Digital twin of patient in clinical workflow

Abstract

The concept of a digital twin being made use of in healthcare is an emerging research area. Digital twins in healthcare have the potential to enable more precise and personalised care, especially for patients experiencing chronic conditions. Previous work has identified digital twins as mathematical models. Digital twins have been classified as grey box, surrogate and black box models. Based on this classification, the black box models can handle data intensive and sophisticated problems. This makes black box models a candidate for constructing digital twins of patients. Such digital twins can then assist clinicians in clinical decision making. This has the potential to reduce cognitive burden on clinicians, to increase precision and personalisation of care through enhanced use of data, and to improve patient outcomes and cost implications. However, introducing such digital twins to healthcare would be a significant intervention that would alter traditional clinical workflows. As such, we present in this paper one of the first attempts of conceptual mapping of altered next generation primary care clinical workflows that would allow the incorporation of digital twins of patients in managing chronic conditions.