Human Digital Twins to Support Nurse Practitioners’ Clinical Decision-Making Using Multimodal Data: A Theoretical, Methodological, and Analytical Framework

This chapter proposes a theoretical, methodological, and analytical approach to supporting nurse practitioners’ clinical decision-making using human digital twins (HDTs). HDTs are symbolic digital replicas of (real) humans that can serve as a novel research platform to represent, model, and simulate how healthcare professionals with varying levels of expertise, knowledge, and skills behave, operate, and reason across various contexts including blended learning immersive environments while caring for patients. We argue that clinicians can augment or accelerate their clinical decision-making (CDM) skills and knowledge development by designing, modeling, and testing HDT supports for use while caring for actual or simulated patients. We adopt the exemplar of preceptors as CDM supports in immersive virtual environments, which can be used in conjunction with traditional methods (e.g., through explicit verbal instruction of the CDM process) and novel uses of displaying, duplicating, merging, and exchanging their multimodal multichannel data (e.g., eye movements, concurrent verbalizations, facial expressions of emotions, physiology, affective states). An exemplar scenario is outlined.