The Preconference workshop at Pathology Visions, presented by PathPresenter (an IMS company), Paige (a leader in AI models for pathology), and the MSKCC Pathology Department, aims to address the evolving landscape of digital pathology in academic settings and lessons learnt from the experience. The session opens with insights from Dr. Ardon at MSKCC, highlighting the unique goals and challenges faced by academic centers during rapid digital pathology expansion, including integration with Epic Beaker and the complexities encountered during their RFP process. This sets the stage for PathPresenter's exploration of interoperability within image management systems (IMS), focusing on the critical integration issues with large-scale systems like Epic. Emphasis is placed on how MSKCC's needs extend beyond primary diagnosis to encompass consultations, education, and research.

AI is set to drive further enhancements to the world of pathology and when considering the digital pathology business case, cannot be an afterthought.  Paige will present their cutting-edge clinical AI products, and how integration with case management and workflows like PathPresenter can make AI central to digital diagnostic review. This will include diagnostic applications like the Paige Breast portfolio - and other multi cancer screening tools for case prioritization.  This review will also include new and upcoming AI applications like Omniscreen and Alba, which are truly changing the face of digital pathology. 

The discussion concludes by underscoring the significance of an end-to-end solution and the necessity for collaborative efforts between leading innovative companies to work together with the goal of maximizing utility for institutions like MSKCC and other organizations.

Background: Digital pathology is slowly being adopted in laboratories around the world, yet discussions of barriers to digital implementation in pathology tend to focus on hardware and software choices, technical interfaces and compatibility, data management, and the adoption of new AI based decision support tools. The cost and quality of the digital transformation are cited as recognized as the major barriers in wide adoption. Another challenge in successful digital pathology implementation is the impact on work culture, prioritizing digital workflows in a high paced environment, the alignment of pathologists and laboratory staff on digital image availability turnaround time, and the need to define the requirements and expectations around communication and performance. This adoption of disruptive technologies preceding transformation of processes and employee interactions is not unique to digital pathology, and can result in costly failures, lack of workflow change, and flawed organizational practices.Methods: An assessment and identification of workflows and teams affected by the introduction of digital workflow was conducted at Memorial Sloan Kettering's (MSK) Department of Pathology and Laboratory Medicine, an early adopter of digital pathology. The study aimed to define the training needs of the different teams and the change management activities required for the digital pathology transformation through workflow observations and interviews of team members.Results: The digital transformation at MSK started years before the clinical implementation of digital pathology in 2020, and it is still ongoing. These activities included team identification, new departmental workflows development, training materials and townhall meetings, as well as small group training, digital adoption surveys and leadership/champion identification and development in the different teams. The activities are continuing well past the introduction of the new digital workflows into the histology laboratories and are aimed to allow seamless migration from analog to digital workflows while maintaining quality operations metrics and all stakeholder approval.As the current laboratory technologists training program in the US does not include digital pathology workflows, most histotechnologists are not familiar with the Digital Pathology Certificate NSH/DPA. In addition, most laboratory staff is not involved in discussions of digital pathology workflow adoption and will only get trained on the technologies once they get brought into the laboratory. Pathologists were given training sessions in a group setting as well as access to training materials. There were departmental wide town hall meetings that discussed the change in the workflows and gave updates on the timelines and the teams involved.Conclusions: The practice of laboratory medicine and pathology requires multiple teams to be physically present in the workplace to conduct tests and provide coordinated care with clinical teams. Transformations occurring without attention to the changes' impact on worker experience, and how our clinical colleagues experience us, may affect teamwork and morale. To prevent transactional relationships from replacing deeper relationships, full engagement of the pathology and laboratory community in the coming sea change will be necessary. Technology adoption across pathology departments should include clear communications, all stakeholders' identification and involvement, clear goals and policies, review of all existing analog and future digital workflows, and the setting of new roles and responsibilities. Other change management activities include administration support and leadership guidance before, during and after the digital pathology workflows implementation.

Learning Objectives

1. Understand impact of digital pathology implementation on teamwork, culture, and communication.
2. Describe change management activities developed for digital pathology implementation in a clinical setting.            
3. Recommend steps for departmental wide adoption of digital pathology.

Background: Pathologists are becoming increasingly familiar with digital pathology for primary diagnostic use, and the field is witnessing a growth in pathologist buy-in and comfort using the technology. However, in order to enable successful clinical implementation, a suitable, multi-component digital pathology infrastructure must be in place. Each laboratory and organization will require foundational components, including hardware, software, and network components to support the digital pathology system. The exact infrastructure blueprint chosen for institutional deployment will depend on the laboratory use cases, resources, and overall strategy. An often-overlooked component of the clinical digital pathology operation is the need to ensure quality whole slide imaging workflows that will control image quality for the downstream pathology use cases. These quality considerations need to be applied in all phases of the digital pathology process: preanalytical, analytical and post analytical phases and get updated with new technologies and workflows that are being adopted and as scanning volumes increase. The objective of this study is to identify quality processes required for clinical grade digital pathology operations.Methods: Our team at Memorial Sloan Kettering Cancer Center (MSK) started developing a quality management system (QMS) shortly after the integration of digital pathology into our clinical systems in 2020. With the expansion of our operation in recent years, a departmental wide effort to identify additional potential quality needs for the different digitization phases was identified. The effort included multiple teams and a systemic review of all digital pathology related workflows from specimen accessioning to slide storage that led to changes in some of these workflows. The financial implication of these new workflows was calculated to ensure long term sustainability.Results: Identified new workflows in different phases of the pathology operation resulted in changes in glass slide delivery, the addition of automated tracking capabilities, added quality dashboards, turnaround time monitoring and overall lean operations that were integrated in our daily workflows. New centralized scanning laboratory workflows resulted in a decrease in the downtime of the digital scanners. A major effort was given to finding an automated solution for the labor-intensive image quality review process, which was previously found to be essential for our large-scale clinical pathology effort.Conclusions: The transition to fully digital pathology operations requires adjustments to current workflows. A systemic approach to identify quality improvements should be ongoing as technologies and scanning operations expand. The talk will describe these integration efforts and the financial implications of these improvements on our overall pathology operations.

Learning Objectives

1. Understand quality management system requirement for digital pathology.
2. Identify potential quality risks in different digital pathology workflows.
3. Develop an ongoing approach for a sustainable quality management system for their operations.