Using the Non-Adoption, Abandonment, Scale-Up, Spread, and Sustainability (NASSS) Framework to Identify Barriers and Facilitators for the Implementation of Digital Twins in Cardiovascular Medicine

Abstract

A digital twin is a computer-based “virtual” representation of a complex system, updated using data from the “real” twin. Digital twins are established in product manufacturing, aviation, and infrastructure and are attracting significant attention in medicine. In medicine, digital twins hold great promise to improve prevention of cardiovascular diseases and enable personalised health care through a range of Internet of Things (IoT) devices which collect patient data in real-time. However, the promise of such new technology is often met with many technical, scientific, social, and ethical challenges that need to be overcome—if these challenges are not met, the technology is therefore less likely on balance to be adopted by stakeholders. The purpose of this work is to identify the facilitators and barriers to the implementation of digital twins in cardiovascular medicine. Using, the Non-adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework, we conducted a document analysis of policy reports, industry websites, online magazines, and academic publications on digital twins in cardiovascular medicine, identifying potential facilitators and barriers to adoption. Our results show key facilitating factors for implementation: preventing cardiovascular disease, in silico simulation and experimentation, and personalised care. Key barriers to implementation included: establishing real-time data exchange, perceived specialist skills required, high demand for patient data, and ethical risks related to privacy and surveillance. Furthermore, the lack of empirical research on the attributes of digital twins by different research groups, the characteristics and behaviour of adopters, and the nature and extent of social, regulatory, economic, and political contexts in the planning and development process of these technologies is perceived as a major hindering factor to future implementation.

Insights into implementation planning for point-of-care testing to guide treatment of chronic obstructive pulmonary disease exacerbation: a mixed methods feasibility study

“…We propose that a mixed methods feasibility study adds value in an iterative product design process and supports the development of implementation strategies, alongside patient and public involvement. Frameworks including non-adoption, abandonment, scale-up, spread and sustainability (NASSS) considers challenges beyond the initial implementation phase ( 46 ) as demonstrated within the field of cardiovascular medicine ( 47 ). This mixed methods feasibility study tested an early-stage diagnostic product.…”

3D Printing Approach in Maxillofacial Surgery in Iran: An Evaluation Using the Non-Adoption, Abandonment, Scale-Up, Spread, and Sustainability (NASSS) Framework

“…Domain 1 explores the complexities of health conditions in maxillofacial surgery, highlighting the intricate challenges presented by the coexistence of these conditions with other comorbidities. Moreover, the management of these conditions is significantly affected by sociocultural factors, such as poverty or race and ethnicity, which are vital in determining the condition's impact and the management strategies used in the context of 3D printing in oral and maxillofacial surgery [7,8]. Domain 2 explores the complexities of 3D printing technology, focusing on its operational effectiveness, reliability, and speed.…”

“…This domain highlights how external factors influence the adoption and potential deceleration of innovative technologies. Aspects like social, political, technological, and economic conditions significantly affect how new technologies are received and incorporated into the healthcare field [7]. Domain 7 centers on the continuous integration and adjustment of the technology and the involved service or organization over time.…”

Stroke physicians’ and staff perspectives on machine learning to optimise thrombolysis decision making in stroke: a qualitative study

“…We chose NASSS because it allows identification and consideration of the multiple, overlapping influences (or 'domains') of influence on health-technology uptake and use. The NASSS framework is flexible and can be used prospectively to address implementation challenges [34][35][36] and has previously been applied to the implementation of AI/machine learning in healthcare 34,[36][37][38][39] .…”