**Transcript of Cyberseminar**

**HERC Health Economics Seminar**

**Facility Variation in Hospital Readmissions for Heart Failure Patients**

**Presenter: Chuan-Fen Liu, PhD, MPH**

**January 16, 2013**

**Facility Variation in Hospital Readmissions for Heart Failure Patients**

**January 16, 2013**

Jean:So we are pleased to have Chuan-Fen Liu present for us today. She is the Core Investigator and Health Economist at the Northwest HSR&D Center for Outcomes Research in Older Adults at the VA Puget Sound. She is also Research Associate Professor in the University of Washington’s Department of Health Services. Dr. Liu has done extensive work in evaluation of VA healthcare delivery, and today she will be presenting on Facility Variation and Hospital Readmission for Heart Failure Patients. So welcome, Fen.

Dr. Chuan-Fen Liu:Well, thank you. Thank you for having me today. So is my screen on now?

Jean:Yes. Your screen is on. But, Fen, if I could – that dashboard that is on the right-hand side of your screen is giving us some graphic distortion. If you could click on that orange arrow ...

Dr. Chuan-Fen Liu:Yes, uh huh.

Jean:... get it out of the way. Perfect.

Dr. Chuan-Fen Liu:Okay.

Jean:Great, right there. Perfect. Thank you.

Dr. Chuan-Fen Liu:Mm hm. Okay. Thank you. So today I am going to present some of my current work looking at the facility variation in hospital readmissions.

So should I just go to the arrow to get the slides moving?

Jean:You can do the arrow that is in the lower left-hand corner or you can use your up and down arrow keys or your space bar or any of those should work.

Dr. Chuan-Fen Liu:Okay. Thank you. Okay. So this is the current project and it is funded by VA HSR&D and I would like to acknowledge my study team, which is mainly based in Seattle with our co-investigator and research staff. And we also have Matt Maciejewski and Becky Yano and Peter Kaboli and Paul Heidenreich from different sites.

And the outline for my presentation, my – today’s presentation—first I would give you a little bit of background of the study and objectives of today’s presentation. And then I am going to give the methods and then I would like to share with you some of the preliminary results that we have so far, mostly in the heart failure readmission rates and costs and facility variation in heart failure readmissions.

So this is – my presentation is a part of a larger study looking at the organizational factors related to hospital readmissions. There are two aims for the study.

The first aim is to assess variation in readmission rates and costs for the two most common medical conditions in VA for inpatients: heart failure and COPD. And we will include both VA and Medicare readmissions in our outcome measures.

And the second aim will be to identify the three key level factors, clinical practices that are associated with readmissions and we will use – after we finish the first aim of constructing measures at the patient level and we will merge with our facility surveys to do the second aim. And today’s presentation will mainly focus on the heart failure results in Aim 1.

And so readmission today is much bigger and at the higher level is the policy issue related to hospital readmissions because they are very common and very costly. Like in Medicare the 30-day readmission rate is like – 20 percent overall. And heart failure is one of the medical conditions with the highest re-admission rates. About 25 percent of heart failure patients are readmitted in 30 days.

And also the readmission reflects this is really costly. I think it is in 2004 I think it is estimated to have about $17 billion for readmissions in 30 days after they were admitted first. And so it also reflects the really poor quality of care and have the efficiency of care.

So reducing hospital readmissions, I think, could really be a way to improve quality care and improve the efficiency of care.

So Medicare started public reporting in readmissions for three conditions: heart failure, acute MI, and pneumonia in 2009. VA hospitals were included in Medicare Hospital Compare in 2011.

Medicare tried to push the reduction of the readmission rates so they were put into another financial incentive. Actually it is a penalty. So under the Affordable Care Act, Medicare put in a reimbursement penalty for hospitals with excessive readmissions. In 2012 last year I think the penalty can be up to one percent and then this year it will be two percent and next year it will be three percent.

