Towards Extended Reality Intelligence for Monitoring and Predicting Patient Readmission Risks

TL;DR

This paper combines machine learning and mixed reality to predict and visualize patient readmission risks, aiming to enhance clinical decision-making for diabetic patients.

Contribution

It introduces a predictive model for readmission risk and an MR visualization tool to improve healthcare provider insights and communication.

Findings

XGBoost model achieved AUROC of 0.72

Key factors include prior visits and glycemic control

MR prototype visualizes risk and contributing factors

Abstract

Hospital readmissions remain a challenge for healthcare systems, especially among patients with chronic conditions such as diabetes. Unplanned readmissions within 30 days are costly, strain hospital resources, and can indicate poor care coordination or discharge planning. In this work, we explore the use of machine learning to predict readmission risk for diabetic inpatients and propose a mixed reality (MR) to provide effective visualization and insights. We trained an XGBoost classifier after data cleaning, encoding, and feature engineering. The model achieved an Area Under the Receiver Operating characteristic Curve (AUROC) of 0.72 and an Area Under the Precision-Recall Curve (AUPRC) of 0.11. Key predictive factors included prior inpatient visits, discharge disposition, and glycemic control indicators such as A1C (blood sugar test) results and medication adjustments. Additionally, we developed an MR prototype that visualize patient records and predictions containing risk level, major contributing factors, and a concise summary of care. Together, the predictive model and the MR interface aim to improve clinician awareness and communication around readmission risk in real-time clinical settings.