The Relationship Between Discharging Patients From the Hospital Too Early and the Likelihood of a 30 Day Readmission: Treat, Street and Repeat.

I'm baaaaack!

When persons are admitted to a hospital, insurers' payment rates are based on the diagnosis, not the number of days in the hospital (known as a "length of stay").  As a result, once the admission is triggered, the hospital has important economic incentive to discharge the patient as quickly as possible.  The Disease Management Care Blog's physician colleagues used to refer to this as "treat, then street."

Unfortunately, discharging patients too soon can result in readmissions.  That's why the DMCB has agreed with others that diagnosis-based payment systems and a policy of "no pay" for readmissions were working at cross purposes.  Unified bundled payment approaches like this seem to be a good start.

But that's all theoretical.  What's the science have to say?

Peter Kaboli and colleagues looked at the push-pull relationship between diagnosis-based payment incentives  and the likelihood of readmissions in a scientific paper just published in the Annals of Internal Medicine. 

The authors used the U.S. Veterans Administration (VA) Hospital's "Patient Treatment Files" to examine length of stay versus readmissions in 129 VA hospitals.  The sample consisted of over 4 million admissions and readmissions (defined as within 30 days and not involving another institution) from 1997 to 2010. The mean age started out at 63.8 years and increased to 65.5 years, while the proportion of persons aged 85 years or older increased from 2.5% to 8.8%. Over the years, admissions also grew more complicated with a higher rate of co-morbid conditions, such as diseases of the kidney (from 5% to 16%).

As length of stay went down, readmissions should have gone up, right?

The answer was yes and no.

Yes, if the data were trended over time: Over the 14 year period of observation, the number of days in the hospital (length of stay or LOS) decreased from 6.0 days to 4.3 days.  Yet, as LOS decreased, readmissions also decreased from 16.6% to 15.2%. 

The decreases held up when the LOS was risk-adjusted for hospital and patient characteristics.  There was also no increase in mortality rates

No, if hospitals were compared to each other:  Hospitals with risk-adjusted low lengths of stay had higher readmission rates compared to their average peers.  In that group, each day of saved LOS was associated with a 6% increased rate of 30-day readmissions.

It gets even more complicated.  As the LOS increased beyond the average, each additional day in the hospital was associated with a 3% increased rate of 30-day readmissions.

What should the DMCB learn from these data?  Keeping in mind that the VA is not necessarily generalizable to the typical community medical center,

1. Over 14 years of worth of VA data for 129 hospitals suggest it is possible to have your cake (a lower LOS) and eat it too (lower readmissions).  That's the good news.

2. While overall performance improved over the years, between hospital comparisons showed there is a "U" shaped relationship between days in the hospital and the likelihood of readmission.  The DMCB agrees with the authors: premature discharge before the patient is ready is associated with an 6% per day readmission rate, while patients who are very sick and have to stay a few extra days in the hospital are also at risk to the tune of 3% per day.  That's the sobering news.

What are the implications?

Overzealous efforts to discharge patients can backfire with readmissions.  It appears there's an optimum length of stay that minimizes, but will never eliminate, readmissions.

Patients who do go home "too soon" or need extra days in the hospital appear to be at special risk.  Accountable care organizations and population health management service providers should use this information to target patients at special risk of "treat, street and... repeat."

Medicare Readmissions Equals Revenue Cuts Equals Hospital Consolidation. Here's Why

This way to consolidation....

Disease Management Care Blog readers may recall that Medicare's Hospital Readmissions Reduction Program was among the many provisions of the Affordable Care Act. 

According to the finalized regulations, if a hospital's readmission rate within 30 days for heart attack, heart failure or pneumonia exceeds an established norm (using three years of data based on a minimum of 25 patients with a statistical risk adjustment to account for co-morbid conditions), that hospital's Medicare payment rates will be reduced for all discharges in the following year. The reduction, depending on the excess rate, can go from zero (readmissions meet the norm) to a maximum of 1% (the hospital penalty results in payment of only 99% of the applicable fee schedule).