So readmission in the VA—and the VA has been focusing on a quality improvement initiative trying to reduce readmissions. A recent article by Peter Kaboli in Annals looked at the 30-day readmission rates over time in the VA and it shows that this is a decreasing trend over time. Overall readmission rates from 1997 16.5 percent decreased to 13.8 percent in 2009-10. And for a heart failure cohort they observed a similar decreasing rate trend from 1997-98 it was 20.5 percent to 19 percent in 2009-10. 

However, Paul Heidenreich had a paper a couple years earlier looking at heart failure hospital mortality and the readmission rate and found kind of a diverging result. Now they were found to have improved heart failure mortality but slightly worsened heart failure readmission rates in the VA during 2002-2006.

And in a VA Hospital Compare I think you look up the Hospital Compare and it published that the heart failure readmission rate is 20.8 percent for the trends over 65 years old in 2007-2009.

And all the current published data on readmission rates only includes readmissions in VA and have not included the readmissions in Medicare and we know that this is a large proportion of patients, VA patients also covered by Medicare.

There are some effective interventions from the trial that show they are able to reduce hospital readmissions for heart failure like discharge planning, which has become a Medicare performance measure. We now have proper discharge planning for heart failure patients, and patient education and post-discharge management like post-discharge followup. 

But not all trials show this to be effective, like in a large trial in the VA back in the 1980s showing that improving primary care access after hospitalization actually significantly increased hospital readmission in a couple of telemedicine trials in the VA. There is no significant impact.

And CHF QUERI is implementing the initiative to Quality Improvement Initiative like the hospital to home initiative and tried to better manage heart failure patients in their transition from hospital to home. The early results do not seem to have a big impact.

And so this is debate over preventability of the readmission, whether—no. These readmissions are preventable. Some studies show us the proportion of preventable readmissions made up of as larger what Medicare assume.

And last week, Harlan Krumholtz who led the group that developed Heart Failure Medicare Performance Model has a paper in New England Journal that talks about hospital risks. Now this patient is admitted to a hospital. They have a risk during the hospital stay. Maybe they are very vulnerable. And that could have affected their recovery. 

And a most recent paper by a VA researcher in the Hockenberry paper in Medical Care shows that the prior quarter of readmission rates does not really predict the next quarter readmission rate at the hospital level in the VA.

So this is the debate over whether readmissions are preventable.

And the objective of my talk today is looking at the estimate of heart failure readmission rates and costs, accounting for readmission in the VA and Medicare, and examining the facility variation in heart failure readmission rates.

Our study was a retrospective cohort study of heart failure inpatients in the VA. We studied three years. It is from FY 2007-09 and we had one year follow-up period for each patient after their index admission and for each cohort.

The data sources from VA are the administrative data, so we have patient treatment files that we looked at. We included outpatient encounter files, fee-basis files, vital status, DSS data including inpatient, outpatient, lab, pharmacy, and CDW vital records. We used Medicare claims and so MedPAR inpatient and Carrier files outpatient and then hospital outpatient files.

And for the 30 characteristics or clinical practices, we used facility surveys of CHF Practices conducted by CHF QUERI in 2008. 

So our study samples: we identified our identification specification. We basically took the one that was on Medicare heart failure readmission performance measure. So we identified patients with an index admission in VA hospitals based their principal discharge diagnosis. These are the ICD-9 codes. 

The index admission is defined as the first admission in a VA facility for each patient in a given year.

Our outcomes—the first one is readmission rate. When we defined readmission rate, we used the first hospitalization after the index admission. So if a patient has multiple readmissions, we would use the first hospitalization to determine which time period to determine readmission rate.

Readmission rates—we calculated all-cause readmissions if a patient was readmitted for any reason, and heart failure was a specific readmission rate, so if a patient was admitted for heart failure.

Our main outcome—we tried to compare the 30-day all-cause readmission that is – we tried to – it is kind of – have to parallel outcome with Medicare and the VA hospitals compare. We also calculate in the secondary outcomes of readmissions in 60 days, 90 days, and one year.

And for source of readmissions from the VA, we included the readmissions to any VA hospitals. We also included non-VA care through fee-basis and this is in process, so we did not make a presentation. This is not in our readmission rates. But we do include the non-VA care portion that was reported in current results. And then we included other readmissions that occurred in Medicare.

