title: Do collaborative case management models decrease hospital readmission rates among high-risk patients?
abstract: BACKGROUND
  Case management provides a process and structure in health care systems that influence and control quality of care while reducing costs. A quality indicator of widespread concern is 30-day readmission of patients. There is significant initiative to drive down hospital readmission rates through development and/or redesign of case management models.
  
  
  OBJECTIVE
  To examine the relationship of a collaborative case management model on hospital readmission rates among patients aged 65 years and older.
  
  
  METHODS
  A retrospective chart review of patients discharged alive (n = 978) was conducted to evaluate and compare 2 care management models on hospital readmission rates. Demographic data, diagnosis, insurance carrier, admission source, discharge disposition, and incidence of readmission were collected using a structured data extraction tool. Logistic regression was used to identify predictors of readmission within 30 days of hospital discharge.
  
  
  RESULTS
  The sample was elderly (mean age = 79.5 years), White (88.8%), and primarily female (60%). Mean length of stay between pre- and postmodel groups was not statistically different (p = .2). The model contained 6 independent variables (gender, payer, admission source, discharge disposition, diagnosis, and length of stay) and none were statistically significant, χ2 (1, n = 978) = 1.97, p = .58. The analysis indicates that group characteristics did not distinguish who would get readmitted on the basis of independent variables measured.
  
  
  CONCLUSIONS
  Age, gender, admit source, diagnosis, length of stay, and discharge disposition are not significant predictors of readmissions. Hospital case management programs may want to consider structuring processes to support patient adherence. Additional research is needed in this area.
authors.affiliation: Marisa A. Bisiani, DNP, RN, ANP-BC, has been in the field nursing for more than 20 years. As a young nurse who began her career as a nurse's aide, she pursued education as a licensed practical nurse and then an associate prepared registered nurse to obtaining a bachelor of science. She received her master of science from Stony Brook, leading her to become a licensed ANCC Board Certified Adult Nurse Practitioner. She recently graduated from Stony Brook Universities Doctorate of Nursing Practice (DNP) program. She works as a nurse executive and she has directed many departments such as care management, allied health professionals, employee health services, and hemodialysis. Her specialty is inclusive of focusing on care transitions, as we move one patient to each level of care. She is exceptionally interested in the work of preventable readmissions and population health. Corrine Y. Jurgens, PhD, RN, ANP-BC, FAHA, is a nurse scientist. Her program of research focuses on patients with heart failure and self-care. In particular, she studies symptom perception, recognition, and response in this population. Her prior work indicates patients lack a context for determining whether their symptoms are heart-related or associated with less threatening illnesses. She conducted a randomized trial testing an intervention based on my prior studies and theory that provides patients with a way to monitor and interpret their symptoms in a meaningful way. Other interests include investigating a phenotype for mild cognitive impairment in patients with heart failure, exploring self-care interventions for frail elders, implementing guidelines for heart failure management in skilled nursing facilities, and examining factors related to symptom blunting in this population.
authors.forename: Marisa A
authors.initials: MA
authors.lastname: Bisiani
authors.forename: Corinne Y
authors.initials: CY
authors.lastname: Jurgens
mesh_terms: D000368:Aged; D019090:Case Management; D003299:Cooperative Behavior; D005260:Female; D006801:Humans; D008297:Male; D010343:Patient Admission; D012189:Retrospective Studies; D012307:Risk Factors