New Study Results by Spaulding and Harvard Researchers Support Switching the Focus on Readmission Causes

(Boston, MA)- A new retrospective study published in the May issue of Journal of General Internal Medicine by researchers from Spaulding Rehabilitation Hospital and Harvard Medical School found that readmission models based on functional status consistently outperformed models based on medical comorbidities.  The readmission of patients is one of the greatest challenges to the healthcare system, with estimates at well over forty billion dollars annually in costs to providers. Recently, the Centers for Medicare and Medicaid Services began issuing Financial penalties to hospitals with excess 30-dayhospital readmissions; more than 2,200 hospitals were fined a total of $280 million in 2013.  The team examining the issue conducted a retrospective study of 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.

“Finding the markers that determine the likelihood of readmissions is a high priority for the entire medical community. The resources they command and impact on quality of life for patients is significant. Our study strongly demonstrates that previous notions of disease driven factors are less of a determinant of readmissions than a marker of functional status; hospitals and clinicians would be better served using functional status as a predictor of at risk cases,” said principal investigator Dr. Jeffrey Schneider, Medical Director, Burn and Trauma Rehabilitation Program at Spaulding Rehabilitation Hospital, Assistant Professor at Harvard Medical School.

For the study, logistic regression models based on function and gender were developed to predict the odds of three, seven, and thirty day readmission from inpatient rehabilitation facilities to acute care hospitals.  These function-based models were compared with models based instead on comorbidities and gender.  Additionally, comorbidities were added to the function-based models to determine whether this improved model predictive ability.  Functional status was measured by a validated, standardized assessment of functional status—the Functional Independent Measure (FIM). Comorbidities were assessed using three different comorbidity measures (the Elixhauser index, Deyo-Charlson index and Medicare comorbidity tier system). 

Model performance was assessed using c-statistics, a statistical test used to assess the ability of the model to predict patients that require readmission to the acute care hospital. For 3-, 7-, and 30-day readmissions, models based on function and gender (c-statistics 0.691, 0.637, and 0.649, respectively) performed significantly better than even the best-performing models based on comorbidities and gender (c-statistics 0.572, 0.570, and 0.573, respectively).  Furthermore, the addition of comorbidities to function-based models did not appreciably improve model performance (c-statistic differences of only 0.013, 0.017, and 0.015 for 3-, 7-, and 30-day readmissions, respectively, for the best-performing models).   

“The models show a clear opportunity to improve current national readmission risk models to more accurately predict readmissions and more fairly reimburse hospitals based on performance. This study opens a significant opportunity to assess the impact of early function-based interventions on reducing readmission risk,” said Schneider.

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