Master of Public Health

Predicting hospital readmission in Medicaid patients with diabetes using administrative and claims data

AUTHORS

ABSTRACT

OBJECTIVES: Readmission is common and costly for hospitalized Medicaid patients with diabetes. We aimed to develop a model predicting risk of 30-day readmission in Medicaid patients with diabetes hospitalized for any cause.

STUDY DESIGN: Using 2016-2019 Medicaid claims from 7 US states, we identified patients who (1) had a diagnosis of diabetes or were prescribed any diabetes drug, (2) were hospitalized for any cause, and (3) were discharged to home or to a nonhospice facility. For each encounter, we assessed whether the patient was readmitted within 30 days of discharge.

METHODS: Applying least absolute shrinkage and selection operator variable selection, we included demographic data and claims history in a logistic regression model to predict 30-day readmission. We evaluated model fit graphically and measured predictive accuracy by the area under the receiver operating characteristic curve (AUROC).

RESULTS: Among 69,640 eligible patients, there were 129,170 hospitalizations, of which 29,410 (22.8%) were 30-day readmissions. The final model included age, sex, age-sex interaction, past diagnoses, US state of admission, number of admissions in the preceding year, index admission type, index admission diagnosis, discharge status, length of stay, and length of stay-sex interaction. The observed vs predicted plot showed good fit. The estimated AUROC of 0.761 was robust in analyses that assessed sensitivity to a range of model assumptions.

CONCLUSIONS: Our model has moderate power for identifying hospitalized Medicaid patients with diabetes who are at high risk of readmission. It is a template for identifying patients at risk of readmission and for adjusting comparisons of 30-day readmission rates among sites or over time.

Tags: 2023 Alumni Publications