Health for life

This way to consolidation....

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

According to the finalized regulations, if a hospitals 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 hospitals 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, Washingtons 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.

Well see.

Image from NIHSeniorHealth

A physician welcoming the patient back to the hospital

Years ago, the Disease Management Care Blog had to readmit a recently discharged elderly patient back to the hospital.  It paged the colleague who originally discharged the patient and she offered up an important insight: the patient was so ill and feeble that it was remarkable that the patient made it out of the hospital in the first place.

Later, when the tut-tuting hospital quality assurance nurses started to swarm, the DMCB provocatively informed them that readmissions were the price of doing business.

Thats why the DMCB liked this New England Journal of Medicine article that questions the overall wisdom of Medicares national focus on reducing readmissions. Authors Karen Joynt and Ashish Jha make some good points:

1. What is the evidence? While a "readmission" is widely viewed as a failure of not having gotten it right the first time, in-depth chart reviews of readmitted patients reveal that only 12% - 25% are truly preventable.

2. Multiple causes: Not getting it right the first time is less of a cause than absent families, poor community supports and lingering poverty.

3. Death is the most effective solution: Hospitals that perform well in keeping patients with end-stage illness alive are ironically destined to have higher readmission rates.

4. Priorities: Trying to reduce readmissions will consume hospitals time and resources better spent on other patient safety initiatives. 

Drs. Joynt and Jha have two good recommendations:

1. Focus on those readmissions occurring within 3-7 days.  Those patients are more likely the victims of poor discharge planning that is under the hospitals control.

2. Alter diagnosis related group payments to include a "warranty" that covers the likelihood of readmission within a few days of discharge.

And, as is common among the health care academia, one good recommendation was missed:

3. Medicare should learn how to adopt, incent and pay for "best practices" from population health management interventions like this and this that lead to meaningful decreases in readmissions for the patients who are at greatest risk.  Whats more, the DMCB believes that if hospitals (and their spawn, ACOs) can "outsource" this to a third party who can provide this on a turnkey basis, they can better devote their attention to other critical patient safety needs.

"I  think I need to go back to the hospital....."

The Disease Management Care Blog remembers her very well. She had come to the United States after escaping on foot from post-war Hungary.  She liked to have sliced pears and cognac for lunch. As her health began to fail, her stubbornness began to exceed her self-sufficiency, leading to multiple hospitalizations. One time, despite making sure she had a post-discharge outpatient appointment, a fresh pile of prescriptions and home visiting nurse services, her illness flared and she had to come back to hospital.

She was not only my patient, but she represented one of Medicares dreaded readmission statistics.

By now, DMCB readers know that CMS, buoyed by its value-based purchasing program, has targeted readmissions by reducing payment levels to hospitals that fall outside the expected norm. Threatened by the loss of income, it’s assumed that hospitals will respond by developing higher quality discharge planning and care programs that keep patients from having to come back.

An important part of reducing readmissions is to identify those patients that are at greatest risk. That would help on two levels:

1) if a hospital had more than its fair share of patients at risk, it could argue that an increased number of readmissions is the result of a sicker patient population and not quality of care. As a result, the hospital could be held "harmless;

2) hospitals would be able to focus extra care resources on those patients who are spotted early as likely to come back, thereby reducing the readmission rate.

In other words, patients like the lady from Hungary would not necessarily lead to a cut in hospital payment rates and, for example, she could be proactively given extra care, such as a doctor appointment within 48 hours, a week’s supply of free medications and twice a day home nurse visits.

Which is why this just published JAMA article "Risk Prediction Models for Hospital Readmission" by Devan Kansagara, Honora Englander, Amanda Salanitro David Kagen, Cecelia Theobald, Michele Freeman and Sunil Kripalani is important.  The authors set out to see what the evidence-based published scientific literature had to say about predicting readmissions.   They filtered thousands of erences, reviewed 286 publications found 30 rigorous studies that described 26 models. 

To the DMCBs delight, the authors applied a “c statistic” to the 30 publications to assess a wide variety of retrospective and concurrent prediction methodologies using a host of data inputs such as age, gender and past diagnoses. According to this article, the c (or "concordance") statistic measures how well a test can predict the presence or absence of a "condition" which, in this case, was being readmitted to the hospital.  One way to think of this is the likelihood of correctly identifying a condition when there are two people, one with it and one without it.  If the likelihood is 50%, thats no better than random guessing.  If its 100%, thats perfect.  By the way,if this sounds a lot like the area under the receiver operator curve, youre right.

And what did the all-seeing "c statistic" say?  All of the published models had disappointingly similar levels of performance that ranged between the extremes of .52 to .83 with most in the .50 to .7 range. Whats more, only one study examined the most important question of all: is it possible to find patients with preventable readmissions?

What does the DMCB think?

1) This may be another area where national health policy has gotten out in front of the scientific evidence.  If we cant reliably assess or predict readmissions with sufficient accuracy, there is a distinct likelihood that statistical variation, not quality of care, will lead to some hospitals being victimized by CMS with lower payment rates. Whats more, if hospitals cant tell which patients are likely to come back, how are they supposed to target their expensive care management programs at those who are most likely to benefit?

2) There are undoubtedly some proprietary predictive models that havent been reported in the literature that claim to have higher levels of accuracy.  Yet, without the scrutiny of successful peer-reviewed publication, itd be difficult to believe that theyre really any better than the mainstream published range of .5 to .7.  The next time the DMCB runs into one of these outfits, its going to ask about the "c statistic" and if they havent published their results, why not.

3) Last but not least, while the hospital payment rates are being held hostage by CMS, its the doctors that are making the call on readmissions based on the best interest of their patient.  The c statistic suggests that that will be the most important determinant in the readmission rate.

"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 cant 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 its 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 dont get well after a discharge, its often more a function of social support than medical skill. 

That doesnt 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 CMSs $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 theyll 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 its 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