**Transcript of Cyberseminar**

**HERC Health Economics Seminar**

**Hospital Readmission: A Measure of Hospital Quality?**

**Presenter: Peter J. Kaboli, MD, MS**

**February 20, 2013**

**Hospital Readmission: A Measure of Hospital Quality?**

**February 20, 2013**

Moderator:It looks like we are right at the top of the hour. I would like take this opportunity to introduce Peter Kaboli. He will be presenting today’s session. Peter is an investigator and hospitalist at the Iowa City VA Medical Center and professor of Internal Medicine, Division of General Internal Medicine at the University of Iowa Carver College of Medicine. With that Peter, I would like to turn things over to you.

Peter:Okay well, thank you Heidi and thanks to everyone who is calling in today. I’m here at the Iowa City VA waiting for a snow storm to come towards us. So, I’m glad we’re not all traveling to a meeting for this. Today I want to cover a number of things related to hospital readmissions. I’m going to go through an outline slide of what we’re going to cover but first I want to say I’ve broken this up into three sections. I’m going to take questions through the moderator at each break. If you have any questions you can type them into the system and they’ll be ready when we have our breaks. I’m going to do about 15 minutes. I’ve got about 18 slides to cover then a break. And then, another 15 slides and a break and then another 12 slides and then we’ll be finished. I want to make sure we’re done before the top of the hour for those of you that need to move on to something else. If anybody wants to stay on and keep asking questions we’ll be happy to keep going.

I will do my best to clarify when I’m stating my opinion about things and when it’s supported by the evidence. I have no conflicts of interest. I am going to go fairly quickly because we have a fair number of things to cover related to this topic as well as to keep you awake. These are the objectives today. I’m going to first take some time to describe some rates of readmissions within the VA. I’m going to talk about some associations with lengths of stay and some factors associated with higher rates. Just to give everybody sort of different backgrounds and sort of the same understanding of what we’re talking about, we’re going to talk about readmissions and whether it’s a quality metric. We're going to have some audience response questions related to this as well. I look forward to that.  I’m going to talk about some strategies that have been proven or are in the literature for improvement and talk about implementation of some of these. And, I’m going to talk about bundled payments – although that doesn’t directly affect the VA I think there are some implications for VA. And lastly some, at least what I see as research opportunities. 

Here’s the first question. I’m going to read it and then Heidi is going to put up a polling. The question is: In the past 14 years hospital length of stay has steadily declined in the VA. Over that same time, what happened to 30 day hospital readmission rates? 1) They increased, 2) they decreased, or 3) they remained unchanged?

Moderator:Responses are coming in. We’ll give just a few more seconds and then I’ll show the results here. 

Peter:Okay, so the results are there. A little over half think they increased and then split between the others...between decreased and unchanged. It looks like about 20% of people maybe read the paper that we just published last month. So, this is timely. Okay, so let’s move on.

Here’s the paper that just came out...that came out actually in December in internal medicine. I’m going to briefly describe this work we did in the VA and the results that have shown actually that readmission rates have decreased during this time. You can see we looked at a 14 year period, about 4.3 million admissions to the VA. We had some exclusion criteria included anybody who died in the hospital – they can’t be readmitted. Then the top diagnosis down there at the bottom, heart failure, which is the most common but it only represents about 5%. So, when you see papers and other people talk about diagnosis specific hospital readmission. We’re still talking relatively small proportions because in medical patients we take care of a very heterogeneous group of patients. 

So, this is just a graphic and then I’m going to show the actual numbers of reductions. I’d like to say, I think those of you who have been in the VA for a while have seen really amazing improvements in efficiency in VA’s hospitals. That improvement in efficiency has paralleled the private sector. We sort of have historically lagged behind the private sector in efficiency but we’ve really closed the gap recently.

Here are the actually numbers. If you look at...these are adjusted analysis looking at length of stay, length of stay in days. We broke these up into two year periods because it will be a little easier to look at the numbers instead of every year at a time. If you just look at the far left under all medical diagnoses the adjusted length of stay went from about 5 1⁄2 days down to just under four days. So, reduction of about 1 1⁄2 days. If you go over to AMI – acute myocardial infarction – had the largest reduction from a little over six to a little under four days. 

Here’s what happens with readmission rates. This is just a graphic to see...there’s a little bit of variation in terms of some going up and down a little bit, but overall the blue line – hopefully not too many color blind people here, and I apologize to those who are – the blue line is the all diagnosis. There’s this downward trend. So, let’s look at the real numbers. These are adjusted readmission rates. Again, the far left column is all medical diagnosis. You see the readmission in the VA went from about 15.5% in the VA to about 14% - so almost a 3% reduction there. And, across all diagnosis the biggest reductions were for COPD and acute myocardial infarction, with the least reduction for community acquired pneumonia. 

