Predicting future hospital utilization for mental health conditions

• Published in: The journal of behavioral health services & research Vol. 34; no. 1; pp. 34 - 42
• Main Authors: Kolbasovsky, Andrew, Reich, Leonard, Futterman, Robert
• Format: Journal Article
• Language: English
• Published: United States Springer Nature B.V 01.01.2007
• Subjects: Adult; Aftercare - utilization; Discharge; Female; Forecasting; Future events; Health Maintenance Organizations; Health services utilization; Hospitalization; Hospitals, Psychiatric - utilization; Humans; Male; Mental disorders; Mental Disorders - diagnosis; Mental Disorders - economics; Mental Disorders - epidemiology; Mental health; Mental health care; Mentally ill people; Middle Aged; New York; Patient Readmission - statistics & numerical data; Psychiatric Department, Hospital - utilization; Psychiatric hospitals; Regression Analysis; Retrospective Studies; Studies; Utilization Review - statistics & numerical data; New York; United States > US
• ISSN: 1094-3412; 1556-3308
• DOI: 10.1007/s11414-006-9044-0

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.