Now, Kaiser Health News has just looked at the numbers and calculates that, thanks to the HRRP,over 2000 hospitals will forgo close to $300 million. According to KHN, 278 hospitals - including some household names - will achieve the dubious distinction of a full 1% reduction.  You can check out how your local hospital will likely fare here.

While readmissions themselves are a significant problem, the approach used by the HRRP has its own set of under-appreciated methodologic challenges (as noted here and here). Now that hospitals are about to get battered by a well-meaning if flawed payment system, your DMCB raises one more red flag:

This will drive hospital consolidation.

That may well be one intent of the law. Cheesecake Factory logic tells us that large hospital systems have the intellectual and capital resources to systematize care, apply best practices, reduce variation and maximize outcomes.  Rather than weep for those hospitals that are losing income, Washington's policymakers are probably hoping that the losers have one more reason to join forces with the bigger, smarter and more efficient hospitals or systems nearby or in the next state (especially the ones with a smartly run disease management program).

Yet, whether or not hospital consolidation alone would make a palpable difference in cost or quality remains to be seen (as indicated here and here). What could happen instead is the rise of too-big-to-fail, politically connected and market-dominant health care systems.

We'll see.

Image from NIHSeniorHealth

The Evidence Supporting Heart Failure Care Management

"Yawn!"

In the earliest days of the national trade association "disease management" meetings, the Population Health Blog encountered so many sessions and abstracts on heart failure rehospitalization-reduction programs, that it was almost boring

They all involved some version of risk stratification and a combination of telephonic and in-person nurse-based care management. 

And they all worked.

Which is why the Annals of Internal Medicine could have saved itself a lot of time and effort by simply asking the PHB for a summary.  Instead, it did the next best thing and published this meta-analysis by Feltner et al.  The authors pooled the data from 47 randomized clinical trials and found that both in-person and telephonic nurse-led disease care management reduced readmissions to a statistically significant degree.

Takeaways:

1. This is another example of old news not making reaching the elite ruling classes of Academikstan until well after the fact.

2. For my colleagues in the medical home movement, take note: achieving financially relevant outcomes will depend on focusing care management where it will have greatest impact. Instead of managing all patients with heart failure (for example), start by managing the patients at risk of (re) hospitalization.  That's where the return-on-investment gold can be mined.

Image from Wikipedia

Discovering What We Don't' Know About Risk-Adjustment for Hospital Readmission Rates in Medicare

Something like this?

When the Population Health Blog agreed with the spouse that it was time to replace the living-room gas fireplace insert with something more sleek and modern, it then turned its attention to changing the surrounding mantle. The PHB favored something heraldic, featuring partially-garbed warrior babes, sporting shields and sandals. Cherubs too.  Preferably oak.

After some counseling from the PHB spouse, it came to realize that its wayward tastes in interior design may be a function of going sans helmet during its childhood bicycle riding, its deepening appreciation of bourbon's mysteries and pausing too frequently on Fox News' The Kelly Files. 

Naturally, the PHB wants to know the relative influence of each. Increasing exposure will help it propose some ideas for the unfinished basement.

Hospital administrators are dealing with a similar problem when it comes to readmissions.

Approximately 20% of discharged Medicare beneficiaries come back within 30 days. In response, CMS financially penalizes hospitals with high readmission rates for heart attack, heart failure and pneumonia. To reduce that penalty, hospitals have asked about the quality of their care, discharge planning and follow-up outpatient care. 

But, what is the relative impact of each? Where should administrators focus their corrective actions? 

Or, like the PHB and interior design, are readmissions ominously outside of anyone's control?

According to some interesting research, it turns out that more than half of the variation in readmissions may be outside of hospitals' control.  What's worse, CMS doesn't account for that in its calculation of the penalty that uses patient factors, such as age, gender and illness burden.

That's the conclusion of this recent article appearing in HSR Health Services Research.

