The international symposium entitled “Innovation in Transplantation: The Digital Era” took place on June 7 and 8, 2018 in Verona, Italy. This meeting was borne out of the productive collaboration between the Universities and Hospital Trusts of Verona and Padua in Italy, in the context of a vast regional project called Research and innovation project within the Health Technology Assessment. The project aimed to create an innovative digital platform for teleconsultation and delivering diagnostic second opinions in the field of organ transplantation within the Veneto region. This conference brought together pathologists, health informatics leaders, clinicians, researchers, vendors, and health-care planners from all around the globe. The symposium was conceived to promote the exchange of knowledge and kindle fertile discussion among the 130 attendees from 15 different countries. This article conveys the highlights of this symposium.

Background: Artificial intelligence is advancing at an accelerated pace into clinical applications, providing opportunities for increased efficiency, improved accuracy, and cost savings through computer-aided diagnostics. Dermatopathology, with emphasis on pattern recognition, offers a unique opportunity for testing deep learning algorithms. Aims: This study aims to determine the accuracy of deep learning algorithms to diagnose three common dermatopathology diagnoses. Methods: Whole slide images (WSI) of previously diagnosed nodular basal cell carcinomas (BCCs), dermal nevi, and seborrheic keratoses were annotated for areas of distinct morphology. Unannotated WSIs, consisting of five distractor diagnoses of common neoplastic and inflammatory diagnoses, were included in each training set. A proprietary fully convolutional neural network was developed to train algorithms to classify test images as positive or negative relative to ground truth diagnosis. Results: Artificial intelligence system accurately classified 123/124 (99.45%) BCCs (nodular), 113/114 (99.4%) dermal nevi, and 123/123 (100%) seborrheic keratoses. Conclusions: Artificial intelligence using deep learning algorithms is a potential adjunct to diagnosis and may result in improved workflow efficiencies for dermatopathologists and laboratories.

Background: When a provider orders a test in a pattern that is substantially different than their peers, it may indicate confusion in the test name or inappropriate use of the test, which can be elucidated by initiating dialog between clinicians and the laboratory. However, the analysis of ordering patterns can be challenging. We propose a utilization index (UI) as a means to quantify utilization patterns for individual providers and demonstrate the use of heatmaps to identify opportunities for improvement. Materials and Methods: Laboratory test orders by all providers were extracted from the laboratory information system. Providers were grouped into cohorts based on the specialty and patient population. A UI was calculated for each provider's use of each test using the following formula: (UI = [provider volume of specific test/provider volume of all tests]/[cohort volume of specific test/cohort volume of all tests]). A heatmap was generated to compare each provider to their cohort. Results: This method identified several hot spots and was helpful in reducing confusion and overutilization. Conclusion: The UI is a useful measure of test ordering behavior, and heatmaps provide a clear visual illustration of the utilization indices. This information can be used to identify areas for improvement and initiate meaningful dialog with providers, which will ultimately bring improvement and reduction in costs. Our method is simple and uses resources that are widely available, making this method effective convenient for many other laboratories.

Biomedical informatics is the science of information, where information is defined as data with meaning. This definition identifies a fundamental challenge for informaticians: connecting with the healthcare team by enabling the acquisition, retrieval, and processing of information within the cognitive capabilities of the human brain. Informaticians can become aware of the constraints involved with cognitive processing and with workplace factors that impact how information is acquired and used to facilitate an improved user interface providing information to healthcare teams. Constraints affecting persons in the work environment include as follows: (1) cognitive processing of information; (2) cognitive load and memory capacity; (3) stress-affecting cognition; (4) cognitive distraction, attention, and multitasking; (5) cognitive bias and flexibility; (6) communication barriers; and (7) workplace environment. The human brain has a finite cognitive load capacity for processing new information. Short-term memory has limited throughput for processing of new informational items, while long-term memory supplies immediate simultaneous access to multiple informational items. Visual long-term memories can be extensive and detailed. Attention may be task dependent and highly variable among persons and requires maintaining control over distracting information. Multitasking reduces the effectiveness of working memory applied to each task. Transfer of information from person to person, or machine to person, is subject to cognitive bias and environmental stressors. High-stress levels increase emotional arousal to reduce memory formation and retrieval. The workplace environment can impact cognitive processes and stress, so maintaining civility augments cognitive abilities. Examples of human-computer interfaces employing principles of cognitive informatics inform design of systems to enhance the user interface.