Health care is undergoing a profound transformation driven by an increase in new types of diagnostic data, increased data sharing enabled by interoperability, and improvements in our ability to interpret data through the application of artificial intelligence and machine learning. Paradoxically, we are also discovering that our current paradigms for implementing electronic health-care records and our ability to create new models for reforming the health-care system have fallen short of expectations. This article traces these shortcomings to two basic issues. The first is a reliance on highly centralized quality improvement and measurement strategies that fail to account for the high level of variation and complexity found in human disease. The second is a reliance on legacy payment systems that fail to reward the sharing of data and knowledge across the health-care system. To address these issues, and to better harness the advances in health care noted above, the health-care system must undertake a phased set of reforms. First, efforts must focus on improving both the diagnostic process and data sharing at the local level. These efforts should include the formation of diagnostic management teams and increased collaboration between pathologists and radiologists. Next, building off current efforts to develop national federated research databases, providers must be able to query national databases when information is needed to inform the care of a specific complex patient. In addition, providers, when treating a specific complex patient, should be enabled to consult nationally with other providers who have experience with similar patient issues. The goal of these efforts is to build a health-care system that is funded in part by a novel fee-for-knowledge-sharing paradigm that fosters a collaborative decentralized approach to patient care and financially incentivizes large-scale data and knowledge sharing.

Background: Little is known about the effect of a minimum number of slides required in generating image datasets used to build generalizable machine-learning (ML) models. In addition, the assumption within deep learning is that the increased number of training images will always enhance accuracy and that the initial validation accuracy of the models correlates well with their generalizability. In this pilot study, we have been able to test the above assumptions to gain a better understanding of such platforms, especially when data resources are limited. Methods: Using 10 colon histology slides (5 carcinoma and 5 benign), we were able to acquire 1000 partially overlapping images (Dataset A) that were then trained and tested on three convolutional neural networks (CNNs), ResNet50, AlexNet, and SqueezeNet, to build a large number of unique models for a simple task of classifying colon histopathology into benign and malignant. Different quantities of images (10–1000) from Dataset A were used to construct >200 unique CNN models whose performances were individually assessed. The performance of these models was initially assessed using 20% of Dataset A's images (not included in the training phase) to acquire their initial validation accuracy (internal accuracy) followed by their generalization accuracy on Dataset B (a very distinct secondary test set acquired from public domain online sources).Results: All CNNs showed similar peak internal accuracies (>97%) from the Dataset A test set. Peak accuracies for the external novel test set (Dataset B), an assessment of the ability to generalize, showed marked variation (ResNet50: 98%; AlexNet: 92%; and SqueezeNet: 80%). The models with the highest accuracy were not generated using the largest training sets. Further, a model's internal accuracy did not always correlate with its generalization accuracy. The results were obtained using an optimized number of cases and controls. Conclusions: Increasing the number of images in a training set does not always improve model accuracy, and significant numbers of cases may not always be needed for generalization, especially for simple tasks. Different CNNs reach peak accuracy with different training set sizes. Further studies are required to evaluate the above findings in more complex ML models prior to using such ancillary tools in clinical settings.

Background: Free-text sections of pathology reports contain the most important information from a diagnostic standpoint. However, this information is largely underutilized for computer-based analytics. The vast majority of NLP-based methods lack a capacity to accurately extract complex diagnostic entities and relationships among them as well as to provide an adequate knowledge representation for downstream data-mining applications. Methods: In this paper, we introduce a novel informatics pipeline that extends open information extraction (openIE) techniques with artificial intelligence (AI) based modeling to extract and transform complex diagnostic entities and relationships among them into Knowledge Graphs (KGs) of relational triples (RTs). Results: Evaluation studies have demonstrated that the pipeline's output significantly differs from a random process. The semantic similarity with original reports is high (Mean Weighted Overlap of 0.83). The precision and recall of extracted RTs based on experts' assessment were 0.925 and 0.841 respectively (P <0.0001). Inter-rater agreement was significant at 93.6% and inter-rated reliability was 81.8%. Conclusion: The results demonstrated important properties of the pipeline such as high accuracy, minimality and adequate knowledge representation. Therefore, we conclude that the pipeline can be used in various downstream data-mining applications to assist diagnostic medicine.

Background: Previous studies have demonstrated the noninferiority of pathologists' interpretation of whole slide images (WSIs) compared to microscopic slides in diagnostic surgical pathology; however, to our knowledge, no published studies have tested analytical precision of an entire WSI system. Methods: In this study, five pathologists at three locations tested intra-system, inter-system/site, and intra- and inter-pathologist precision of the Aperio AT2 DX System (Leica Biosystems, Vista, CA, USA). Sixty-nine microscopic slides containing 23 different morphologic features suggested by the Digital Pathology Association as important to diagnostic pathology were identified and scanned. Each of 202 unique fields of view (FOVs) had 1–3 defined morphologic features, and each feature was represented in three different tissues. For intra-system precision, each site scanned 23 slides at three different times and one pathologist interpreted all FOVs. For inter-system/site precision, all 69 slides were scanned once at each of three sites, and FOVs from each site were read by one pathologist. To test intra- and inter-pathologist precision, all 69 slides were scanned at one site, FOVs were saved in three different orientations, and the FOVs were transferred to a different site. Three different pathologists then interpreted FOVs from all 69 slides. Wildcard (unscored) slides and washout intervals were included in each study. Agreement estimates with 95% confidence intervals were calculated. Results: Combined precision from all three studies, representing 606 FOVs in each of the three studies, showed overall intra-system agreement of 97.9%; inter-system/site agreement was 96%, intra-pathologist agreement was 95%, and inter-pathologist agreement was 94.2%. Conclusions: Pathologists using the Aperio AT2 DX System identified histopathological features with high precision, providing increased confidence in using WSI for primary diagnosis in surgical pathology.