Systems thinking approach to the implementation of digital pathology and AI

A rapid rise in digitization and AI implementation in pathology services has been seen over the last decade, which is likely to bring a paradigm shift in these services in the near future.

However, the process of implementing AI solutions to pathology practices has been relatively slow and somewhat problematic due to a technology-centered mindset, which leads to a focus on technology alone and neglecting systemic factors including a wide range of stakeholders involved, ethical issues and the overall workflow, etc.

The aim of this project is to explore how a systems thinking approach can improve the implementation of digitalization and AI implementation in pathology services.

The Royal College of Pathologists (RCPath) position statement on digital pathology and AI provides an essential foundation for understanding the perspective of the RCPath on the implementation of digital pathology and AI.

A qualitative analysis of this foundational document using the Non-adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework as a guiding system framework for analysis was conducted. The NASSS framework, rooted in implementation science and complexity science, was developed to aid in identifying dynamic systemic factors and their interrelationships within the implementation. It promises to offer a comprehensive understanding of the implementation process and its associated complexities. Based on our NASS framework analysis, a visual system map was produced showing how facilitators and barriers interact together and any gaps in the document.

The analysis indicates that the position statement identified some significant facilitators and barriers to implementation, but it focused mainly on pathologists without considering a broader range of indirect adopters like biomedical scientists and associated clinicians within the system. The awareness of the wider healthcare systemic issues was lacking. This presentation will report on more detailed findings, the visual system map produced and further on-going case study work.