Herrin and colleagues correlated CMS's Hospital Compare readmission data with each hospital county's socioeconomic data (rural vs. urban, persons living alone, employment status and educational level), access to care (the per capita density of primary care and specialist physicians as well as hospital beds) and nursing home number and quality (the number beds and the number of high-risk, long-term patients with bed sores).

Based on risk-adjusted rates from 4,079 hospitals in 2,254 counties, the authors found that more half of the variation in hospital readmissions was statistically explained by the counties' data.  That included persons living alone, low educational attainment, urban setting, a higher number of Medicare beneficiaries, fewer primary care physicians, fewer nursing home beds, higher numbers of nursing home patients with bed sores.  More beds and more specialist physicians were also independently associated with higher readmission rates.

The Population Health Blog's take?

As it noted previously, much of the vituperation around the unexplained variation in health care has been less a function of an inefficient health care system and more a function of our inability to identify the underlying drivers of utilization.

And now we're getting better. The HSR article shows that when it comes to readmissions, much of that variation is a reflection of the poverty in our neighbors' homes as well as the strength of the primary care network and the ability of nursing homes to act as a cushion.

Hopefully the mandarins at CMS will take these findings into account as they continue to financially sanction hospitals for readmissions. A more sophisticated approach to risk adjustment could help lessen the budgetary impact of county-level factors that are outside the hospital administrators' control. 

And since hospitals' bottom lines typically reflect the populations they serve, better risk adjustment could also lessen the disparate impact on the nation's poorest hospitals.

Image from Wikipedia

A Scoring System to Predict Hospital Readmissions

Knowing, based on this paper, that the readmission rate to U.S. hospitals is as high as 20%, you and your colleagues decide to implement a readmissions prevention program."  Your state-of-the-art plan includes evidence-based interventions such as frequent telephone calls, nurse home visits, telemonitoring, referral to community programs and close coordination with the outpatient physicians.

Your problem, however, is that the "reach" of your program is limited.  With only a limited budget with a limited number of nurses, you can't afford to call, visit, telemonitor, refer and coordinate every patient discharge. 

You wish you could focus on the highest risk patients.

Jacques Donzé et al to the rescue.

As the Disease Management Care Blog understands it, this team of researchers retroactively looked at one year's worth of medical service discharges from Boston's Brigham & Women's by dividing them into 3 groups: 1) no readmission within 30 days, 2) an “unavoidable” readmission within 30 days (for a new unrelated condition or a planned return to the hospital, like another round of chemotherapy for cancer) and 3) an “avoidable” readmission.  The initial sorting was done using a computer algorithm followed by a chart-review that confirmed the sorting.

Then the researchers discarded the "unavoidable readmission group" and compared the “avoidables” to the "no readmission" group.  Logistic regression, based on a total of 9212 patients, was used to find the independent “signals” that were statistically and independenly associated with the avoidable readmission group: in other words, what features did they have that the no-readmission group didn’t have?

Some features had a stronger “signal” and therefore warranted a greater weight, which was reflected in a point scoring system. The authors cleverly dubbed it the HOSPITAL Score:

Low hemoglobin level at discharge (less that 12 g/dL) ...1 point (H)

Discharge from an oncology service... 2 points (O)

Low sodium level at discharge (135 mEq/L)... 1 point (S)

Procedure during hospital stay (any ICD-9-CM coded procedure)... 1 point (P)

Index admission type: nonelective... 1 point (I)

No. of hospital admissions during the previous year 0... 0 points, 1-5... 2 points, >5.. 5 points (A)

Length of stay >5 days ...2 points (L)

Then the point scores were arbitrarily stratified into 3 groups.  If the point score added up to 4 or less, that was a “low” risk group, while 5-6 points was 'intermediate' risk group and 7 or more points was 'high' risk.

If your score was low: you had a 5.2% chance of a 30 day readmission

Intermediate: 9.8% chance of a 30 day readmission

High risk: 18.3% chance of a 30 day readmission

The DMCB's take:

1. This was a Boston academic medical system with a high readmission rate of 22%.  The results may not be applicable to settings such as this with a readmission rate of 8%.

