Should Medical Schools Incorporate Formal Training in Informatics?

Are we preparing future generations of physicians with the skills to practice in the information age? Has the health care IT industry matured to the stage that we can standardize training physicians in how to search and synthesize massive databases of clinical information and tease out complex diagnoses based upon scant information? Will literacy in information technology become a differentiator between physicians’ abilities? For the proposition of changing existing curriculum in medical schools to incorporate formal informatics training is Michael Chen, a second year medical student at the University of Maryland School of Medicine. Taking the opposing position is Nabile Safdar, M.D., assistant professor of radiology at the University of Maryland School of Medicines

A DICOM Framework for Machine Learning and Processing Pipelines Against Real-time Radiology Images

Real-time execution of machine learning (ML) pipelines on radiology images is difficult due to limited computing resources in clinical environments, whereas running them in research clusters requires efficient data transfer capabilities. We developed Niffler, an open-source Digital Imaging and Communications in Medicine (DICOM) framework that enables ML and processing pipelines in research clusters by efficiently retrieving images from the hospitals’ PACS and extracting the metadata from the images. We deployed Niffler at our institution (Emory Healthcare, the largest healthcare network in the state of Georgia) and retrieved data from 715 scanners spanning 12 sites, up to 350 GB/day continuously in real-time as a DICOM data stream over the past 2 years. We also used Niffler to retrieve images bulk on-demand based on user-provided filters to facilitate several research projects. This paper presents the architecture and three such use cases of Niffler. First, we executed an IVC filter detection and segmentation pipeline on abdominal radiographs in real-time, which was able to classify 989 test images with an accuracy of 96.0%. Second, we applied the Niffler Metadata Extractor to understand the operational efficiency of individual MRI systems based on calculated metrics. We benchmarked the accuracy of the calculated exam time windows by comparing Niffler against the Clinical Data Warehouse (CDW). Niffler accurately identified the scanners’ examination timeframes and idling times, whereas CDW falsely depicted several exam overlaps due to human errors. Third, with metadata extracted from the images by Niffler, we identified scanners with misconfigured time and reconfigured five scanners. Our evaluations highlight how Niffler enables real-time ML and processing pipelines in a research cluster

Overview of Noninterpretive Artificial Intelligence Models for Safety, Quality, Workflow, and Education Applications in Radiology Practice

Artificial intelligence has become a ubiquitous term in radiology over the past several years, and much attention has been given to applications that aid radiologists in the detection of abnormalities and diagnosis of diseases. However, there are many potential applications related to radiologic image quality, safety, and workflow improvements that present equal, if not greater, value propositions to radiology practices, insurance companies, and hospital systems. This review focuses on six major categories for artificial intelligence applications: study selection and protocoling, image acquisition, worklist prioritization, study reporting, business applications, and resident education. All of these categories can substantially affect different aspects of radiology practices and workflows. Each of these categories has different value propositions in terms of whether they could be used to increase efficiency, improve patient safety, increase revenue, or save costs. Each application is covered in depth in the context of both current and future areas of work

A Suggested Classification Guide for PACS Client Applications: The Five Degrees of Thickness

This article defines and describes the numerous types of “clients” for picture archiving and communication systems (PACS). A radiologist uses a client to view images stored in the system. Many PACS are available in the market, and each offers different methods by which a client can view images from the server. The terminology used to describe these different methods can cause confusion and lead to poor choice for those imaging team members who are given the task of purchasing, implementing, and supporting the PACS. We propose a classification of clients with respect to their impact on client work stations, an effect often referred to as the application’s thickness. The thinner the client, the less effect it has on the hosting work station. In contrast, a thick client consumes the work station’s resources and often prevents a work station from being used to effectively run anything other than the client application. Functionality and supportability are highlighted as key and interacting metrics in determining optimal correct PACS solutions. The importance of a clear understanding of the needs and requirements of all users as well as the client application is emphasized. This relationship between supportability and functionality becomes increasingly important as the industry shifts to enterprise information technology solutions

Unbiased review of digital diagnostic images in practice: informatics prototype and pilot study

RATIONALE AND OBJECTIVES: Clinical and contextual information associated with images may influence how radiologists draw diagnostic inferences, highlighting the need to control multiple sources of bias in the methodological design of investigations involving radiological interpretation. In the past, manual control methods to mask review films presented in practice have been used to reduce potential interpretive bias associated with differences between viewing images for patient care versus reviewing images for purposes of research, education, and quality improvement. These manual precedents from the film era raise the question whether similar methods to reduce bias can be implemented in the modern digital environment. MATERIALS AND METHODS: We built prototype “CreateAPatient” information technology for masking review case presentations within our institution’s production Radiology Information and Picture Archiving and Reporting Systems (RIS and PACS). To test whether CreateAPatient could be used to mask review images presented in practice, six board-certified radiologists participated in a pilot study. During pilot testing, seven digital chest radiographs, known to contain lung nodules and associated with fictitious patient identifiers, were mixed into the routine workload of the participating radiologists while they covered general evening call shifts. We tested whether it was possible to mask the presentation of these review cases, both by probing the interpreting radiologists to report detection and by conducting a forced-choice experiment on a separate cohort of 20 radiologists and information technology professionals. RESULTS: None of the participating radiologists reported awareness of review activity, and forced-choice detection was less than predicted at chance, suggesting radiologists were effectively blinded. In addition, we identified no evidence of review reports unsafely propagating beyond their intended scope or otherwise interfering with patient care, despite integration of these records within production electronic workflow systems. CONCLUSION: Information technology can facilitate the design of unbiased methods involving professional review of digital diagnostic images