Predicting the likelihood of readmission in patients with ischemic stroke: An explainable machine learning approach using common data model data.

Background: Ischemic stroke affects 15 million people worldwide, causing five million deaths annually. Despite declining mortality rates, stroke incidence and readmission risks remain high, highlighting the need for preventing readmission to improve the quality of life of survivors. This study developed a machine-learning model to predict 90-day stroke readmission using electronic medical records converted to the common data model (CDM) from the Regional Accountable Care Hospital in Gangwon state in South Korea.

Cost-utility analysis of transitional care services for older inpatients with chronic obstructive pulmonary disease (COPD) in Korea.

Background: Chronic obstructive pulmonary disease (COPD) is associated with a high readmission rate and poses a significant disease burden. South Korea initiated pilot projects on transitional care services (TCS) to reduce readmissions. However, evidence from cost-effectiveness analyses remains undiscovered.

Facilitating and barrier factors to the implementation of a transitional care program: a qualitative study of hospital coordinators in South Korea.

Background: Transitional care is an integrated service to ensure coordination and continuity of patients' healthcare. Many models are being developed and implemented for this care. This study aims to identify the facilitators and obstacles of project performance through the experiences of the coordinator in charge of the Community Linkage Program for Discharge Patients (CLDP), a representative transitional care program in Korea.