One side note: Any of you that are familiar with observation status – I might come back to that. I want to make a point that this includes all observation admissions. The reason is the way observation is used in the VA, there’s a considerable amount of variation. So, those admissions were not excluded. Some non-VA studies exclude observation stays, which I personally think it is incorrect and appropriate that they should be included. That’ll probably come up later when we talk about implications and the potential for gaining, especially in the private sector.

I have to do a little bit of advanced math here. I put this picture up because I saw this last summer when we were riding our bikes across the state of Iowa – a big bike ride across Iowa. It really struck me that really, do we need to do that kind of math for people these days? But, I guess after you've been riding all day and drinking beer, you probably need that completed for you. I’m going to do a little bit of math there that’s beyond my statistical skills. But fortunately, smarter people than me were on this paper. 

The question is then...this paper took about five revisions and about 2 1⁄2 years to get accepted. The reason was some of these statistical reviewers didn’t believe the results that we have. They didn’t believe that by reducing length of stay we weren’t somehow missing an increase in readmissions. So, after multiple, multiple ways of looking at it I’m going to present two different ways that we looked at this to show there is a tradeoff. It is what I would call a small tradeoff, but it is a tradeoff. 

What this figure shows is that if you take the grey line, which is as if there was no change in length of stay over this period for an individual hospital, their reduction and readmission rates was greater than if you had the blue line, which is a 40% reduction in length of stay. What that basically shows – and I’m going to show it a different way here in a second – is that if you dramatically reduce your length of stay – or more dramatically than other sites – that you're still going to have a reduction in readmissions. It’s just not going to be as great of a reduction. 

The other thing that makes this complicated is that when we talk about reduction in length of stay and the concern of sending people home sicker and quicker – is what oftentimes sited, we’re just sending people home too soon – is that are we talking about the patient that goes home that’s been in the hospital for two days and we send them home in a day and a half or the patient that’s been in the hospital for 12 days and we send them home on day 11? Is that the same thing in terms of reduction of a half a day or a day length of stay? 

Here’s another way of looking at it. A patient discharged from a hospital with a length of stay that was one full day lower than expected – given the year and patient characteristics – was 6% more likely to be readmitted compared to a patient discharged from a hospital with a mean length of stay equal to the expected length of stay for that year and the patient characteristics.

Again, this is just showing that if you are decreasing length of stay beyond what’s expected you're going to have a slight tradeoff. 

Moderator:Peter, I’m sorry. I need to interrupt you for just a second. You're audio is coming in and out a little bit.

Peter:Oh, okay.

Moderator:I just wanted to bring that to your attention.

Peter:Thank you. This headset moves off my head. It’s my first time with a head set. 

The second bullet there says within hospitals each additional day of stay was associated with 3% relative increase in the likelihood of readmission. Again, this has been shown – I’m going to come back to this – that the longer people stay in the hospital the more likely they’re going to be readmitted. This is a function of people being sicker and needing to be in the hospital for longer and needing to come back to the hospital. That’s going to be a repeating theme. 

Reducing length of stay to much below expected will result in a small tradeoff for higher readmission rates. So we did see a tradeoff but it was small. Patients with longer length of stay have higher readmission rates. So duh, these are the people who were sicker and need to return to the hospital more frequently. 

Now we get to ask another question, those of you are... I’ll read the question and it will come up. VA shortened length of stay has become more efficient and at the same time reduced hospital readmission. However, we have improved palliative care services (i.e., the dead people don’t get readmitted), this 30 day mortality has: increased, decreased, or remained unchanged?

Moderator:We’ll give it just a few more seconds. Responses are coming in well. 

Peter:Okay, so about half guessed that mortality has decreased. I can tell you that when we did this analysis I had thought it was going to be relatively unchanged, thinking we probably haven’t done anything to change mortality. But in fact, actually this was an unexpected finding. This was actually not part of the...this is one of the, hopefully, the good things about painful peer review process is that the paper gets better. We had no intention initially of including mortality in the analysis but the reviewers wanted it. So, we added it and this is what we found. So, 30 day mortality after discharge decreased by 25%, and if we go to 90 days it decreased by 18%. We thought this was really important to make sure we weren’t just sending patients home with palliative care services, which is very appropriate, but then having them die and therefore not being readmitted and making it look like we were just doing a better job – which you could argue that is a better job. Or, that we were sending patients home too soon and they were having sudden unexpected deaths at home that were poor measure of quality. So we did this a number of different ways and found similar reduction. Again, this is one of those things we looked at multiple ways. 

We were trying to look at it as also graphically and visually and as many ways as we could to say well, what other kind of associations could there be between readmission and length of stay? To be honest, it was really hard to find. But, this is just one way of looking at the same hospital readmission...two hospitals with the readmission rate of 15% during the study period. Actually, we picked...this is a two year block, but just for illustration...and one hospital had a length of stay of six days and the other had a length of stay of 3.7 days. So again, there’s another way of looking at the same data.

