within 60 or 90?

Todd Wagner:  You probably have already talked to the folks at the query centers that relate to these two areas.  I think of the IHD and the CHF queries and GHF in particular, Paul [Hayden] writes here and Paul also has been looking a lot at CHF readmissions, you might want to just reach out to him.  I think his perspective, when I heard him speak last was the same thing is that we see these high readmission rates, but we don't know what percentage is preventable and it's probably a lot smaller than we think it is.

Jim Burgess:  We haven't really reached out to those queries, but we certainly could and communicate with them and see how this is dovetailing with things they're finding.  I know I haven't, have you or anybody else—or the Iowa part of the team done that?

Jason Hockenberry:  No.  Well, I think Peter Kaboli may have originally, going back two or three years ago when this all first started, but I don't think recently as this has developed and evolved as a project, we've looked at that and some of the other things we're trying to do, Todd, as a group, is look at things that might actually predict—reduce readmission rates, so the non physician [inaudible]—so nurse practitioners and PAs being present in the hospital with better discharges that result in lower readmission and these kind of things and that work is just getting started, but we are sort of going after maybe facility level characteristics that might predict differences in readmission rates, but that's really an inconsistency in our group, but I will definitely talk to those folks and I'll get their contact information from you.

Todd Wagner:  Yeah.  I'm happy to help with that.  I guess I'm still—your first model when you were trying to figure out are these things predictable, you're saying that they're largely not, using a t-1 period.  I guess I'm struggling with the identification of this, that's just me as a health economist.  I guess what I'd like to say is that you run the risk of having an error where you say there's no effect, when there's actually an effect there.  You just have this unobserved heterogeneity that's really clouding the picture here.

Jason Hockenberry:  Right.  So, again, from an econometric standpoint, is this model mis-specified?  Yes.  Is this the model that at the very base level is going to be used for policymaking?  Yes.  So I guess what I'm doing—Jim always talks about how I like to set up my straw men and punch them in the chest and that's kind of what we're doing here is saying, "Look, this is a model that we're running in a system where the other perverse incentives that we think are out in the community hospitals are largely absent."

Now, this facility level quality lag one period predict quality in terms of treating individual patients in the current period and we just aren't able to show it.  We have done some models where instead of looking at individual patients, we looked at lag facility rates on current facility rates and you find that there is statistically significant impacts of lag readmission current rates, so there's probably just some serial correlation to this work in hospitals, but the point estimates are so small that that's not what's driving it and people have come back and said to me, "Well, you don't have enough variation."  You'd actually be very surprised and I think most people would be if they grabbed a bunch of hospitals and whatever data they had and they looked at like their quarter-to-quarter or year-to-year variation and readmission for a given condition.  There is substantial variation within hospitals.

Jim Burgess:  See that's the same thing I would say, Todd.  My expectation—if the kind of story you're trying to tell is the story that is fitting the data, then we actually should see our point estimates jump a lot around with seeing really high standard errors on the [inaudible] hospital readmission rates parameter.  So the fact that we're just really not getting any effects at all—I don't think it's really purely a signal to noise ratio kind of problem.  Now that doesn't say that there isn't some other mechanism and that's perfectly possible, but I don't think it's that.  

Jason Hockenberry:  Todd, your point is a good one and we are trying to figure out if there are facility level characteristics that differentiate these hospitals in terms of their readmission rate.

Jim Burgess:  That's actually the strength of the underlying study here that Peter Kaboli and Joe Restuccia are the PIs for is that we really collected a massive amount of inpatient medicine facility level data from which to do that kind of study and this in some sense is a pre study now that we're just getting that other data.  So we're actually trying to get this result out and then kind of move on to things where we can look at some of the things that are actually going on within the inpatient medicine service.  We actually have quite a lot of information on that.

Todd Wagner:  Just because you're involved with Peter, one of the questions then because Medicare often does things differentially for rural versus urban, I would think that somehow sort of doing some additional analysis, looking at whether it's distance or the urban versus the rural hospital, would be useful here too, because I think that patients who are living far away: One, have a disproportionate use of Medicare hospitals, so you're sort of missing them.

Jason Hockenberry:  So Peter and some of that rural health group have done some work, looking at whether rural or urban patients are more likely to be readmitted and actually mapping some distances from residents to hospitals.  I wish I could remember some of the results off the top of my head.  I know that rural veterans were sort of different in their propensity to being readmitted, but I couldn't remember if it was lower or higher and you could think it goes either way because they don't have access to care in rural areas, maybe they're more likely to come back, but at the same time maybe physicians are keeping them a little longer to insure that they don't come back because they know they travel a long distance and that was a discussion at the beginning of this project that we also had about rural versus urban differences.  If you do a quick pub-med search on Kaboli, you'll find some of this stuff on there, I just can't remember off the top of my head.