For readmission costs, we included cost of all readmissions of the event and in one year after the index admission. And we take the payers’ perspective and so we take VA expenditures. So we use DSS costs for VA care, and then VA payments for non-VA care, but again this is still in process. And we take the Medicare reimbursement amount, the costs of the Medicare.

And we constructed the VA-only costs and total costs and VA plus Medicare; and that is the same for readmission rates. We constructed VA-only and total. That means VA plus Medicare.

And for cost, the cost of readmission rates – the admission costs are in 2009 dollars and were inflation adjusted using Consumer Price Index.

And for patient characteristics, we included kind of general, regular sociodemographic factors like age and gender, income means test, and disability. And we constructed access to VA care in terms of the distance to the closest VA hospital/clinic. And we have health status in their comorbidities including DCGs.

We have for some additional measures that we are going to use for our future alternative risk adjustment models like their medication adherence and BMI and some clinical factors that we are going to explore next.

Okay. The analytical/statistical methods in addition to the kind of descriptive analysis that we do and first the analysis we performed was doing the risk adjusted readmission rates at the hospital level. We used the hierarchical models. You can do the facility level random effect and we used GLIMMIX in the SAS. We used the Medicare model, Medicare heart failure readmission model. We adjusted for age, gender and 35 HCC categories from DCG.

And the next one we are going to do is we have to construct different risk adjustment models and we are going to start working on the cost model and takes up a lot of [inaudible]. I think we at this point we are going to use a two-part model to estimate risk adjusted readmission costs.

And next I am going to present our preliminary results.

So this is our cohort selection I am going to present. Most of results are for 2007 because we started out with 2007 and we are in the process of getting the two years of data together and that is how we can combine them.

So when we started out with finding our cohort, we had a larger, kind of broader definition of heart failure. We looked through the literature. We found every possible diagnosis code or DRGs to define heart failure, so we have a broader definition based on diagnosis. And so then we noticed about now 20,000 inpatients in VA. We excluded people who are institutionalized or in nursing home. 

Then the next step would exclude people who did not meet the strict heart failure criteria based on the readmission performance measure. So we got about 17,000 patients. And we started excluding patients that were transferred, who were discharged AMA, who were admitted because we take every – all the VA care from PTF files. So excluded patients who were admitted to a non-VA hospital. 

So at the end we have 15,000 patients with an index heart failure admissions. And we excluded now 458 people who died during their index admission. So our cohort in 2007 is 14,800 patients.

And actually the numbers are pretty stable over the three years. Now we have about now 14,800 in 2007, 14,700 in 2008, and a little bit more than 15,000 in 2009. So in the combined cohort we have about 39,000 patients.

And we kind of do a first look at who are admitted and what is the proportion of people who get admitted in one year after their index admission so over the three-year time period. So, 70 percent of patients were admitted up to their index admissions. Among those who were admitted, 83 percent had their first readmission in the VA and about 17 percent had their first readmission in Medicare.

For 30 percent of them who never had any readmissions, 20 percent of them died after the index admission and 80 percent were alive. So looking at this number, we can see about a quarter of the cohort would be alive and never be readmitted. Only just about a quarter, like seven percent of people that died are readmitted.

And here are some – here are the patient characteristics for 2007 cohort and we do the readmission – now we know their readmission based on their one-year status—whether they were readmitted in one year or not.

Look at the age. People get readmitted if they are older, well, one year older. And 67 percent of people were readmitted 65 years and older. So the age is older for people to get readmitted. And the majority of them are male. 

And most of them receive free VA care. We constructed this variable determined by if you have 50 percent or above service-connected rating or your means test is below – your income is below means test. So the majority of the patients are covered by the free VA care in the VA, which is this number, because it is kind of higher than what our work previously found in our previous work in looking at the general primary care patients. It is around 70 to 80 percent. So the heart failure patients here we identify mostly receive free care in VA.

And 78 percent in the readmission group covered by Medicare and 70 percent in the no-readmission group. So we can see a 65 year-old is 78 percent covered by Medicare. It means some of them are under 65 and eligible for Medicare due to disability.

