Assigning patients a predictive score as they leave the hospital could reduce readmissions in a more cost-effective way than typical interventions for readmissions, a new study in JAMA Internal Medicine found.
Using clinical and administrative data readily available prior to discharge, the study aimed to determine a prediction model for avoidable 30-day hospital readmissions. The study analyzed about a year's worth of patient discharges at three hospitals in the Partners HealthCare Network in Boston.
Among 10,731 eligible discharges, 2,398 were followed by a 30-day readmission, and 879 had been identified by the hospital as potentially avoidable.
According to the study, seven independent factors make up what researchers dubbed a "HOSPITAL" score used to determine a patient's readmission potential: hemoglobin at discharge, discharge from an oncology service, sodium level at discharge, procedure during the index admission, number of admissions in the past 12 months, and length of stay.
Study authors concluded that the prediction model successfully identifies the risk of avoidable readmission in discharged patients. It's been a critical question to address since the Centers for Medicare & Medicaid Services started imposing penalties based on excessive readmission rates.
On Oct. 1, 2012, CMS began enforcing the hospital readmissions reduction program from the Affordable Care Act. The rate of 30-day readmissions dropped to 17.8 percent in the fourth quarter of 2012, down from between 18.5 percent and 19.5 percent during the previous five years, according to Jonathan Blum, acting CMS deputy director.
"This study is a step in the evolution of our understanding of how we're going to prevent readmissions to the hospital, which is a major problem both in terms of patient safety and cost of care," co-author Jeffrey L. Schnipper , M.D., of Harvard and Brigham and Women's Hospital in Boston told MedPageToday.
To learn more:
- read the JAMA Internal Medicine study
- read the MedPageToday article
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