A predictive model of hospitalization risk among disabled medicaid enrollees.

OBJECTIVES: To identify Medicaid patients, based on 1 year of administrative data, who were at high risk of admission to a hospital in the next year, and who were most likely to benefit from outreach and targeted interventions. STUDY DESIGN: Observational cohort study for predictive modeling. METHODS: Claims, enrollment, and eligibility data for 2007 from a state Medicaid program were used to provide the independent variables for a logistic regression model to predict inpatient stays in 2008 for fully covered, continuously enrolled, disabled members. The model was developed using a 50% random sample from the state and was validated against the other 50%. Further validation was carried out by applying the parameters from the model to data from a second state's disabled Medicaid population. RESULTS: The strongest predictors in the model developed from the first 50% sample were over age 65 years, inpatient stay(s) in 2007, and higher Charlson Comorbidity Index scores. The areas under the receiver operating characteristic curve for the model based on the 50% state sample and its application to the 2 other samples ranged from 0.79 to 0.81. Models developed independently for all 3 samples were as high as 0.86. The results show a consistent trend of more accurate prediction of hospitalization with increasing risk score. CONCLUSIONS: This is a fairly robust method for targeting Medicaid members with a high probability of future avoidable hospitalizations for possible case management or other interventions. Comparison with a second state's Medicaid program provides additional evidence for the usefulness of the model.

Predictive models for diabetes patients in Medicaid.

Predictive modeling can be used to identify persons who are at increased risk for adverse health outcomes. We used demographic, medical, and pharmacy claims data to create a gender-specific model for fee-for-service Medicaid based on 2 states' data that can assist with the identification of persons with an elevated future risk of hospitalization, elevated claims expense, or death. Depending on age and the outcome of interest, the area under the receiver operating characteristic curve for this predictive modeling tool across 2 states' diabetes populations ranged from 0.608 to 0.834. We conclude that this analysis yielded a level of accuracy comparable to other predictive models that can be used to target patient enrollment in population-based care management.

Improving patient compliance with best practices guidelines: a web based automated and personalized reminders system.

Health Reminders is a prototype application that delivers personalized guidelines and recommended testing to the patient at the appropriate time and through multiple communication channels such as email, web, cellular text messaging (Simplified Message Service), automatic insertion into Personal Information Managers (schedulers like MS Outlook) as appointments and tasks.