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Harvard Team Questions CMS Predictive Readmission Models

Researchers find functional status, rather than comorbidity, is better predictor of readmission

The way the Centers for Medicare & Medicaid Services (CMS) predicts readmissions is likely neither the most accurate nor the fairest, according to a study by researchers at Harvard Medical School.

The study, published in the May issue of the Journal of General Internal Medicine, found that functional status, rather than comorbidities, was a better predictor of hospital readmissions.

“This raises a question of whether Medicare is really using the best predictors to really understand readmission,” as well as questions about how fairly hospitals are being financially penalized, principal investigator Jeffrey Schneider, MD, told HealthLeaders Media.

Schneider pointed out that the CMS fined more than 2,200 hospitals a total of $280 million in 2013 for excess 30-day hospital readmissions, so having accurate readmission models is critical. But the ones CMS uses “are not very good predictive models, and they have relied heavily on simple demographic data, like age and gender and comorbidities,” he said.

Moreover, “there's mounting evidence that function is a good predictor of all sorts of hospital outcomes.”

The researchers developed a basic model based on gender and functional status using logistic regression to predict the odds of 3-, 7-, and 30-day readmission from inpatient rehabilitation facilities to acute care hospitals. Patients’ functional status was measured by the FIM motor score. The basic model was compared with six other predictive models: three “basic plus” models, which added a comorbidity measure to the basic model, and three gender–comorbidity models, which included only gender and a comorbidity measure.

The researchers used their method to evaluate 120,957 patients in the Uniform Data System for Medical Rehabilitation database who were admitted to inpatient rehabilitation facilities under the medically complex impairment group code between 2002 and 2011.

They found that the model with gender and function was significantly better at predicting readmissions.

Models based on function and gender for 3-, 7-, and 30-day readmissions performed significantly better than even the best-performing model based on comorbidities and gender, Schneider said.

He explained that functional status is a good predictor of readmission because it may represent something else, such as the severity of a patient’s illness. Cancer patients, for instance, have a wide degree of functional statuses depending on how sick they are. In this way, it’s “intuitive” that function would be a good predictor of readmissions, he said. In other words, if patients can’t care for themselves, they are likely to end up back in the hospital.

In addition, “comorbidity is a fixed variable,” Schneider said, but function is not. And since function is a better predictor of readmission, even at shorter time intervals, assessing a patient’s functional status and doing things to improve it could be a way of reducing preventable readmissions, especially the 3- and 7-day readmissions.

“Acute care hospitals are not routinely collecting a functional measure of their patients,” Schneider said. He also pointed out that recent research on functional interventions — such as early mobilization in the intensive care unit — in acute care hospitals has shown that they improve patient outcomes.

On a larger scale, there is also the policy perspective that the CMS's current readmissions models aren't as good as they could be. Schneider said that he and his colleagues are conducting another study, using the same framework, but looking at all patients in a rehab hospital, not just medically complex ones.

Sources: HealthLeaders Media; June 16, 2015; and JGIM; May 9, 2015.

 

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