So in summary, unadjusted and adjusted length of stay decreased over this time period. And, in multiple variable analysis we saw greater reductions in length of stay resulted in less reduction in 30 day readmission – so again, that small tradeoff. The good thing about this variation is it suggest there are some fixable solutions. When you have this kind of variation there’s something that we could probably improve on. We’re going to talk more about that. And, it’s reassuring to show that increased inpatient efficiency has not resulted in increased hospital readmissions. 

If you want to read the paper that’s great. I think actually what’s more interesting than the paper is the editorial that Eugene Oddone and Morris Weinberger at the Durham VA wrote. I really appreciated this comment because I’ve wanted to say this in papers. They say, “The authors acknowledge that a limitation of the study is that it was conducted in a single health care system (VA). That statement is included in all articles from VA investigators. Perhaps it’s time to embrace the VA as the largest US accountable care organization. Let the VA serve as an example of how to enhance both efficiency (reduced length of stay) and quality (reduced readmissions and mortality).” So, that’s a thank you to Morris and Gene for that statement. 

Okay, so we’re going to try this to see if we can do some questions. Actually, Todd Wagner was going to do it but I don't think he was able to make it. 

Moderator:So, we have Patsy Sinnott here instead.

Peter:Okay Patsy, do we have any questions I can answer at this point?

Patsy:Can you hear me now?

Moderator:We can hear you, yes.

Patsy:No, there are no questions. 

Moderator:No Patsy, we do have questions out here. Does VA readmission rates include readmissions into non-VA hospitals?

Peter:Oh, I figured I should have covered that. Thank you for asking that question. That is a great question. And so, when we started this study, this is...those of you who know we had a moratorium on merging VA with Medicare data. So, we weren’t able at the time to merge the file. It became available towards the end of our revisions and we just weren’t able to add that in at the end. 

I can tell you that other people have looked at that – Al West at the White River Junction has looked at this. There are people that do get readmitted to non-VA hospitals. When you look at Medicare it’s a little bit complicated and I probably can’t go into all the details, but yes we are missing some. When we compare our numbers, and we say this in the paper, we’re probably missing a couple percent that end up at a non-VA hospital under their Medicare benefit as a readmission. That’s something for the future to do. 

The other thing is, the under 65 that are non-Medicare, we don’t have access to right now. So, good question.

Patsy:Did you include fee basis or purchased care?

Peter:We did not include fee basis file in this. That’s another thing we could have done. We didn’t. It certainly would add a little bit. Their experience is ... again, it’s probably not going to add many but it will add some. So again, some of our estimates are going to be off a little bit. 

Patsy:Okay, are you saying VA is better than the private sector such as Coleman study at Denver?

Peter:You know, I think that’s a hard thing to tell. I mean, one of the things that we have as a great advantage in the VA system is both our integrated medical record as well as you know, that we provide comprehensive care within the same system of care. Now, obviously some managed care organizations do that as well. So, I don't think we can say that we’re better. I think that we’re doing things well and the main question for this study was to make sure that we weren’t...there wasn’t a signal yet that there was a significant tradeoff. 

Patsy:And the next question is: Can improvements in healthcare, or more costly care in general, account for the improvements in length of stay and 30 day readmission over 14 years? 

Peter:I don't know if I can answer that. Patsy, can you answer it? 

Patsy:No. 

Peter:Okay. 

Patsy:I can’t... Let me just...

Peter:Try one more, then we’ll go on just so that I don’t get behind. But, that’s a good question. I’m not sure I can answer it in the context of things. 

Patsy:Okay. I don't know Coleman’s paper.  Can improvements in general account...okay. What norms did the VA use, if any, in objective of making stays more efficient by decreasing length of stay?

Peter:I think during this time...two things happened during this time period. One is just that there was a greater focus and those of us who practiced in VA over this time period just know there has been a greater tension put on efficiency, that availably to the office systems redesign. They ran a series of collaborative called FIXX – the flow improvement inpatient initiative. That was during like ‘07-’08. That was sort of a national IHI type collaborative to improve flow through the hospital. I think with that amount of focus during that time period there was sort of an acceleration of improvement and efficiency. So, I think that was part of it, but there was just a lot of other things going on. 

Let me keep going.

Patsy:Okay.

Peter:So, the next...we have another question for the audience. Do you believe that 30-day readmission rate is a measure of healthcare quality? Yes, no, or I’m not sure but I’ll have a more informed opinion at the end of the hour? 

