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