Challenges in implementing a digital pathology workflow in surgical pathology - Giovani Lujan; Zaibo Li; Anil V. Parwani (Elsevier, 2022)
The rate of digital pathology adoption had been slow until the COVID-19 pandemic served as catalyst, demonstrating the possibilities of saving time and money by converting glass slides into whole slide images (WSI) and thus making them available to all staff pathologist immediately after, regardless of where the pathologists are located, in their office, at home or travel location. Regardless of the cataclysmic proportions of this event in health care and in pathology specifically speaking, the transition is still slow. After the COVID 19 pandemic, there was an increase in interest among laboratories and pathology groups. We recognize two periods, pre COVID 19 with low interest and low conversion rates; and during COVID 19 where the interest grew exponentially but not much so the adoption. Right now, we are on this high interest, but low adoption period. Since it is not for lack of interest from pathologists and administrators, now the issue is that all these interested parties have found out that the transition process is intrinsically slow and full of challenges.In this paper we are reviewing the main challenges that laboratories are facing in the adoption process, some are obvious, other are not so much. Those challenges can be divided in financial, technical, and perhaps the most difficult to overcome, changing management. All these challenges are not insurmountable, and the key is not to tackle them when they appear, but far in advance, this is accomplished by a thorough investigation of the variables and the particular use cases of the institution and to start educating medical and non-medical stakeholders about the benefits of a digital workflow, how these benefits will definitively pay off the investment and will render the laboratory not only prepared for other disruptive event like COVID 19, but it will also make them ready for the fast growing trend of utilizing machine learning and artificial intelligence to analyze images and finetune the diagnosis.