2. There is no information on  the "planned-unavoidable" readmissions; the DMCB doesn't know how the HOSPITAL score works on predicting readmissions for an unrelated condition.

3. The study is limited to "medical service" readmissions.  There is no information on the use of this scoring system for patients being discharged after surgery.

4. Keep in mind that Medicare’s readmissions program is based on patients with heart attack, heart failure and pneumonia.  While Medicare patients with those diagnoses were included in this study, this research didn’t focus on those particular conditions in Medicare. That means the DMCB doesn’t know if HOSPITAL will adequately predict readmissions in this key payor group.

5. It’s counter-intuitive, but some of the “signals” are associated with readmissions don’t necessarily cause them.  A casual observer might think that correction of anemia or a low blood sodium level would lead to a lower rate of readmissions. Not so. Rather, anemia of chronic disease and a low sodium level have been known for years to happen in chronically sick fragile patients.  It’s the fragility, not the labs.

6. This shows what readmission prevention programs are up against. Among the high risk patients, the algorithm only correctly spots 18%.  So, if you commit a nurse case manager to go after all patients with a score of 7 points or more, 80% are destined to not be readmitted anyway.

7. That being said, this is an evidence-based study that represents an important advance in indentifying patients at high risk for readmission, using a simple point system for information that is typically available at the time of discharge.

The DMCB likes it.

The Hospital Readmissions Reduction Program: Cautions and Caveats

"Maybe you should go
back to the hospital!"

Ask most wonks - especially ones who never took care of a patient - about "readmissions," and, after quoting this article, these health policy Urkels will tell you that returning to a hospital is the poster-child of all that ails U.S. medical care. Providers who can't get it right the first time, they say, are not only giving slipshod care, but are double dipping because their mistakes generate even more fat fees the second time around.

"Balderdash!" says the Disease Management Care Blog. Many Medicare inpatients are so sick that it's a miracle that they get to go home in the first place.  Keeping patients in the hospital can be more life-threatening than the home environment and, when things don't get well after a discharge, it's often more a function of social support than medical skill. 

That doesn't mean that CMS is going to listen to docs and back off of its Hospital Readmissions Reduction Program (HRRP). Using risk-adjusted actuarial projections, every U.S. hospital will be prone to a possible payment reduction if their observed rate of readmissions for heart attack, heart failure, and pneumonia exceeds the expected rate. Based on those projections, approximately two thirds of hospitals could be penalized.

Writing in the New England Journal of Medicine, Karen Joynt and Ashish Jha point out that hospitals are concerned because 1) readmissions fall outside of their control and 2) the actuarial projections are imperfect.  As a result, hospitals that care for the most fragile and socioeconomically disadvantaged are at risk for paying more than their fair share of CMS's $280 million claw-back penalty. 

The NEJM authors recommend three modifications to CMS' HRRP:

1. Include patients' socioeconomic status in any risk adjustment modeling. One easy-to-obtain modifier, for example, could be whether the patient is on Supplemental Security Income.  Patients on SSI are less able to cope, which is why they quality for the program in the first place.

2. Include hospitals' mortality rates in any risk adjustment modeling.  Hospitals with special expertise are less likely to have borderline patients die on their inpatient services, which means they'll have their more than their fair share of fragile survivors.

3. Limit the penalty to readmissions that occur within hours or days of a discharge, instead of the current problematic policy of counting any readmission that occurs within 30 days.  It makes sense to believe that a premature discharge or slipshod discharge planning is at fault if the patient returns within 3 days instead of three weeks.

Since it's unlikely that HRRP program is going away, the DMCB agrees with the three recommendations.  In the meantime, it also suggests:

1. CMS should be held accountable by Congress to execute well on the program,

2) Claims analytics - possibly using a "Big Data" approach - should be applied to Medicare claims to examine whether hospitals are turning to two potential options to undermine the program:

a) gaming the system by altering how they "code" the billing for their readmission patients, or

b) accepting the penalty because of favorable income from readmissions.

Image from Wikipedia