A Presidential Politics-Free Health Wonk Review

Welcome to the Health Wonk Review, a compendium of the latest insights from more than two dozen health policy blogs. Each HWR issue is hosted at a different participant's blog, with topics that include health policy, delivery infrastructure, pharma, insurance and information technology.

Your HWR host, the Population Health Blog, uses a skeptical physician's perspective to write about "systems" of care.  Lately, it has focused on mHealth interventions that influence clinical and economic outcomes at a "population" level, as well as the effective governance of health enterprises.

 

It's also been a proud HWR participant for more than eight years.

The PHB is pleased that NONE of this issue's participants chose to mention any of the appalling lead candidates for U.S. President. Readers could use a break from the campaign cacophony, so the PHB welcomes you to the  Presidential Politics-Free Health Wonk Review.

The Affordable Care Act - What are the numbers?

Charles Gaba of ACASignups has been tracking the progress of the Affordable Care Act. This ongoing labor of love led him to comb through too-numerous-to-count public domain sources to provide an original-sourced summary (with links galore) of the health insurance status for the entire U.S. population in one chart.  He calls it "ambitious."  The PHB calls it gloriously detailed, credible and superb. KHN, you've met your match.

Medicaid

Hank Stern of the InsureBlog reminds us that Medicaid fails to meet the true definition of "health insurance." While beneficiaries get their health bills covered, this payment system is a government program that is ultimately paid for by taxpayers. As this form of income redistribution program expands, the opportunity for the "real" commercial insurance market dims. Ʀєfùsєηíκ indeed!

#mHealth - or the PHB is going to need an app to manage all its patients' apps.....

Peggy Salvatore of the Health System Ed Blog provides a summary of the ePharma Summit 2016 and regales readers with descriptions of how eHealth is helping persons who have gastrointestinal disorders, cancer or complex medication regimens be placed at the center of care.  "eHealth" is reaching critical mass without the help of any government mandates or meaningful use requirements. Imagine that.

David Harlow of the HealthBlawg takes a bite of Apple's CareKit Platform by unpacking the first app entrant from Iodine dubbed "Start."  Start promises to help users to individually manage both the benefits and side effects of anti-depressant medications. The app relies on a validated depression survey to assess progress, promising to take the guesswork out of treatment.

Outcomes

Brad Flansbaum of The Hospital Leader not only summarizes "the best (peer-reviewed) study on (hospital) readmissions to date," but interviews the lead author. As many have suspected, a significant proportion of preventable readmissions are outside the control of the institution and practically all of the current public-reporting measures fail to take that into account. Two insights are that 1) readmission rates will never go to zero, nor should they and 2) innovative interventions to minimize the risk of readmission are just now being developed. The PHB predicts that soon, no at-risk patient will leave the hospital without a dedicated app and telehealth-linked handheld device.  Given the dollars at stake, perhaps those patients without handhelds should be given one.....  

Pharma Misbehavior

Roy Poses from Health Care Renewal pulls aside the curtain and exposes the persons ultimately responsible for the OxyContin fiasco. Members of Purdue Pharmaceutical's C-suite had to pay hefty fines for the company's allegedly misleading advertising, but the upstream owners seem to have escaped scrutiny with their gazillions intact. If any of this is true, we've learned nothing about combatting corporate misdeeds.

Health Savings Accounts

Jay and Louise Norris of the Colorado Health Insurance Insider Blog take a look at some of the arcana and paranoia emerging around health savings accounts (HSAs).  First the arcana: HHS has a BPP about the HSA designation from QHPs that have otherwise been contrived to get around other regulations, likely promulgated in other BPPs. The paranoia is from wary conservatives, who are wondering if the liberals are unable to limit themselves to just "the nine words" by using BPPs to ultimately undermine HSAs.  What could possibly go wrong?  

Dual Eligibles

Tom Lynch of Worker's Comp Blog reviews the history of the successful Commonwealth Care Alliance.  This non-profit HMO currently serves over 17,000 "dual eligibles" in Massachusetts; these persons have significant disabilities and therefore qualify for both Medicare and Medicaid.  Despite huge claims costs, this HMO has been ably served by leadership who understands how money and mission underlie successful health insurance.

A Minimum Wage A Day Keeps the Doctor Away

California's Anthony Wright of the Health Access Blog is not only unapologetic about his home state gradually increasing the minimum wage to $15, he argues that that level of income correlates with better insurability, out-of-pocket affordability, higher health status, improved social determinants and less need for Medicaid.  What's there not to like, especially since the 48 other states can see how this ultimately works out.

Drugs: You Don't Get What You Don't Pay For

David Williams of the Health Business Blog has some thoughts for the pharmaceutical industry's efforts to justify its drug pricing policies. He recommends that pharma not only embrace cost-effectiveness, but lead the fight to include that methodology in all things healthcare.  They also need to help the public understand that you don't get good stuff for free: someone has to pay.