And the mortality rate is high. For those who were readmitted in one year, the mortality rate is 32 percent. And even for the no-readmission group it is 21 percent. So the overall readmission rate for the cohort is 29 percent.

And they are sick. The DCG score is 2.6 versus 2.3. And they have a high proportion of patients with hypertension, ischemic heart disease, and diabetes, and half of them had COPD. So the patients not with heart failure readmissions had really high comorbidities – have other conditions.

This is our unadjusted readmission rate. So the dark blue is Medicare, when we add Medicare data. So the left side, this is all-cause readmission. So for 30-day readmissions, if we counted looking at the VA only, it is 21.4 percent. Adding Medicare data added 3.6 percentage points, so the total readmission rate is 25 percent. So as we get the readmission rate in the longer time frame, of course the readmission rate increases. So by the end of one year after the index admissions, 63 percent of patients were readmitted in the VA and adding Medicare data of 7.5 percent, so the one-year readmission data actually is like 70 percent based on our cohort were readmitted in one year.

So when we look at the heart failure specific readmission rate, it is about half of the all-cause readmissions. So they get readmitted for other reasons. And for the 30-day readmission rate, heart failure-specific, if we count VA it is 8.6 percent. Adding Medicare data added about 4.6 percent. So it is 13.2 percent. So by one year the total heart failure-specific readmission rate is 34.2 percent.

And so we can see the comparison to how much Medicare adds, and Medicare adds about more in the heart failure-specific readmission rate.

And this is ...

Jean:I have a question here.

Dr. Chuan-Fen Liu:Okay, sure.

Jean:Somebody asked, does CMS exclude observation stays? So how does the VA [overlapping voice] ...

Dr. Chuan-Fen Liu:Yeah, we [overlapping voice].

Jean:... without admissions without observation stays?

Dr. Chuan-Fen Liu:We excluded observation stays, too. But if the observation stay lead to an inpatient admission, we included that one. But we look at observation as actually not that many in our data. And it did not – whether we included or excluded this really did not make that much of difference.

Jean:Okay, thanks.

Dr. Chuan-Fen Liu:Okay. And this other number of readmissions in one year. And this is average number across – from our cohort. Some – on average this 2.3 not be admissions in one year. And about it’s 1.7 in the VA and 0.6, so about a quarter in Medicare.

So heart failure is 1.1 number of readmissions per patient in one year, about half and half, half in VA and half in Medicare. So there are a lot of readmissions. When you think we have 14,000 patients and on the average it is 2.3. That is a lot of inpatient events.

Here are the costs and this is the only slide I have for costs to share at this moment. So we counted all the inpatient – all the readmission costs. So for VA inpatients and Medicare inpatients and plus their professional fees. And in 30 days the average cost per patient is about $3,300 and about a quarter from Medicare. But by one year the average cost per patient is like $30,000. And that is $24,000 in VA and $6,000 in Medicare. So the admission cost is really high.

And when we look at those at the really aggregate level I just presented and now we are looking at the facility level readmission rates. So we are looking at – we calculate the facility level readmission rates. So in our sample we have 127 VA hospitals included. So the mean number of patients is 117 in the range from 141 to 543, so they are from very small hospitals. To calculate the facility level readmission rate we excluded seven facilities with fewer than 25 patients. So an adjusted rate is at a mean of 24.5 and ranged from about 13 percent to 40 percent [inaudible] and the rate is from 22 to 28.

So this is – so we break it up by whether we include VA and Medicare or VA only. We see the higher readmission rate at a facility level after including Medicare data and so it is kind of the same as what we observe at the aggregate level at Medicare after taking the number of readmissions in our cohort.

And we look at the readmission rates in three years in our three cohorts and it is really stable over time again, this unadjusted, so it is pretty similar in three years.

Next we look at the all-cause readmission rate adjusted rate using Medicare model. So the adjusted rate, the mean, readmission rate is 24.1 percent. So the ranges of the titre compared to unadjusted is from 13.5 to 37.2, so interquartile range is 21 to 26.

