title: Identification of factors associated with hospital readmission and development of a predictive model.
abstract: Multiple hospital admissions, especially those related to the chronically ill, represent a particular challenge to both the acute and long-term care sectors to identify effective methods of resource management. This study analyzes the multiple admission patterns associated with a cohort of 4,219 adult medical-surgical patients discharged alive from a community teaching hospital in Michigan. The sample was divided into two groups: 3,818 patients who survived and 392 who expired during the one-year follow-up period. For the surviving subsample, the characteristics found to be directly associated with the likelihood of readmission included increased age, advanced stage of disease, greater index-episode length of stay, discharge by an internist rather than a surgeon, Medicare as expected source of payment, decreased physician age, discharge to a community setting, and increased number of prior hospital episodes. For the subsample who died, only one explanatory variable was significantly associated with an increased likelihood of readmission-discharge to a community setting (with or without home care) rather than a nursing home. The article includes illustrates of the importance of decisions regarding posthospital, long-term care services on the likelihood of rehospitalization.
authors.affiliation: National Committee for Quality Assurance, Washington, DC 20036.
authors.forename: J M
authors.initials: JM
authors.lastname: Corrigan
authors.forename: J B
authors.initials: JB
authors.lastname: Martin
mesh_terms: D000328:Adult; D000367:Age Factors; D006746:Hospital Bed Capacity, 500 and over; D006764:Hospitals, Community / Q000706:statistics & numerical data*; D006784:Hospitals, Teaching / Q000706:statistics & numerical data*; D006801:Humans; D008824:Michigan; D015233:Models, Statistical; D009026:Mortality; D010351:Patient Discharge / Q000706:statistics & numerical data; D010359:Patient Readmission / Q000706:statistics & numerical data*; D011336:Probability; D011379:Prognosis; D016016:Proportional Hazards Models; D012737:Sex Factors