Digital Twin Application in Patient Centered Care

Researchers: Fangzhou (Luna) Xie, Yangyuechen (Fiona) Zhao

This research focuses on developing a decision support system (DSS) that facilitates collaboration among clinicians and patients when selecting medical treatments. The innovative use of digital twin methodology, traditionally applied in supply chain management, forms the basis of its application. The digital twin forms a virtual representation of the patient, focusing on benefit and risk probabilities associated with treatment options for their medical condition. These probabilities are updated using Bayesian probability methods, with data integrated from Electronic Health Record (EHR) systems and available patient-reported outcomes (PRO). It supports SDM by showing visualizations using a personalized patient dashboard.

The DSS will use visualizations to simplify patients’ decisions. It addresses the complex balance between the treatment benefits and risks, which include their probabilities and magnitudes. Increasingly, these parameters are known and often their values can be customized based on the patient’s characteristics, such as age and family history. However, it is difficult for a patient to make decisions in the context of uncertainty. The types of treatments under consideration will tend to have specific benefits that are the main focus of attention, but these benefits can be uncertain. The risks of many treatments include potential side effects or long-term impacts. Examples include various cancer treatment approaches,  treatment of carotid artery disease, and orthopedic choice like hip or knee pain management.