And when we rank the hospitals from the lowest to the highest, you can see some of the 95 percent confidence intervals are quite large. But in a way you can see the hospital in the middle is pretty tight and pretty close, but the hospitals with high readmission rates look like they are significantly different from the hospitals with low readmission rates and there is no overlapping confidence intervals.

So when we look at – the next one is heart failure-specific readmission rates. So the adjusted heart failure-specific readmission rate is 12.5 percent and range from about 4 percent to 22 percent. And the interquartile range is from 10.9 to 13 percent.

And here is the ranking of when we ranked the hospitals from lowest to highest. Again, some of the large confidence intervals so I think still set a small one. But this whole one is really like 13 percent. No, it is very, very low. And again we can see some of the hospitals with high readmission rates, really significantly higher than the lower ones – ones with low readmission rates.

So I am going to summarize the results we have so far. First, in really accounting for VA-only readmissions, we underestimate both readmission rates and costs and the magnitude is bigger for heart failure-specific readmissions.

And the second point really struck me as well is that 25 percent were readmitted within 30 days, but 30 percent of them were readmitted in one year. And while our performance measurements are focusing on the readmissions in 30 days, but a lot of readmissions occurred after 30 days.

To me it felt like we need to – whether of course I think the issue is whether we can hold hospitals accountable for readmissions in a longer timeframe. But to me this is a bigger health services problem. If patients are really sick and their healthcare burden is extremely high, then I think that the healthcare system needs to figure out how to manage these patients and maybe consider maybe it is not just the healthcare system. We need to consider kind of the social perspective. When you think if a homeless person would come back no matter what or a patient does not have family members or caregivers to show them they will cut down the full impact of something.

And so it really felt like there are a lot of – there are other reasons. Kind of look beyond not just 30 days and think about the healthcare system, how we can manage these patients and better. And our results show wide variation in readmission rates across VA hospitals, which really now gets to the next step when we identify other facility characteristics.

So this is preliminary results. And so some of the next steps we are trying to get done is to complete all of the readmission rates and costs for heart failure cohorts, and then combine our facility surveys with patient level data so we can look at organizational factors associated with heart failure readmissions.

And this concludes my presentation. I am happy to answer any questions.

Jean:Okay. We have several questions in the queue right now. Okay. One question was, what is the source of the DSS data?

Dr. Chuan-Fen Liu:Well, the source that we used costs from DSS inpatient extract. So we take the DSS costs from inpatient – DSS National Inpatient Extracts. But some other DSS data we used, we – no, I do not present it here, the last and promising data that we construct from medication adherence and other factors.

Jean:Do you have any plans to look at other types of costs like outpatient and skilled nursing facility and long-term care costs for these patients?

Dr. Chuan-Fen Liu:No. We will look at outpatient costs but not adherence [inaudible]. Yeah.

Jean:Yeah. Okay. So you are not – you do not have any plans to look at like the episode of care costs or like the annual care cost per patient?

Dr. Chuan-Fen Liu:No.

Jean:I would think there would be a tradeoff between you know greater admissions and outpatient care.

Dr. Chuan-Fen Liu:Yeah, that is a very good point. But it is not in our plan because I think yet – we look at – we would collect outpatient care costs and we did look at some of the long-term care costs, whether they go into a long-term care facility and the number is not that big at least from our cohort in 2007. And we thought to look up two different issues about readmission, whether you live in a community or you live in a nursing home. I think the issues are different. So we focus on the community patients.

Jean:Okay. Oh, I wanted to let you and Heidi know that they are going to shut off our server in about five minutes. 

Heidi:Okay.

Jean:So I may not be able to read off the questions, so, Heidi, I may need you to take over in about five minutes.

Heidi:Well, that is fine. I will just keep track of where we are.

Jean:Okay. So the next question is – for Fen is, do you have any hypothesis on whether the pattern of readmissions to VA versus Medicare is different between all-cause versus heart failure?

Dr. Chuan-Fen Liu:We kind of thought about it initially and maybe – we have not looked at exactly what other conditions for their readmissions. But one thought is that heart failure made be more acute, so they would just go into – go to whatever the nearest hospital and – rather than go to the VA hospital. So maybe just the distance and acuity issue. But we need to look into in terms of what other cause of readmissions to see how different they are.

