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.
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.
Authors: William J Deardorff; Richard J Sloane; Juliessa M Pavon; Susan N Hastings; Heather E Whitson Journal: J Am Geriatr Soc Date: 2020-08-27 Impact factor: 5.562
Authors: Daniel Z Louis; Mary Robeson; John McAna; Vittorio Maio; Scott W Keith; Mengdan Liu; Joseph S Gonnella; Roberto Grilli Journal: BMJ Open Date: 2014-09-17 Impact factor: 2.692
Authors: Daniel Z Louis; Clara A Callahan; Mary Robeson; Mengdan Liu; Jacquelyn McRae; Joseph S Gonnella; Marco Lombardi; Vittorio Maio Journal: BMJ Open Date: 2018-05-05 Impact factor: 2.692