Okay, so it looks like about half feel that yes it’s a measure of quality and then the others said no, or not sure. So, this is one of those questions where there... where there is no right or wrong answer. But, I’m going to give you sort of this is more my opinion. This actually, when I was putting these slides together I just happened to come across a blog that, one of the healthcare blogs, and Ashish Jha, who is in Boston and has worked as a VA person as well, he had sort of similar thoughts on this. I am taking some of this from him and some work that I did. 

So first of all under quality, just as simple definition  that it represents a degree of excellence or some superiority in kind. A metric is something that has a standard measure to capture performance. The great thing about readmissions – if you like them – is that they’re very, very easy to measure. And in fact, actually determining the numerator and denominator are really not that complicated and you don’t get people arguing too adamantly about who should be included or not included. Occasionally you'll get people arguing about whether palliative care should be included. But, if you're more inclusive it’s easier to compare things. 

If you look at quality from the sort of donabedian structure process outcomes model, something like a structural thing such as presence of hospitalists or presents of intensivists in the ICU may be associated with quality. But, that’s really not a readmission. It’s readmission of a structural thing. It’s not really a process thing either. It’s not like aspirin for acute myocardial infarction or other interventions we do that may improve outcomes. So, it’s not a process. Is it an outcome? 

Well, it’s not really a healthcare state. So, death is an outcome. What I was thinking and actually Ashish has said it in is blog is that there’s really no external validity for readmission rates. So, he was sort of doing the same thing I’m trying to say. I don't really think it’s a quality metric, but it may have some value. 

I also wanted to say well is it an error? I sometimes hear people talk about it in the sense that well, you know this is clearly an error. Something bad happened and that’s the reason the patient came back to the hospital. It actually kind of annoys me when I see people cite some of the work with Medicare and say hospital readmission cost $17 billion. Well, they site that number as if we could eliminate all hospital readmissions. That’s just simply not possible. 

So, some work that Erik Thomas and Lora Peterson at the Houston VA, they’ve done a lot of work on errors. So I pulled this up to say well, is it an error? Errors include terms such as mistakes, close calls, near misses, active errors, latent errors. Well, a readmission isn’t an error. Is it an adverse event? Adverse events include things that imply some sort of harm, whether it be some sort of medical or iatrogenic injury. So, what I would argue is that readmission may result from an error or adverse event but it itself is not an error or adverse event. 

Thinking of some recent patients, you know a patient I had recently went home, got the wrong dose of their diuretic, and came back because we gave them actually more than they needed and that gave them some acute kidney injury. I had a patent who became deconditioned in the hospital. We thought he was safe to go home. He had a fall, injured himself, came back, and was readmitted. But I think more frequently as opposed to errors or adverse events readmissions are subsequently due to the process or uncontrollable factors. 

I had a patient I actually admitted multiple times who was homeless and he lived in a van. He chose to live in a van. This was his home because he liked to move between Iowa and Arizona, but he was oxygen dependent. The sad thing was he could only plug in his concentrator when he was driving. That was kind of unfortunate because it’s hard to be just driving around all the time with your oxygen concentrator on. Again, those were uncontrollable factors and he chose to go back there.

If we could say, another polling question: If readmissions are not an adverse event – my opinion – or not a great quality metric, are they preventable? So, assuming they are preventable, what percent of readmissions could we prevent? Oh, and the answers are 10%, 25%, 50%, 60% or 90%.

Okay so, it looks like most people were between 10 and 50% with most people thinking 25%. Well, the right answer is probably somewhere between 10 and 60%. I’m going to show you the evidence that supports that. 

This is one of the first papers that came out. In 1991, I see Lee Goldman was one of the authors of this. This is in Boston. They looked at a few thousand admissions over four months and did detailed chart review. What they came up with at that time, in 1991, was that 9% (or 1% of all readmissions of the index admissions) were preventable. Of the preventable ones, almost 90% occurred within the first ten days. They broke it down into three sort of general groups of system failures, hope that the patient would improve – which I think those who take care of these patients do that frequently. I hope they’re going to get better. We can’t keep them in the hospital forever. Then a third was sub-optimal judgment in evaluation or treatment. This really supports, I think, what a lot of us when we take care of patients see. The preventable readmissions usually happen within the first few days, something happens that we say to ourselves we could have done better. But, that’s still only 10%. 

At the other end of the spectrum, this is a paper that looked at using a software that was developed – and some of you are familiar with the potentially preventable readmission software developed by 3M. What they came up with was about 60% of readmissions were potentially preventable. These were just related readmissions. There’s nothing in administrative data that really can get at the issue of preventability. 

So if you say 10%, 60%...the right answer is probably somewhere in between. I think the best study that really looked at this carefully is Carl Van Walraven in Canada did a systematic review of 34 studies that looked at readmissions and avoidably or preventability. The median proportion - if you want to read the whole paper, it’s very well done – found about 27% were deemed avoidable or preventable. So, I’d say the answer to the question was I think maybe 25% are potentially preventable based on the literature. But, if you say that 25% are potentially preventable no intervention is going to prevent them all. So, I’m going to mention some of these interventions here shortly. 

