A Comprehensive Framework Identifying Readmission Risk Factors Using The Chaid Algorithm: A Prospective Cohort Study

Abstract

Objective: To identify frequency of readmission after discharge from internal-medicine wards, readmission risk factors, and reasons and costs of readmission. Design: Prospective cohort study. Setting: A tertiary-care hospital in Turkey. Participants: 2622 adult patients discharged from internal-medicine wards of the hospital between 1 February 2015 and 31 January 2016. Main outcome measures: Thirty day all-cause readmission rates, reasons and costs of readmission. To identify readmission risk factors Chi-square Automatic Interaction Detector (CHAID) analysis was conducted. Results: The same hospital readmission rate was 17.9%, while the same hospital or different-hospital readmission rate was 21.3%. Receiver operating characteristic (ROC) curve analysis showed that the predictive performance of the CHAID algorithm was high. According to the CHAID algorithm, the most significant readmission risk factor was the main diagnosis of neoplasm at the index admission. In other diagnosis groups, higher Charlson comorbidity score, higher level of education, having a regular physician, and three dimensions of Readiness for Hospital Discharge Scale were significant risk factors for readmission. The most frequent reason for readmission was neoplasm, and the total cost of readmissions was similar to$900 000. Conclusions: The CHAID algorithm for readmissions had a high predictive strength and provided details that aid physicians in decision-making. Measures must be taken from initial diagnosis to post-discharge follow-up, to minimize readmissions, especially in patients with neoplasm.