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Lessons Learned from Filmless Radiology for Slideless Pathology

BY Metin Gurcan. February 10, 2015

Background: Adoption of digital images for pathological specimens has been slower than adoption of digital images in radiology, despite a number of anticipated advantages for digital images in pathology. In this paper, we explore the factors that might explain this slower rate of adoption for “slideless” pathology.

Materials and Method: Semi-structured interviews on barriers and facilitators to the adoption of digital images were conducted with two radiologists, three pathologists, and one pathologist’s assistant.

Results: Barriers and facilitators to adoption of digital images were reported in the areas of performance, workflow-efficiency, infrastructure, integration with other software, and exposure to digital images.The primary difference between the settings was that performance with the use of digital images as compared to the traditional method was perceived to be higher in radiology and lower in pathology. Additionally, exposure to digital images was higher in radiology than pathology, with some radiologists exclusively having been trained and/or practicing with digital images. The integration of digital images both improved and reduced efficiency in routine and non- routine workflow patterns in both settings, and was variable across the different organizations. A comparison of these findings with prior research on adoption of other health information technologies suggests that the barriers to adoption of digital images in pathology are relatively tractable.

Conclusions: Improving performance using digital images in pathology would likely accelerate adoption of innovative technologies that are facilitated by the use of digital images, such as electronic imaging databases, electronic health records, double reading for challenging cases, and computer-aided diagnostic systems.

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Metin Gurcan

Dr. Gurcan is an Associate Professor, Biomedical Informatics at the Ohio State University and the founding director of the Clinical Image Analysis Lab there. He is an image analysis scientist who has ... Learn More