Okay, another audience response question. I’ll read it and then the cues will come up. Readmissions – again, these are my opinions here – are a poor quality metric, they rarely represent an error, and only 25% are preventable. Can we at least predict who will be readmitted? 1.) No, the models are no better than a coin flip. So these are some prediction models I’m going to show. Yes, but the prediction models aren’t very good...aren’t great – area under the curve of ...625. Or, yes the prediction models are great within AUC of about 9, .95. 

And the winner is...yes. Most people got it right. As you can imagine, anybody that’s done any prediction roles, these models are not great. I’m going to show you two different models that basically came up with the...that’s my opinion there when I say readmission prediction models stink. So, Marta Render at Cincinnati at IPEC, she and her people there, her team developed a model. This is what they had in their model: They gave the things at the top there a point each for age, diagnosis, lab values, length of stay greater than seven days, and then more points for cancer, a prior admission – which has been shown in multiple models – and then more medications. 

The paper we worked on - Omar Hasan is the first author in Boston – looked at six university hospitals and came up almost the same key statistics. The model that he developed, you can see there the type of pay – Medicare, Medicaid, or self-pay – there was some admission for number of Charlson index, conditions, length of stay – longer length of stay was predictive – how many admissions you had in the past year. You can see if you were admitted five times or more in the last year it was a big predictor. Having a primary care provider actually increased your chances of being readmitted, which is surprising to many people. I’m going to come back to why that may be not surprising. Being married actually, having good social support or presumably marriage is some form of social support, was a predictor of readmission. Also, had SF-12 data in this study – so we had functional status, which is not usually included. You add functional status and other information plus administrative data and still couldn’t get an AUC or an key statistic over .65. 

I think this is probably one of the better papers. Devin Kansagara from the Portland VA had this come out about a year and a half ago. He did a systematic review of prediction models for readmissions. If you look at the way he broke it down, there were about 36 total studies or 26 unique models. So if you use just 14... I’m sorry, 14 used just administrative data and would be valuable for hospital comparison. You can see the area under the curve was just slightly better than a coin flip. If you can identify high...to use them to identify high-risk patients during hospital stays, and this is where people, I think, are going with this. They think they can identify high-risk patients and target them. You can move that up a little bit.

If you use them right at discharge, again moves that up a little bit. Only two of the models had functional and social variables and they found they improved discrimination. But, the problem with those variables is that they’re rarely available. 

I’m going to go back to the question about quality. It’s the same question we had before. I want to see how people’s responses have changed. Now, do you believe 30-day readmission rate is a measure of healthcare quality? 1) Yes, 2) No, or I’m still not sure? Well, we are split right across all three – a third and a third and a third of yes, no, and I still need to wait till the end. 

As promised, this is break number two for questions. If some questions have come in, Patsy, you can go ahead and ask and I’ll answer them.

Patsy:Interesting global results, but our VA has a low length of stay and high readmission rate with evidence of decreased patient understanding. How do these other stellar facilities make patient education better?

Peter:So, I’m going to talk about the interventions that have been done and pretty much all of them involved patient education in different forms. Some of them have some more specific things - for example – some specifically talk about the use of teach back and having patients explain back to their providers, to the care providers, what they understand about their care and what they understand about their follow-up. There is a considerable amount of variation. If you're one of those high readmission, low length of stay places or if you're high length of stay, high readmission, use your local data. Look at the trends over time and start looking at some of the resources that are available. I’ll come back to those.

Patsy:The next question about Medicare, Medicaid, and private data. I worry not so much about the levels of estimates but the change over time. Could they be driven by compositional changes in the VA population for example?

Peter:Again, it’s always this question when you don’t have something you don’t know what you're missing. The papers that have combined them, they don’t suggest to me that we’re missing a tremendous amount as far as changes over time. I’ve only looked at it over the last five or six years. And somebody...well, I guess we can’t have a discussion so somebody can’t correct me. But, they can send me an email and correct me. The last I checked, about 75-79% of Veterans have another form of payment – another form of insurance. Most of those, about half of those, are Medicare. A few have Medicaid. About 28% have private insurance. About 10-12% have Tri-Care. 

So, they do have other options. There’s only about...it’s about a quarter that have no other source of payment. So yes, they could go to another hospital. I can tell you from experience, and this is just my hospital n of 1 that patients tend to come back to the hospital they were treated in. If they’re admitted to a non-VA hospital frequently transferred, especially if they don’t have another source of payment. Other hospitals are really good about transferring patients who either they don’t know if they’re going to get...are fee based to pay for it or if the patient doesn’t have another form of payment. Yes, there could be changes over time and always another study to do. 

