Readmissions

Screen Shot 2014-04-28 at 5.47.28 PMPotentially preventable readmissions are a scourge on the US healthcare system.

Each year millions of patients are discharged from the hospital, only to return within 30, 60, or 90 days.

Not only do patients, their families, and their caregivers suffer as a result, but hospitals, insurers, and the government waste billions of dollars that could be spent on other public health priorities. Many if not most of these readmissions could have been avoided if clinicians had effective, scalable, and timely methods for identifying not only which patients were the highest risk, but what steps should have been taken to mitigate that risk.

In recent years there has been a proliferation of readmission risk assessment models, yet readmission rates have barely budged. Fundamental flaws exist in most approaches in the areas of Data, Model Adaptability and Clinical Workflow Integration.

Many tools rely solely on historical patient data mined from the EHR or are disease-specific models that cannot be scaled to address all readmissions challenges. Models that rely on data collected at discharge are not timely enough to enable clinicians to take meaningful action, and ones that are not well-integrated into clinical workflow are not easily adopted.

For a readmission risk assessment tool to achieve a meaningful and long-lasting impact, these common pitfalls must be avoided at all costs. Today, I’m going to address some of the many data challenges faced when trying to risk assess patients.

Historical Data does not Predict Future Readmissions

Anybody who has ever invested in the stock market, rooted for a local sports team, or stuck with a television show past its tenth season knows that past performance gives you no guarantee on future returns. Factors beyond our control and beyond our ability to predict may cause our fortunes to turn on a dime.

Consider the Dow Jones Industrial Average: Those who had any investments around July of 2007 remember the feelings of unabashed optimism and certainty inspired by the great bull run of the early 2000s. Unfortunately, those same investors also most assuredly remember what happened shortly thereafter, when the financial crisis of 2008 erased trillions of dollars’ worth of wealth.

A recent systematic review of readmission risk models concluded that many hospitals still model their approach to identifying high-risk patients based on historical admissions, claims data, and outdated information on patient populations [1].

Using these old data to model and predict readmissions is dangerous. And with increasing pressure on hospitals to reduce readmissions, this approach also runs the risk of becoming extremely costly. Just ask the guy who splurged on Brooklyn Dodgers tickets in 1958, or the guy who put all his money into 8-track cassettes in 1979, or the guy who started a Hummer dealership in 2005.

Any of these folks will tell you that past performance data can not only betray you, but it may also prevent you from recognizing the obsolescence of your sources. As a result, this data may cost you a fortune.

Continue reading “Why a Majority of Readmission Risk Tools Fail in Practice”

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In 1980, while working at the University of Chicago Pritzker School of Medicine, I wrote an article for the Harvard Business Review entitled “The Health Care Market: Can Hospitals Survive?”. This article, and the book which followed, argued that hospitals faced a tripartite existential threat:

1)  ambulatory technologies that would enable physicians to compete successfully with hospitals at lower cost in their offices or freestanding settings, 2)  post-acute technologies that would enable presently hospitalized patients to be managed at home and 3) rapidly growing managed care plans that would “ration” inpatient care and bargain aggressively to pay less for the care actually provided.

I predicted a significant decline in inpatient care in the future, and urged hospitals to diversify aggressively into ambulatory and post acute services.   Many did so.  A smaller number, led by organizations like Henry Ford Health System of Detroit and Utah’s Intermountain Health Care, also sponsored health insurance plans and became what are called today “Integrated Delivery Networks” (IDN’s).

In the ensuing thirty years, US hospital inpatient census fell more than 30%, despite ninety million more Americans.   However, hospitals’ ambulatory services volume more than tripled, more than offsetting the inpatient losses; the hospital industry’s total revenues grew almost ten fold.

Ironically, this ambulatory care explosion is now the main reason why healthcare in the US costs so much more than in other countries.  We use far fewer days of inpatient care than any other country in the world.  But as the McKinsey Global Institute showed in 2008 ambulatory spending accounts for two thirds of the difference between what the US spends on healthcare and what other countries spend, far outstripping the contribution of higher drug prices or our multi-payer health financing system.

Continue reading “Can Hospitals Survive? Part II”

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When Barack Obama was merely a senator running for the White House, he told one physician association, “I support the concept of a patient-centered medical home” and would encourage the model if he ever became president.

Six years later: Mission accomplished.

Nearly 7,000 primary care practices have officially been accredited as PCMHs, and thousands of other providers have adopted some features of medical homes, which use a team-based approach to coordinated care. And while the movement toward medical homes might have evolved without Obama, his health reforms clearly laid the groundwork for rapid adoption.

The only problem? There’s still no clear evidence that the model even works.

A prominent Journal of the American Medical Association study last month found that after three years, one of the nation’s largest medical home pilots didn’t lead to lower costs or significantly higher care quality.