Jean:Okay. And the next question asks, are there any plans for the VA to begin including non-VA admissions into the readmission rates? From a data collection standpoint how automated can this be?

Dr. Chuan-Fen Liu:Well, because of lack of – there is a big time lag between getting Medicare data. So I think -- this I do not know exactly what the – in terms of public reporting, I do not exactly know. But I think it is important to include Medicare readmission and of course that causes us to think about whether – now we talk about it. It is only – kind of – only VA perspective. Or no if you want – would want more patient-centered perspective, then we should include Medicare. But I think lag time is a big issue because we can pretty much get VA data.

Jean:Okay. So there is no discussion in terms of performance measures to include non-VA admissions?

Dr. Chuan-Fen Liu:Not that I know of. But I cannot tell for sure.

Todd:Yeah, I have not heard of anything, either. And as Fen points out, the lag is critical because right now we have FY12 data, National Data Extracts available. But we do not have – I think that we are still at FY10 or calendar year 10 Medicare data and Medicaid data is even farther behind. So depending on how you want to pull these data in, you are looking at a significant lag.

Jean:Okay. And the next question asks, any sense of how these readmission rates have trended since 2007?

Dr. Chuan-Fen Liu:We look at – one of the slides I think I showed – it is not – it is pretty similar from our data – from our three-year cohort. But those are unadjusted, so we need to look at after adjustment whether they now have any significant change. But from the unadjusted numbers, they look pretty stable over the three years.

Jean:Okay: And just do you happen to know if PACT is tracking readmission rates and whether there is the thought that PACT may have an influence on lowering readmission rates at all?

Dr. Chuan-Fen Liu:Well, the readmission rate is in one of the big sets of measures for PACT evaluation. But I do not think we have – I think we have not done it yet. So I cannot tell you exactly the results. But it is in one of the measures but not on top of our list. So.

Jean:Okay. Sure. Then somebody had a [inaudible] and they asked about can you address the typical difference in the differences between those readmitted versus those not within one year of index admission? This is on slide 19. You had some data presented in a table.

Dr. Chuan-Fen Liu:Okay let me see. Oh, the patient characteristics. I – we would do that, but when I pulled the tables together I did not put in the statistics ...

Jean:Okay.

Dr. Chuan-Fen Liu:... to ...

Jean:Okay.

Dr. Chuan-Fen Liu: ... that is a very good point. I should have done it. [Laughter] 

Jean:Okay.

Dr. Chuan-Fen Liu:So I cannot tell you exactly. I think some of them must have [inaudible] but I am not quite sure exactly.

Jean:Okay.

Dr. Chuan-Fen Liu:Mm hm.

Jean:Okay. Todd, did you have any other questions?

Todd:Fen, can you go back to how you defined readmission? I guess I want to make sure I understand that because we are doing similar stuff here and I just want to make sure I learn from you.

Dr. Chuan-Fen Liu:Okay. Okay. [Overlapping voices] 

Todd: So you defined ...

Dr. Chuan-Fen Liu:We defined it – we take the first hospitalization up to the index admissions. That is the one we defined as readmission rate.

Todd:Okay. So the page before I think you talk about index admission.

Dr. Chuan-Fen Liu:Right, mm hm.

Todd:And you can have an index per fiscal year, right, or is it per calendar year?

Dr. Chuan-Fen Liu:Right. Per fiscal year.

Todd:So I am curious about the year-to-year interactions here. Or is interaction not the right word because it is not a model per se.

Dr. Chuan-Fen Liu:Yeah.

Todd:So let us say you get admitted in September. Then that is really your inpatient admission.

Dr. Chuan-Fen Liu:Mm hm.

Todd:And then you get discharged and you get readmitted in October or November.

Dr. Chuan-Fen Liu:Yeah.

Todd:It is a new fiscal year. Is it a new index or is that a readmission?