Patsy:The next one: We talk about hospital readmissions like they are only indicative of inpatient quality. But, conceptually couldn’t they apply much more broadly as an indicator rather than a metric of health system function since so much happens after the patient leaves the hospital?

Peter:Yes and whoever asked that question could probably take it from here and explain all the reasons why. This is really a system issue. In fact, there’s this changing sort of mentality that we are responsible for patients across the continuum. I think the PACT model is a great way to improve these transitions using the PACT teams. I think different VA’s are sort of experimenting. We’re looking at it ourselves. I mean, how do we optimally transition patients? We have the great benefit – I work with non-VA hospitals on this question – but most of them don’t have an integrated medical record. The patients don’t follow up in their hospital. We have that as a great advantage. This is definitely a systems issue and not just the ownership of the inpatient physicians and the inpatient team providers or the outpatient teams. This is an integrated issue. 

Patsy:The current debate on readmissions has focused on whether SES matters and whether it is appropriate to adjust rates for it. Thoughts?

Peter:Yeah, that is a good question. There are smarter people than me that can answer that question. So, I don't think I want to get into it. It’s one of these things like with any models when you're adjusting. Instead of throwing everything into your model you have to really think about what it’s going to do to your model. The other thing is to look at it with and without and then decide from there. Those are the discussions...that’s what I love about working in VA and working with a team of investigators is that I don’t understand those kind of things well enough. Fortunately, I have co-investigators here and at other places that can talk me through those things. I’m going to defer that. I’m going to have to punt.

Patsy:Okay. The next question...

Peter:Yeah, one more and then I’ll go on and finish the last section.

Patsy:Okie doke. It seems to me that we need to develop explanatory models which hopefully would identify areas for intervention which may decrease readmission rather than just predict who will be readmitted. The Hasan model looks like it predicts that patient who are frequently admitted are likely to be readmitted. That makes sense, but no target for intervention. 

Peter:That’s great. That is exactly why I am not a big fan of all these prediction models. It’s like okay, you can make another prediction model and you're going to get you know, move the C statistic up slightly or you know sideways a little bit, but what are we going to do with it? You know, I think those of you that are...if you're researching in this area, that’s where you can really move things forward and say how are we going to use this to design interventions?

There are some things that people would argue in the readmission literature is that we should be doing for everybody. Medication reconciliation and giving patients and accurate medication list, you know if I were admitted to the hospital or my mother was admitted to the hospital I’d want her to get an accurate medication list. That’s not sort of a targeted thing that we just give accurate medication lists to certain patients. 

Now, there are things that you potentially could do for the very highest utilizers. I think one of them is definitely palliative care. I think the work that the VA has been doing in palliative care is fantastic. Understanding the needs of patients, communicating better, and improving palliative care is going to be one area where we can improve. 

Let me finish up the last section so we finish on time. Those of you who want to move on at the top of the hour, but I can always stay on longer and take questions. Okay, another audience response question: This presentation of the buzz-kill. Is there hope that interventions can improve readmission rates? This is getting at the intervention part. 1) Yes, studies consistently show readmission rates can be improved. 2) No, evidence is mixed. Interventions both have been shown to increase and decrease readmissions. Or 3) it doesn’t matter because the floggings will continue until readmission rates improve. 

Okay, and the results: I like the 18% who are just in for the flogging, but about 42% said yes that the studies show they improve, know that there’s mixed results, and I’m going to show you that there is some mixed results. 

Many of you on this call are probably familiar with this paper, especially those of you that have been in the VA since 1996. This is actually one of the best intervention studies that was done way before readmission rates were cool. So, a shout out to the people in Durham that did this study. If you see here, they did about 1400 patients. They randomized them to usual care or intensive physician/nurse intervention before discharging and continuing for six months. So, this is really integrating into the...integrating primary care with inpatient care with inpatient care.

Look what they did. They successfully increased readmission rates by about 25%. So from 19% being the intervention group and 14% of the control group. The patients also had more days of re-hospitalization. But, the patients were more satisfied with their care. Actually, Gene and Morris talk about it in the editorial they wrote for our paper and said kudos to the VA for not saying oh well let’s not expand primary care. If we expand primary care we’re just going to increase readmissions. 

The thing is, I would argue that a lot of these patients needed to come back and the people that aren’t getting follow-up care, some of them are either...they either suffer at home or more likely, they just actually get better on their own. You can argue that may be better, just let them get better on their own. The point is, we can increase readmissions with good primary care. 