“There are folks who believe the medical home is a proven intervention that doesn’t even need to be tested or refined,” lead study author Mark Friedberg told the Wall Street Journal‘s Melinda Beck. “Our findings will hopefully change those views.”

An accompanying editorial also sounded caution. “It is time to replace enthusiasm and promotion with scientific rigor and prudence,” Thomas Schwenk wrote, “and to better understand what the PCMH is and is not.”

Continue reading “Obamacare’s Payment Pilots Are Struggling To Prove They Work. Here’s Why It’s OK.”

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Why readmission penalties are controversial

Penalizing hospitals for high readmission rates has been pretty controversial.  Critics of the program have argued that readmissions have little to do with what happens while the patient is in the hospital and are driven primarily by how sick or how poor the patient is.  Advocates of the readmissions program increasingly acknowledge that while readmissions may not reflect the quality of care that occurred within the hospital, someone should be accountable for what happens to patients after discharge, and hospitals are the logical choice.  While the controversy continues, there is little doubt that the metric is here to stay.  This October, the CMS Hospital Readmissions Reduction Program (HRRP) will increase its penalty on excess readmissions from 1% to 2% of total hospital reimbursement.

So far, CMS has focused on readmissions that occur after patients are discharged with one of three medical conditions—acute myocardial infarction, pneumonia, and congestive heart failure.  The data on the impact of the program are mixed:  while readmission rates appear to be dropping, the penalties seem to be targeted towards hospitals that care for some of the sickest patients (academic medical centers), poorest patients (safety-net hospitals) and for heart failure, some of the best hospitals (those with the lowest mortality rates).  No wonder the program has been controversial.

Why surgery may be different

In 2015, CMS extends the program to focus on surgical conditions, which provides an opportunity to think again about what readmissions measure, and what it might take to reduce preventable ones.  And if you think about it, surgery may be different.  Most patients who are admitted for Acute MI, CHF, and pneumonia are chronically ill and bounce in and out of the hospital, with any one hospitalization likely just an exacerbation of underlying chronic illness (especially true for pneumonia and heart failure).  Not so for surgery—at least not for the major surgeries.

Continue reading “Finally Some Good News on Readmission Rates”

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While working to develop a broad set of outcome measures that can be the basis for attaining the goals of public accountability and information for consumer choice, Medicare should ensure that the use of performance measures supports quality improvement efforts to address important deficiencies in how care is provided, not only to Medicare beneficiaries but to all Americans.

CMS’ current focus on reducing preventable rehospitalizations within 30 days of discharge represents a timely, strategic use of performance measurement to address an evident problem where there are demonstrated approaches to achieve successful improvement [6]. Physicians and hospital clinical staff, if not necessarily hospital financial officers, generally have responded quite positively to the challenge of reducing preventable readmissions.

CMS has complemented the statutory mandate to provide financial incentives to hospitals to reduce readmission rates by developing new service codes in the Medicare physician fee schedule that provide payment to community physicians to support their enhanced role in assuring better patient transitions out of the hospital in order to reduce the likelihood of readmission [7]. CMS recently announced that after hovering between 18.5 percent and 19.5 percent for the past five years, the 30-day all-cause readmission rate for Medicare beneficiaries dropped to 17.8 percent in the final quarter of 2012 [8], simplying some early success with efforts to use performance measures as part of a broad quality improvement approach to improve a discrete and important quality and cost problem.

However, this Timely Analysis of Immediate Health Policy Issues 3“CMS’ current value-based purchasing efforts, requiring reporting on a raft of measures of varying usefulness and validity, should be replaced with the kind of strategic approach used in the national effort to reduce bloodstream infections.”approach is not without controversy.

Improvements have been modest, and some suggest that readmission rates are often outside the hospital’s control, so CMS’ new policy unfairly penalizes hospitals that treat patients who are the sickest [9]. And while readmission in surgical patients is largely related to preventable complications, readmissions in medical patients can be related to socioeconomic status. Also, some have questioned the accuracy of CMS’ seemingly straightforward readmission rate measure, finding that some hospitals reduce both admissions and readmissions—a desirable result—yet do not impact the readmission rate calculation [10]. And one of this paper’s authors (R. Berenson) has suggested a very different payment model that would reward hospital improvement rather than absolute performance, thereby addressing the reality that hospitals’ abilities to influence readmission rates do vary by factors outstside of their control [11].

Continue reading “2. Use quality measures strategically, adopting other quality improvement approaches where measures fall short.”

