The Blame Game of Hospital Readmissions

After completing treatment at a hos­pital, the last thing people want to do is return. Yet between June 2009 and June 2010, the national rate of 30-day readmission was an astonishing 58.2 per­cent. Doctors were puzzled by why patients were returning with the same or new prob­lems. Many suspected that readmission rates were linked to the quality of care in hospitals.

In response, hospitals have pointed out that there are a number of factors beyond quality of care that might explain the high frequency of readmission. Many of these extraneous factors are not within hospital control. Patients of low socioeconomic sta­tus, for example, are more likely to fall vic­tim to untreated diseases and unnecessary procedures. As a result, their chances of being readmitted are significantly greater.

Connecticut Veterans Affairs West Haven Campus. Courtesy of
Connecticut Veterans Affairs West Haven Campus. Courtesy of Veteran’s Affairs.

So when a group of Yale scientists in 2008 chose to evaluate hospitals based solely on readmission rates, without adjusting for the socioeconomic status of patients, their decision drew outrage from many hospi­tals that treat large populations of the poor. The Yale physicians, led by Dr. Harlan M. Krumholz, maintain that their decision was in the best interest of patients, and re­cent studies support their claim.

As hazy as the data may be, these con­flicting opinions make one point exceed­ingly clear: Hospital readmission is a complicated public health issue. Affecting positive change will likely require input from researchers, providers, and patients alike.

Straining safety net hospitals

The Affordable Care Act (ACA) passed in 2010 established the Readmissions Re­duction Program (RRP) with the mission of raising quality of care in hospitals while cutting costs. In order to assess the progress of this mission, the Centers for Medicare and Medicaid Services (CMS) collaborated with the Center for Outcomes Research & Evaluation (CORE) at the Yale School of Medicine.

The duo created measures that compare the number of predicted readmissions to the number of expected readmissions at each hospital. The predicted number of re­admissions is based on a hospital’s perfor­mance given its mix of medical cases. The expected number, on the other hand, is based on national performance at the level of that hospital’s case mix. This approach is analogous to comparing observed and ex­pected readmissions. It allows a particular hospital’s performance to be compared to an average hospital’s performance with the same case mix.

The government imposes financial penal­ties on hospitals with higher than expected readmission rates. A total of 278 hospitals received the maximum penalty in 2012. Among the hospitals fined included Mount Sinai, NewYork-Presbyterian, and — ironi­cally enough — Yale-New Haven Hospital.

Department of Health and Human Services, where the Centers for Medicare and Medicaid Services are located. Courtesy of Wikimedia Commons.
Department of Health and Human Services, where the Centers for Medicare and Medicaid Services are located. Courtesy of Wikimedia Commons.

The big names aside, many safety net hospitals were also fined. Core safety net providers are defined by two character­istics: First, they must maintain an open door policy, accepting patients regardless of their ability to pay and offering nonex­clusive access to all health services. Second, a substantial number of their patients must be uninsured, on Medicaid, or otherwise financially vulnerable.

Spokespeople from some safety net hos­pitals argue that these institutions are be­ing unfairly targeted by the RRP because the measures do not consider the socio­economic status of their patients. Further­more, they believe that the payment pen­alties will compromise their ability to care for disadvantaged populations.

A recent study by Dr. Salomeh Keyhani at the University of California San Fran­cisco (UCSF), however, found that the addition of social risk factors does not alter hospital readmission. The work, re­cently published in The Annals of Internal Medicine, sampled 5,000 veterans admit­ted to a Veterans Affairs hospital in 2007 with a diagnosis of stroke. Researchers then developed three readmission models. The first was similar to the one written by Yale researchers; the second considered social risk factors; and the third consid­ered both social risk and clinical factors. Social risk factors included socioeconom­ic status, homelessness, and substance abuse. Clinical factors included disease severity, as well as the number of previous medical visits.

The UCSF study found that these so­cial risk factors have a negligible effect on model performance. Furthermore, they determined that social risk factors have little effect on the relative ranking of hospitals. They concluded, therefore, that clinical rather than social risk factors are the most important predictors of 30-day stroke readmission rates.

Yale responds

Krumholz, who serves as director of CORE, thinks that the study coming out of UCSF was helpful. However, he believes that Keyhani’s findings do not preclude the possibility that adjustment for socio­economic status could be important for particular institutions. Keyhani’s research does not resolve the debate, which Krum­holz believes should be centered on the best interest of patients.

Krumholz asserted that patients’ inter­ests have been his first priority from the very beginning. Before the Affordable Care Act, CORE had been working with CMS on process measures, which assess how health care professionals deliver services. “Increasingly, I was getting frustrated with these process measures. I was beginning to feel that what we needed to do was pivot, to start measuring outcomes,” Krumholz said, adding that he wanted to see the im­pact that quality of care has on patients.

President Obama signs the Patient Protection and Affordable Care Act into law. Courtesy of Wikimedia Commons.
President Obama signs the Patient Protection and Affordable Care Act into law. Courtesy of Wikimedia Commons.