In 2013, is the primary care medical home – or the PACT model – is it going to improve readmissions? There’s a paper that just came out in JAMA about two weeks ago that is sort of a different type of sort of community integration that showed some improvements but I don't want to go into that because it’s kind of complicated and I don't know if I agree with all their conclusions. But, I think in the VA this is still yet to be determined. I think some of the PACT demo labs are looking at this question. And certainly, if there’s a way to measure degree of PACTness and how much we have embraced the PACT model that may be it reduces readmissions. But, we’re not sure yet. My hope is that at the very least we’re getting people the care that they need.

Now, on the flip side, and by the way Morris’ paper is one of the least cited studies in the hospital readmission literature. I’m amazed when I see another...you know, papers come out that don’t cite it because he was way ahead of the curve. But these are the ones that get cited frequently. One is an intervention from the Society of Hospital Medicine Project Boost. I’m sure some of you have been involved with it. It’s a really great program. The main study from it hasn’t been published yet, but it provides a lot of tools. The Society of Hospital Medicine website has a lot of them posted on the website itself.

Somebody mentioned earlier Eric Coleman’s work that was some of the first really good work that showed that care transitions really matter. Mary Naylor had some similar work back in ’04. They sort of get cited frequently. They’re really well done studies and really valuable resources that they’ve developed. For example – Eric Coleman developed a care transitions questionnaire, the CTM – I think it’s either 15 or 12, and I think there’s even a three question version. We use that as part of our discharge follow-up, post discharge follow up, because it focuses the...the nurse that’s calling at post discharge, what questions do you ask the patient? You know, not just sort of call them and say hey, how are you doing? But, did you get your medications? Do you have any questions about your medications? Are you having any symptoms? The questions have been well developed. 

The other one that gets sited frequently and oftentimes I refer to this Project RED. This is an ARC funded project. They have a nice website, ARC does, with a lot of the tools from Project RED. This is another nice study done in Boston. They randomized about 750 patients, fairly young population – less than our average population age. What they found is the combined – and this is the last bullet there – readmission plus ED visits. I think that’s actually a really good way to look at...they sort of refer to it as re-hospitalizations. So, one of the things that can happen with the gaming is that patients come back to the ED and then the hospital doesn’t want to readmit them because it goes against them. So they figure out a way to get them out of the emergency department.

Readmissions and ED visits went down. Readmission weren’t statistically significant, but in the right direction. They did this in visits per patient per month. But, the combined was statistically significant. It showed about...you can see the reduction there. 

If you want to look at sort of a summary of all the interventions, This is actually a really well written paper that sort of summarizes what’s in the literature. Their conclusion is – it says right there – no single intervention implemented alone was regularly associated with reduced risk of 30 day readmissions or re-hospitalizations. So they came up with a taxonomy of sort of pre-discharge things like patient education - which some body brought up, med rec - which I think the VA is doing a really good job with, discharge planning. One of the things we’ve been challenged with recently is meeting a new...or I think it’s a new joint commission standard on documenting interdisciplinary care. We’re trying to incorporate that into our discharge planning. 

Post-discharge things like follow-up. Timely follow-up is probably a good thing. I don't know if it’s going to reduce readmissions but it’s a good thing to do. Patient’s like follow-up telephone calls. If you have low satisfaction you can call them and find out what they need. And then, things to bridge the transition – transition coaches and other things like information like appropriate health literacy and so forth. 

Another paper that just came out...I think it just came out yesterday actually – maybe the day before. Bob Burke at the Denver VA just published this in the journal of hospital medicine. I love the diagram because I like pictures. The title is great: Moving Beyond Readmission Penalties. I’m going to come to the penalties here in a minute. But, they sort of describe in this paper a really nice sort of model for transitioning patients from hospital to outpatient care. I’d recommend this one for reading just to give...if you're interested in transition issues. 

Some take home points: Interventions represent a bundle. We all see this more and more frequently about when you're trying to fix complex systems or complex situations you need to use a bundle of things. That’s the problem with this sometimes. You can’t just say well if I do this one thing, I can improve readmission rates. Most of these things represent what I would say are good things that we should be doing anyway. Most of them should be applied to every patient – like I said earlier about medication reconciliation and patient education. Everybody should get that.

I think determining your local rates. I think if you're going to look at this...our VA, when we were looking at it a year or so ago, somebody said we need to improve readmission rates. Well, we looked at our readmission rates and they were like 12%. Even for the highest diagnosis, which I think was heart failure, was still under 20%. It’s really hard to push it much lower than that at this point.

I want to talk about pay penalties. Here’s a question for the audience: CMS is reducing payments for hospitals with higher than expected readmission rates. This is going to: decrease readmissions and save money, increase readmission and cost money, or will have no net effect but will result in gaming of the system? 