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Critically ill Medicare patients, who are battling for stable health at the end of life, are victims of repeated hospitalizations, especially after being discharged to a skilled nursing facility (SNF).  The cycle of hospitalizations is an indicator of poor care coordination and discharge planning – causing the patient to get sicker after every “bounce back” to the hospital.  Total spending for SNF care was approximately $31 billion in 2011; with an estimated one in four patients being re-hospitalized within thirty days of discharge to a SNF.[1]

Each readmission leads to further test and treatments, higher health care costs, and most importantly, patient suffering.  It is hard to imagine that patients would prefer to spend their last few months of life shuttling from one healthcare setting to another and receiving aggressive interventions that have little benefit to their quality and longevity of life.  The heroic potential of medical care should not compromise the patient’s opportunity to die with dignity.   A hospital is not a place to die.

Medicare beneficiaries are eligible to receive post-acute care at SNFs, after a three day hospital admission stay.  SNFs provide skilled services such as post-medical or post-surgical rehabilitation, wound care, intravenous medication and necessities that support basic activities of daily living.  Medicare Part A covers the cost of SNF services for a maximum of 100 days, with a co-payment of $148/day assessed to the patient after the 20th day.  If a patient stops receiving skilled care for more than 30 days, then a new three day hospital stay is required to qualify for the allotted SNF care days that remain on the original 100 day benefit.  However, if the patient stops receiving care for at least 60 days in a row, then the patient is eligible for a new 100 day benefit period after the required three day hospital admission.[1]  It is evident that the eligibility for the Medicare SNF benefit is dependent on hospitalizations – many of which may be a formality and a source of unnecessary costs.

Continue reading “The Bounce Back Effect”

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In the past, neither hospitals nor practicing physicians were accustomed to being measured and judged. Aside from periodic inspections by the Joint Commission (for which they had years of notice and on which failures were rare), hospitals did not publicly report their quality data, and payment was based on volume, not performance.

Physicians endured an orgy of judgment during their formative years – in high school, college, medical school, and in residency and fellowship. But then it stopped, or at least it used to. At the tender age of 29 and having passed “the boards,” I remember the feeling of relief knowing that my professional work would never again be subject to the judgment of others.

In the past few years, all of that has changed, as society has found our healthcare “product” wanting and determined that the best way to spark improvement is to measure us, to report the measures publicly, and to pay differentially based on these measures. The strategy is sound, even if the measures are often not.

Continue reading “Measuring the Quality of Hospitals and Doctors: When Is Good Good Enough?”

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You’d be forgiven if, after reading last month’s Health Affairs, you came to the conclusion that all manner of wellness programs simply will not work; in it, a spate of articles documented myriad failures to make patients healthier, save money, or both.

Which is a shame, because – let’s face it – we need wellness programs to work and, in theory, they should. So I’d rather we figure out how to make wellness work. It seems that a combination of behavioral economics, technology, and networking theory provide a framework for creating, implementing, and sustaining programs to do just that.

Let’s define what we’re talking about. “Wellness program” is an umbrella term for a wide variety of initiatives – from paying for smoking cessation, to smartphone apps to track how much you walk or how well you comply with your plan of care, and everything in between. The term is almost too broad to be useful, but let’s go with it for now.

When we say “Wellness programs don’t work,” the word work does a lot of, well, work. If a wellness program makes people healthier but doesn’t save lives, is it “working”? What if it saves money but doesn’t make people healthier?

Continue reading “Wellness Programs Aren’t Working. Three Ideas That Could Help.”

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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. My physician colleagues used to refer to this as “treat, then street.”

Unfortunately, discharging patients too soon can result in readmissions.  That’s why I have 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?

Continue reading “Building Smarter Hospitals: The Widely Misunderstood Relationship Between Discharging Patients Too Early and the Likelihood of a 30 Day Readmission”

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I’ve been getting emails about the NY Times piece and my quotation that the penalties for readmissions are “crazy”.  Its worth thinking about why the ACA gets hospital penalties on readmissions wrong, what we might do to fix it – and where our priorities should be.

A year ago, on a Saturday morning, I saw Mr. “Johnson” who was in the hospital with a pneumonia.  He was still breathing hard but tried to convince me that he was “better” and ready to go home.  I looked at his oxygenation level, which was borderline, and suggested he needed another couple of days in the hospital.  He looked crestfallen.  After a little prodding, he told me why he was anxious to go home:  his son, who had been serving in the Army in Afghanistan, was visiting for the weekend.  He hadn’t seen his son in a year and probably wouldn’t again for another year.  Mr. Johnson wanted to spend the weekend with his kid.

I remember sitting at his bedside, worrying that if we sent him home, there was a good chance he would need to come back.  Despite my worries, I knew I needed to do what was right by him.  I made clear that although he was not ready to go home, I was willing to send him home if we could make a deal.  He would have to call me multiple times over the weekend and be seen by someone on Monday.  Because it was Saturday, it was hard to arrange all the services he needed, but I got him a tank of oxygen to go home with, changed his antibiotics so he could be on an oral regimen (as opposed to IV) and arranged a Monday morning follow-up.  I also gave him my cell number and told him to call me regularly.

Continue reading “Is the Readmissions Penalty Off Base?”

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