Quantitative tools for addressing hospital readmissions

• Published in: BMC research notes 2012-11, Vol.5 (1), p.620-620, Article 620
• Main Authors: Lagoe, Ronald J, Nanno, Diane S, Luziani, Mary E
• Format: Article
• Language: English
• Subjects: Admission and discharge; Adolescent; Adult; Aged; Aged, 80 and over; Cost control; Electronic Health Records - statistics & numerical data; Health Care Costs - statistics & numerical data; Healthcare industry software; Hospital readmissions; Hospitalization; Hospitalization - statistics & numerical data; Hospitals; Humans; Inpatients - statistics & numerical data; Length of Stay - statistics & numerical data; Medical research; Medicine, Experimental; Methods; Middle Aged; Patient Discharge - statistics & numerical data; Patient Readmission - economics; Patient Readmission - standards; Patient Readmission - statistics & numerical data; Quality assurance; Quality management; Quality of Health Care - statistics & numerical data; Short Report; Time Factors; Young Adult
• ISSN: 1756-0500; 1756-0500
• DOI: 10.1186/1756-0500-5-620

Increased interest in health care cost containment is focusing attention on reduction of hospital readmissions. Major payors have already developed financial penalties for providers that generate excess readmissions. This subject has benefitted from the development of resources such as the Potentially Preventable Readmissions software. This process has encouraged hospitals to renew efforts to improve these outcomes. The aim of this study was to describe quantitative tools such as definitions, risk estimation, and tracking of patients for reducing hospital readmissions. This study employed the Potentially Preventable Readmissions software to develop quantitative tools for addressing hospital readmissions. These tools included two definitions of readmissions that support identification and management of patients. They also included analytical approaches for estimation of the risk of readmission for individual patients by age, discharge status of the initial admission, and severity of illness. They also included patient specific spreadsheets for tracking of target populations and for evaluation of the impact of interventions. The study demonstrated that quantitative tools including the development of definitions of readmissions, estimation of the risk of readmission, and patient specific spreadsheets could contribute to the improvement of patient outcomes in hospitals.