Identifying the patients most likely to return to hospital after release could improve care and save money
By TIM SULLIVAN
A new method of determining which patients are at the greatest risk of being readmitted to the hospital could help improve the quality of patient care and reduce expenses in the health care system.
A new retrospective study published in the May issue of the Journal of General Internal Medicineby Harvard Medical School researchers at Spaulding Rehabilitation Hospital found that readmission models based on functional status—an individual’s ability to perform normal daily activities to meet basic needs and fulfill normal roles—consistently outperformed models based on comorbidities—a measure of what other illnesses patients have, in addition to the illness for which they were hospitalized.
“Finding the markers that determine the likelihood of readmissions is a high priority for the entire medical community,” said principal investigator Jeffrey Schneider, medical director of the Burn and Trauma Rehabilitation Program at Spaulding Rehabilitation Hospital and assistant professor of physical medicine and rehabilitation at HMS. “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.”
The readmission of patients is one of the greatest challenges to the healthcare system, with estimated costs of well over forty billion dollars annually. 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.
Schneider and colleagues 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.
They build models based on functional status and gender 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 the model’s 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).
The researchers found that 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.