Dr. Chuan-Fen Liu:Yes. That would be a new index. We kind of codify. We tried to match what Medicare performance measured, the aggregate three-year data, the cross sectional cohort. Amazingly, when we look at three years of cross-sectional cohort year-by-year, we have only 20 percent overlap from one year to the other. But that is another discussion about how shall we define cohort. And yes, we defined it by year. So it is just what you said. It is – the cut off would be at the fiscal year. And in one of our – I think we constructed a measure in there of kind of a pre – a kind of 30-day and 90-day, whether they get admitted or not. So that kind of gives – that would give us a sense how many patients actually have an index of admission because of the cutoff. 

The other thing that actually Paul Heidenreich had put a very good suggestion about now would be how do we define readmission dates? We should look at something different, our – kind of different measures whether each hospitalization should be treated as an index admission and whatever now. So that means one – if a patient is admitted multiple times a year, it should be treated – have multiple index admissions.

And so we – it is a very interesting question in terms of how we measure whether it is a measurement.

Todd:Yeah. And then the other question that I was going to follow-up on that—and I apologize. I am hearing feedback, so I will try to ignore it. When someone goes into Med/Surg in the VA and then they go to like a SNF and it is contiguous, it is all part of one record. So what happens if they go to Med/Surg-SNF-Med/Surg and it is all one record? So you might say that is all part of the index. But in Medicare that would be a readmission.

Dr. Chuan-Fen Liu:I think we did encounter that. I have to kind of think how we did that. Yeah, in the VA we can see that is one record. In Medicare ...

Todd:Or three treating specialties. Just to be clear, if you use the Treating Specialty File or Bed Section file, you would see it as – you could just differentiate it, if you wanted to. I just was not sure if you did.

Dr. Chuan-Fen Liu:Right. No. We did not differentiate. We take the entire admission as the index.

Todd:Okay.

Dr. Chuan-Fen Liu:Medicare, I do not think we take the SNF in. So if Medicare, if they come into one Medicare admission and the person went to SNF and then that kind of counted as one. So the first one that you – this two admission counted as one of the admission and the SNF does not count. And then we take the exit coming back from SNF. That would be another readmission. I think yes.

Todd:Okay. So my interpretation of this might be that it could affect how you interpret it, but it also might mean that you are underestimating your total readmissions.

Dr. Chuan-Fen Liu:Yeah. Mm hm. Right. Because if you want to only count acute Med/Surg, then it gets – yeah. 

Jean: Mm hm.

Todd:Thanks.

Dr. Chuan-Fen Liu:Mm hm.

Jean:I know a lot of people are starting to look at readmission. I wonder if there are any researchers who are developing methods to sort out are certain admissions inappropriate versus appropriate? Because if you are very sick, I mean you may need a lot of inpatient care and it may be appropriate to get that care.

Dr. Chuan-Fen Liu:Yeah. Amy Rosen in Boston is looking at the preventable readmissions and she is doing the work to validate the preventable readmissions software by [inaudible]. I think now she has some results. But I do not know exactly what they found. But she is working on that. Mm hm.

Jean:Okay. So a company developed software to distinguish between preventable and non-preventable ...

Dr. Chuan-Fen Liu:Yes.

Jean:... readmissions? Okay. Just a reminder. If you have any questions for the presenter, to please enter it to the Q&A panel. So we do not have any questions currently in the queue. And there was somebody who was asking me about handouts. Heidi, do you want to remind people about how to get handouts from the presentation?

Heidi:Yes. We sent a direct link out in the reminder that was sent out this morning. So you can click on the link there. Or we will be sending an archive notice out to everyone as soon as this is posted with – and you will be able to – the archives does link to the handouts. So there will be two places. One you can get to right now. The other one you will need to wait until probably tomorrow morning or so and we will get that out to you.

Jean:Okay. So there are not any more questions in the queue. But we really appreciate you presenting your research to us. If you have any more questions, feel free to email us and we will be sure to forward them to Fen so she can respond.

Todd:Yes, thank you very much, Fen, and thanks, Heidi, for all your help, and Jean.

Dr. Chuan-Fen Liu:You are very welcome, and thank you for having me.

Jean:Okay. Thank you.

Heidi:Thank you very much.

Jean: Bye-bye.

Todd:Bye.

Page 1 of 14

Page 14 of 14