The introduction of fast and robust whole slide scanners has facilitated the implementation of ‘digital pathology’ with various uses, the final challenge being full digital diagnostics. In this article, we describe the implementation process of a fully digital workflow for primary diagnostics in 2015 at the University Medical Centre in Utrecht, The Netherlands, as one of the first laboratories going fully digital with a future‐proof complete digital archive. Furthermore, we evaluated the experience of the first 2 years of working with the system by pathologists and residents. The system was successfully implemented in 6 months, including a European tender procedure. Most pathologists and residents had high confidence in working fully digitally, the expertise areas lagging behind being paediatrics, haematopathology, and neuropathology. Reported limitations concerned recognition of microorganisms and mitoses. Neither the age of respondents nor the number of years of pathology experience was correlated with the confidence level regarding digital diagnostics. The ergonomics of digital diagnostics were better than those of traditional microscopy. In this article, we describe our experiences in implementing our fully digital primary diagnostics workflow, describing in depth the implementation steps undertaken, the interlocking components that are required for a fully functional digital pathology system (laboratory management, hospital information systems, data storage, and whole slide scanners), and the changes required in workflow and slide production.

Tissues fixed in non-crosslinking alcohol based fixatives can successfully be immunohistochemically stained for most antibodies following the usual NBF based protocols. Omission of pepsin pretreatment seems to be important to retain proper morphology of immunostained tissues preserved in alcohol based fixatives. Therefore, when switching to less toxic and non-carcinogenic alcohol-based fixatives like RCL2, no major changes in the daily routine of immunohistochemistry are anticipated.

Histopathology laboratories aim to deliver high quality diagnoses based on patient tissue samples. Timely and high quality care are essential for delivering high quality diagnoses, for example in cancer diagnostics. However, challenges exist regarding employee workload and tardiness of results, which both impact the diagnostic quality. In this paper the histopathology operations are studied, where tissue processors are modeled as batch processing machines. We develop a new 2-phased decomposition approach to solve this NP-hard problem, aiming to improve the spread of workload and to reduce the tardiness. The approach embeds ingredients from various planning and scheduling problems. First, the batching problem is considered, in which batch completion times are equally divided over the day using a Mixed Integer Linear Program. This reduces the peaks of physical work available in the laboratory. Second, the remaining processes are scheduled to minimize the tardiness of orders using a list scheduling algorithm. Both theoretical as well as historical data were used to assess the performance of the method. Results show that using this decomposition method, the peaks in histopathology workload in UMC Utrecht, a large university medical center in The Netherlands, may be reduced with up to 50 % by better spreading the workload over the day. Furthermore, turnaround times are reduced with up to 20 % compared to current practices. This approach is currently being implemented in the aforementioned hospital.

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BackgroundPathology departments face a growing volume of more and more complex testing in an era where healthcare costs tend to explode and short turnaround times (TATs) are expected. In contrast, the histopathology workforce tends to shrink, so histopathology employees experience high workload during their shifts. This points to the need for efficient planning of activities in the histopathology laboratory, to ensure an equal division of workload and low TATs, at minimum costs.MethodsThe histopathology laboratory of a large academic hospital in The Netherlands was analysed using mathematical modelling. Data were collected from the Laboratory Management System to determine laboratory TATs and workload performance during regular working hours. A mixed integer linear programme (MILP) was developed to model the histopathology processes and to measure the expected performance of possible interventions in terms of TATs and spread of workload.ResultsThe MILP model predicted that tissue processing at specific moments during the day, combined with earlier starting shifts, can result in up to 25% decrease of TATs, and a more equally spread workload over the day.ConclusionsMathematical modelling can help to optimally organise the workload in the histopathology laboratory by predicting the performance of possible interventions before actual implementation. The interventions that were predicted by the model to have the highest performance have been implemented in the histopathology laboratory of University Medical Center Utrecht. Further research should be executed to collect empirical evidence and evaluate the actual impact on TAT, quality of work and employee stress levels.