Speaking of Drugs....

Joe Paduda of the Managed Care Matters blog attended the Rx Drug Abuse Summit and has posted some of the more scary data that was presented there. The vast majority of heroin users started with prescription opioid drug abuse and a lot of smart concerned people are mobilizing to address the problem.  Awareness is the first step in addressing this unmitigated disaster.

Food, er Flu Fight

And saving the best for last, in the Health Affairs Blog, Peter Doshi, Kenneth Mandle and Forence Bourgeois scrutinize the CDC's recent recommendations on the treatment of influenza with antiviral drugs. After contrasting the recommendations with the FDA's and others' more detailed analyses on the subject, the authors find the CDC's promotion of a drug of questionable effectiveness to be "problematic."  In academic speak, them's fighting words. This ain't over, so sit back and enjoy while the flu fur flies.  

Your next Health Wonk Review will be hosted by the Health System Ed blog on April 21.

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

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

Medicare Hospital Readmissions: Bad. Our Ability To Understand or Do Much About Them: Worse

"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 Medicare's 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 references, reviewed 286 publications found 30 rigorous studies that described 26 models. 

To the DMCB's 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%, that's no better than random guessing.  If it's 100%, that's perfect.  By the way,if this sounds a lot like the area under the receiver operator curve, you're 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. What's 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 can't 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. What's more, if hospitals can't 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 haven't been reported in the literature that claim to have higher levels of accuracy.  Yet, without the scrutiny of successful peer-reviewed publication, it'd be difficult to believe that they're really any better than the mainstream published range of .5 to .7.  The next time the DMCB runs into one of these outfits, it's going to ask about the "c statistic" and if they haven't published their results, why not.

3) Last but not least, while the hospital payment rates are being held hostage by CMS, it's 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.

Healthcare Reform, Readmissions and the Contribution of Disease Management: The Rest of the Story

Did you know that flu shots are effective in the workplace? Or that exercise can reduce the incidence of falls among the elderly? Or that care management can reduce post-discharge rehospitalizations? All three breakthroughs were reported more than ten years ago, yet recent news reports from the Wall Street Journal (‘unusual!’) and National Public Radio (‘a third way!’) are recycling the old news about rehospitalizations with new anecdotes about clinical programs that can successfully reduce readmissions.

[Yawn] The science has been pretty clear on the topic since Michael Rich’s seminal 1996 publication. Other researchers (for example, here and here) have individually confirmed post-discharge care management works quite well, while this review of the peer reviewed literature suggests there are critical ingredients that can help this be state-of-the-art.

Given the recent renewed interest in reducing rehospitalizations since a) Stephen Jenck’s recent New England Journal article (20% readmission rate among Medicare beneficiaries along with the potential of saving a whopping $17 billion), b) the inclusion of readmission data in CMS’ Hospital Compare website, c) post discharge guarantees by an innovative integrated delivery system's health plan and, last but not least, d) proposed bundled hospital and follow-up payments as a part of health reform, the topic is certainly timely and important.

With their renewed interest, hospital administrators and policy-makers agree that these kinds of care programs haven’t taken root in day-to-day fee-for-service clinical practice and the Medicare benefit because insurers don’t ‘cover’ hospitals’ post-discharge care programs. In the meantime, current health reform proposals seem to be tilting toward penalizing hospitals with a stick of high readmission rates. The DMCB can’t tell if the proposed bundling methodology described above will adequately include 'the carrot' of covering the cost of hospital-sponsored post-discharge care coordination.

[Yawn]. Too bad the media is missing out on telling the rest of the story.

We already know that a host of commercial and employer-sponsored insurers include disease management programs. What isn’t being mentioned is that a standard contractual feature in practically all disease management programs is to provide post-discharge follow-up to patients with the key chronic conditions like heart disease and heart failure. That’s why most of the literature on post-discharge readmission avoidance programs typically refer to them as ‘disease management’ in the first place. In other words, the industry has long since developed a viable business model based on this need. It has already entered hundreds of thousands of recently discharged patients in its decade-old post-hospitalization care programs. And they can work.

While most of the reports in the literature describe research-funded ‘hospital-based programs,’ the DMCB suspects the DM organizations have not gotten around to describing their experience in the peer review literature. They're certainly not being called by the Wall Street Journal or NPR. Pity.

That being said, the key ingredients - patient engagement and coaching with monitoring, self-care and close coordination, usually performed by nurses – can be independent of the location and doesn't necessarily have to be hospital based. Until there is good research that says otherwise, the DMCB doesn’t think it makes any difference how the nurse care is funded, just so long as it’s done one way or another. It would appear the 'fit' is better with disease management because of its outpatient focus, its ability to include this with all its other programs and its successful track record. Hopefully, the Patient Centered Medical Home (PCMH) will eventually demonstrate success in this area also.

Hopefully, the architects of health care reform will recognize that when it comes to readmissions, a ten year old disease management solution is already at hand.