In response to the problem of frequent 30-day readmissions to inpatient psychiatric facilities, Vigod and colleagues (2015) developed the READMIT clinical risk index to identify risk factors for psychiatric inpatient readmissions. The purpose of this descriptive retrospective study was to examine the effectiveness of the READMIT clinical risk index to identify patients that are at high risk for a 30-day inpatient psychiatric readmission at a state psychiatric hospital in the southeastern US. Data were extracted from the discharge summaries of patients discharged between September 2013 and December 2014. Data collected included patient demographic variables (age, gender, race/ethnicity, primary diagnosis, housing status at discharge, employment, long-acting injectable at discharge, substance abuse, education, and insurance status) and study variables from the READMIT clinical risk index (repeat admission, emergent admission, age, diagnosis and discharge, medical comorbidity, intensity, and time in hospital). The inclusion criterion was age 18 and above. There were no exclusion criteria. Findings indicated that age, insurance status, previous lifetime admissions, 'diagnoses and discharge' scores, and higher READMIT clinical risk index scores were associated with 30-day readmissions. Future research should include a prospective study of the READMIT clinical risk index to assess its predictability of 30-day readmissions and explore possible use of the minimum clinical risk index score to trigger evaluation of patient need for enhanced transitional care services posthospital discharge.

Little is known about the factors that influence health behavior decision-making among people with schizophrenia. The purpose of this qualitative study was to describe the processes used by 10 African-American adults with schizophrenia when making health behavior decisions and identification of perceived barriers and facilitators to health. Three phases of health behavior decision-making were identified: Recognizing Complex Components of Health, Personalizing Components of Health, and Tracking Health Status. Findings may guide clinicians' efforts to improve the health status of patients, as well as influence future research in understanding health behavior decision-making among vulnerable populations.

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