Using Health Plan Authorization Data to Predict Hospitalizations and Readmissions in a Combined Commercial and Medicaid Population.

Health plans develop predictive models to predict key clinical events (eg, admissions, readmissions, emergency department visits). The authors developed predictive models of admissions and readmissions for a quality improvement organization with many large government and private health plan clients. Its membership and authorization data were used to develop models predicting 2019 inpatient stays, and 2019 readmissions following 2019 admissions, based on patients' age and sex, diagnoses identified and procedures requested in 2018 authorizations, and 2018 admission authorizations. In addition to testing multivariate models, risk scores were calculated for admission and readmission for all patients in the model. The admissions model (C = 0.8491) is much more accurate than the readmissions model (C = 0.6237). Measures of risk score central tendency and skewness indicate that the vast majority of members had little risk of hospitalization in 2019; the mean (standard deviation) was 0.042 (0.074), and the median was 0.018. These risk scores can be used to identify members at risk of admission and to support proactive risk management (eg, design of health management programs). Different risk thresholds can be used to identify different subsets of members for follow-up, depending on overall strategy and available resources. This model development project was novel in employing authorization data rather than utilization data. Advantages of authorization data are their timeliness, and the fact that they are sometimes the only data available, but disadvantages of authorization data are that authorized services are not always actually performed, and diagnoses are often "rule out" rather than final diagnoses.

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.