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

Abstract

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.