title: Predicting future hospital utilization for mental health conditions.
abstract: To develop a model using administrative variables to predict number of days in the hospital for a mental health condition in the year after discharge from a mental health hospitalization. Background, index hospitalization and preindex inpatient, emergency room, and outpatient utilization information were collected for 766 adult members discharged from a mental health hospitalization during a 1-year period. A regression model was developed to predict hospitalized days for a mental health condition in the year after discharge. A regression model was created containing five statistically significant predictors: Medicare insurance coverage, preindex mental health inpatient days, index length of stay, depression diagnosis, and number of mental health outpatient visits with a professional provider. It is possible to predict future mental health inpatient utilization at the time of discharge from a mental health hospitalization using administrative data, thus allowing disease managers to better identify members in greatest need of additional services and interventions.
authors.affiliation: Clinical Development and Behavioral Medicine, Health Insurance Plan of New York, 55 Water Street, New York, NY 10041, USA. akolbasovsky@hipusa.com
authors.forename: Andrew
authors.initials: A
authors.lastname: Kolbasovsky
authors.forename: Leonard
authors.initials: L
authors.lastname: Reich
authors.forename: Robert
authors.initials: R
authors.lastname: Futterman
mesh_terms: D000328:Adult; D000359:Aftercare / Q000706:statistics & numerical data; D005260:Female; D005544:Forecasting; D006279:Health Maintenance Organizations; D006778:Hospitals, Psychiatric / Q000706:statistics & numerical data*; D006801:Humans; D008297:Male; D001523:Mental Disorders / Q000175:diagnosis / Q000191:economics / Q000453:epidemiology*; D008875:Middle Aged; D009518:New York; D010359:Patient Readmission / Q000706:statistics & numerical data*; D011567:Psychiatric Department, Hospital / Q000706:statistics & numerical data*; D012044:Regression Analysis; D012189:Retrospective Studies; D014600:Utilization Review / Q000706:statistics & numerical data*