Jim Burgess:  I've done most of my work on that question on more of the outpatient side, but remember that the general rule of thumb that you find in pretty much all the studies that anybody does is that veterans kind of travel roughly twice as far to their facilities as people do in the private sector.  So the time-price cost sort of results in a kind of a doubling, so somebody out in rural Montana might travel three hours to the VA, but somebody out in rural Montana who is not a veteran and just is considering traveling for services might travel an hour and a half and that's the kind of—interestingly, that doubling sort of even happens at 10 minutes versus 20 minutes [inaudible].  I think much more research on those sorts of things again I think would be productive both because then it allows you to use some of the recent advances in residual inclusion models and other things, using instrumental variables to do interesting things as well, as well as just the fact that VA fundamentally is this system that's set up in particular places where veterans have to travel to get to it.

Todd Wagner:  Right.  So we have two more questions: Are you going to study the effectiveness or the cost of the coordinated care home telehealth and Project at Home Intervention?

Jason Hockenberry:  We don't have intention of doing that right now because this grant has been largely to focus on hospital care, but one of the things that is not in this model in terms of the episode cost is that sort of raises the idea that the VA also spends money on outpatient intervention to sort of reduce the readmission rate that we're not capturing in the episode cost.  So we would say this is a lower bound of the effect.

Todd Wagner:  Then, finally, "I'm not familiar with the conditions you're examining, but are there any performance measures related to these conditions that might be helpful to include?  For example, continuity of care measures and did the facility get the patients to engage in a followup appointment related to the condition for which they'd been hospitalized and did that reduce readmissions rate?  Was it putting a Band-aid on the problem versus actually creating a patient center planned to get the patient to engage in longer term for those conditions?"

Jason Hockenberry:  Right.  That's part of the reason we were looking at this in VA is there is largely a spot that VA does a little bit better job than average—if not much better job than average of getting people plugged into their primary care providers and delivering that coordinated care.  Jim is it Amy Rosen who's working on this?

Jim Burgess:  Yes.  That's what I was going to say and I think some of Amy's team is probably listening, but yes.  Amy's looking at that a little bit, they're actually some other people in VA who are and then outside in the private sector.  There's a bit of a debate going on within CMS about how to proceed on this and I'm actually a consultant for CMS on some of the questions on how to proceed around these kinds of questions from the CMS perspective and it's a little muddled right now.  So there's actually a lot of muddling here that's going on on the policy—

Todd Wagner:  It's an election year, Jim.

Jim Burgess:  Excuse me.

Todd Wagner:  It's an election year.

Jim Burgess:  Yeah.  But it's really the technical stuff that's the muddling—it's not the election issue—maybe it is from people inside CMS, I view them as technical problems and issues regarding how [inaudible] is approaching measures that CMS has been proposing and things like that.  I think these are really complicated areas that I hope—I know we have roughly a hundred or so—maybe a little less than a hundred attendees, but there's really plenty of space in this area for lots and lots of research.  So I do hope that more people are looking at these questions and all of them are interesting to me.

Jason Hockenberry:  Todd, the one other thing that I would say—not from our study, but I've done a large amount of reading on this is when studies focus on a very narrow proportion of the population, those who somehow they've identified as potentially being higher risk for readmission and they do an intensive intervention, they do reduce readmission and the ROI in a sense does turn out to be positive, but then when studies sort of expand that to really what I call the vaccination level, because we don't really know how to target this, it turns out that the cost per patient winds up being higher than the savings that you get from reducing the readmissions.

Todd Wagner:  You included the Charlson comorbidity index in this—

Jason Hockenberry:  A version of that—the [Quan]—

Todd Wagner:  Okay.  I was going to say one of our colleagues from the CDC is listening has a comment about the number of procedures they found is a better predictor of healthcare associated infections in the Medicare population, but my memory is you just didn't show those coefficients, but you had sort of those in your model.

Jason Hockenberry:  Right.  Those are in the model and because of having Power Point—slides can become unruly, we've muted those.  Those are in there and those demographic characteristics of the patients are in there as well.

Todd Wagner:  I think it's just fabulous that people from outside VA are listening to this, so what I'll probably do for you, Jason, is send you this fellow's name.

Jason Hockenberry:  Tell him if he faces south and he looks at Rollins, if he waves, he might see me waving out my window.

Todd Wagner:  I was going to say it's even better because he's in your city, so it would be great to make sure that you guys are connected down there, but thank you so much.  We're out of time, so I just wanted to thank both Jim and especially Jason for just a wonderful presentation.  This is fascinating work and very important for VA, so thanks so much.

**[End of Recording]**