[Audio cuts out from 00:51:53 to 00:52:14]

Peter:One .... estimated that if you we going to implement a program like project boost or red, you know the cost of it and this is an estimate would be around 170,000. But you’re trying to bring your readmission rate down and used a PRG payment of $5000 for a pneumonia patients and if you had 400 admissions during the year and your readmission rate was 25% that gives you a total of 500 admissions and what that extra payment would be but let’s just say you reduced it from 25% to 20% your number of total admissions goes down, you lose about 100,000 plus the 170000 it took to implement it so from the hospital perspective on the private side they may not be incentivized when they see what the numbers look line but that remains to be seen. Last two slides and we’re done. I think the future in terms of research I think prediction rules could be used but only as somebody asked, only if they can change practice only if we can use them and I think that systematic review really nicely outlines what’s out there and if you can come up with a better mousetrap, congratulations. Figure out to get some more mice. Interventions... I think we have enough already. I think we need to know more about systems implementing them which this systematic review nicely outlines since there’s no single one perfect intervention out there and I think Bob Burke’s paper nicely talks about what are some ideal processes to improve transitional care. Preventability, I think we probably can prevent some, personally I think ways to use this for real time learning and the times patients are readmitted to see if you can to identify system failures. Again there’s a nice paper that outlines it. More papers that talk about race and associations for medical and surgical patients. I’m not that interested in reading them personally because they don’t tell us anything we don’t already know but if you can come up with something new, great.

My last slide here. I don’t think hospital readmissions is a good quality metric but it’s got people’s attention for care transitions and I think that is good. I think identifying preventable re-admissions is difficult but it worth trying if we can prevent then that’s great. I think preventability overall is limited I think you can only prevent maybe half of the 25% that are considered preventable but there’s certainly things that can result from errors and adverse events and system failures that we can identify and correct. I think our prediction models generally stink but I think it’s  not impossible to figure out how to use them better. in terms of intervention there are bundles out there but I think  we need to do a better job of spreading them, whether it be through measured implementation models where high performing VAs help the lower-performing VAs learn from their experiences, learn from better transitional care efforts in the PACT model etc. And I think the floggings will continue at least for a while. And we’ll see where it goes.

And that’s it from Iowa. I’m finished.

I know some people have to leave at the top of the hour but I’ll take questions for as long as people want to ask them.

Margaret: Based on one of the previous questions, are interventions focused on the inpatient portion a relative waste of time. Should intervention be focused post-discharge?

Peter: That’s a good question. I really think it’s sort of... in the VA we have an ideal setting where we have so much of care integrated. Granted we know that a lot of our patients are dual users of VA and non-VA services so we have to recognize that but I think it’s not that one area of focus is better than the other that we should be focusing on either the inpatient or the outpatient but it’s that transition, it’s that handoff. I think the same thing you could argue that on the, some people say well what about the initial admission? There’s a body of literature on ambulatory care sensitive conditions and preventable admissions because you can’t get re-admitted if you weren’t admitted the first time. So I think it’s a system issue and that’s always an easy answer to say it s the system. But I think that where your focus is... where in the system can potentially you have the most leverage. 

Margaret: And David want to say “Flog time” exclamation point

And the next question is “Bundling payments for care will begin this year. It’s a CMS pilot that is looking to reduce readmission and improve care. 

Peter: I know there are people on the call who know way more about the bundled payment issue. This is going to be a big challenge because there are potentially some significant unintended consequences for safety net hospitals, for rural hospitals, for academic medical centers that take care of patients with lower economic status. It’s a challenge. I’m glad we are trying it but I think its going to be difficult.

Margaret: What are your thoughts about public access to compare readmission rates across facilities. Hospitals are incensed by it. Not look great when these rates are published.

Peter: yeah, same answer. There are lots of people who know more about it that I do. But my opinion is that there are lots of, whether it be Hospital Compare or other of these public reporting web sites and we know that people don’t really use them. I think probably the people who look at them the most are people in the B Suite and they get all excited when they look good and they get all anxious and upset when they look bad because nobody wants to look bad. I’m all for transparency and get it out there and if people want to use it they can interpret it how they want. I think it could be used for something elective  that your making conscious decisions about. I f you need a joint replacement and you want to hunt around for the best place to have that elective surgery but if you’re having an acute event you’re not doing a lot of web searching to find out where’s the best place to go. 

Margaret:  Last question. Is that the RAGBRAI bike tour?

Peter: The picture’s not because these people are moving much faster and there’s not people on the side of the road selling pork chops and pie and ice-cream. That’s a bike rave which if I were in it, I’d be way at the back, but the RAGBRAI, yes, last weekend in July, every year for 40 years. So if you ever want to come ride your bicycle across the state of Iowa for seven days, it’s a lot of fun. 

Margaret:  And what degree temperature?

Peter: Well I’ve had some of the coldest days of my life on RAGBRAI in July but also some of the hottest.... you know between 60 and 110.

Margaret: Thanks very, very much.

Peter: Okay everyone. And if anyone has individual questions or criticisms, my email is ...[inaud.]

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