Efficacy of Transitional Care Services for Patients With Stroke: Systematic Review and Meta-Analysis.

Background: Approximately 60% of patients who have a stroke are discharged to their homes, after which they require ongoing community-based rehabilitation to support long-term recovery. Transitional care services (TCS) serve as an intervention aimed at reducing hospital readmissions by maintaining treatment continuity, either when patients transition between different levels of care within the same setting or when they move to a new care environment.

Objectives: The purpose of this review was to evaluate the efficacy of TCS in reducing readmission rates, enhancing functional outcomes, improving self-efficacy, and boosting the quality of life for patients who had a stroke while also alleviating caregiver burden.

Predictive Mortality and Gastric Cancer Risk Using Clinical and Socio-Economic Data: A Nationwide Multicenter Cohort Study.

Gastric cancer is a leading cause of cancer-related mortality, particularly in East Asia, with a notable burden in Republic of Korea. This study aimed to construct and develop machine learning models for the prediction of gastric cancer mortality and the identification of risk factors. All data were acquired from the Korean Clinical Data Utilization for Research Excellence by multiple medical centers in South Korea.

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.