The government asked CORE to mea­sure one outcome in particular: cost. But to Krumholz, cost was a red herring. He was worried that it would be a difficult measure to interpret. For example, some people might equate higher cost with higher quality care. “[We wanted to] cre­ate new measures that might favorably affect peoples’ responsibility about cost, but really in the best interest of patients,” Krumholz said. He noticed that little was being done to actually reverse the rise in readmission rates.

Krumholz’s idea evolved into the 30- day readmission rate metric, now at the core of the RRP. In spite of some health­care officials’ calls for modifying these measurements, Krumholz defends them for two reasons: “One, we’re not sure to what extent it’s quality of care or some­thing beyond the control of the hospital. Two, we don’t think you should hide the differences. If you adjust in the models for the socioeconomic status of the patients, it will make those places appear as if they’re doing as well as everyone else.”

The risks of adjusting for socioeconom­ic status are also apparent in the evalua­tion and ranking of public schools. Say, for example, two schools are being com­pared based on test performance. Each is assigned a grade — A, B, or C — based on the ratio between expected and actu­al mean score. One is suburban, and the other is inner city. We expect that the suburban school will do better based on the higher relative wealth of its students’ families. These expectations are taken into account when calculating each school’s grade. Now, say each school performs as expected. At the end of the year, we give each an A. Great, right?

Not quite. If we were to rank schools across the country based on this method­ology, we might find the inner city school in the upper quartile. The school would appear as if it had performed extreme­ly well, perhaps even better than some schools that had numerically higher test scores. Adjusting for socioeconomic status would similarly conceal disparities among hospitals. We essentially trick ourselves into believing that everything is fine, when in fact it is not. And when we fall prey to such an illusion, we have no way of know­ing where to funnel resources and capital.

Targeted reform

Krumholz points to recent findings that indicate that the RRP measures have sparked positive change. National and re­gional efforts to reduce readmission rates are already underway. The CMS supports programs like the Partnership for Patients, which provides $500 million in support of transitional care services. National 30-day readmission rates have fallen steadily from 58.2 percent in July 2009 to 50.1 percent in June 2013. “It would be hard to believe this would have happened had we not im­plemented the measures. [People] weren’t paying attention to it [before], and now they are,” Krumholz said.

CORE’s Quality Measurement group, responsible for developing the hospital outcomes measures. Courtesy of CORE.
CORE’s Quality Measurement group, responsible for developing the hospital outcomes measures. Courtesy of CORE.

Krumholz concedes that while he be­lieves in the measures, he does not agree with all aspects of the payment policies. He asserts that the government should take into account whether or not a hos­pital serves disadvantaged populations before imposing fines. This would resolve the challenge of burdening safety net hos­pitals without concealing disparities. But whether or not the policy will be amend­ed remains uncertain. Many incoming legislators do not seem too keen on the ACA, much less its provisions regarding readmissions.

All of this, according to Krumholz, is out of his hands for now. “That’s [up to] Con­gress and the government,” he said.

About the Author: Patrick Demkowicz is a freshman biomedical engineering major in Ezra Stiles College. He spends his time studying influenza in the lab of professor Akiko Iwasaki, building a radio telescope with the Yale Undergraduate Aerospace Association, and writing for the Yale Scientific.

Acknowledgments: The author would like to thank Dr. Krumholz for his time and contributions.

Further Reading:

Keyhani, Salomeh, Laura J. Myers, Eric Cheng, Paul Hebert, Linda S. Williams, and Dawn M. Bravata. 2014. ‘Effect Of Clinical And Social Risk Factors On Hospital Profiling For Stroke Readmission’. Annals Of Internal Medicine 161 (11): 775. doi:10.7326/m14-0361.

Krumholz, Harlan M., and Susannah M. Bernheim. 2014. ‘Considering The Role Of Socioeconomic Status In Hospital Outcomes Measures’. Annals Of Internal Medicine 161 (11): 833. doi:10.7326/m14-2308.

Daughtridge, Giffin W., Traci Archibald, and Patrick H. Conway. 2014. ‘Quality Improvement Of Care Transitions And The Trend Of Composite Hospital Care’. JAMA 311 (10): 1013. doi:10.1001/jama.2014.509.

House, J., & Williams, D. R. (2000). “Understanding And Reducing Socioeconomic And Racial/Ethnic Disparities In Health.” In B. D. Smedley & Syme, S. L. (Ed.), Promoting Health: Intervention Strategies From Social And Behavioral Research, (Pp. 81-124). Washington, D.C.: National Academy Press.

Fiscella, Kevin, Peter Franks, Marthe R. Gold, and Carolyn M. Clancy. 2000. ‘Inequality In Quality’.JAMA 283 (19): 2579. doi:10.1001/jama.283.19.2579.

Lewin, Marion Ein, and Stuart H Altman. 2000. America’s Health Care Safety Net. Washington, D.C.: Institute of Medicine.,. 2012. ‘Thousands Of Hospitals Face Penalties For High Readmission Rates’.