Building Blocks for a Clinical Imaging Informatics Environment

Abstract

Over the past 20 years, imaging informatics has been driven by the widespread adoption of radiology information and picture archiving and communication and speech recognition systems. These three clinical information systems are commonplace and are intuitive to most radiologists as they replicate familiar paper and film workflow. So what is next? There is a surge of innovation in imaging informatics around advanced workflow, search, electronic medical record aggregation, dashboarding, and analytics tools for quality measures (Nance et al., AJR Am J Roentgenol 200:1064-1070, 2013). The challenge lies in not having to rebuild the technological wheel for each of these new applications but instead attempt to share common components through open standards and modern development techniques. The next generation of applications will be built with moving parts that work together to satisfy advanced use cases without replicating databases and without requiring fragile, intense synchronization from clinical systems. The purpose of this paper is to identify building blocks that can position a practice to be able to quickly innovate when addressing clinical, educational, and research-related problems. This paper is the result of identifying common components in the construction of over two dozen clinical informatics projects developed at the University of Maryland Radiology Informatics Research Laboratory. The systems outlined are intended as a mere foundation rather than an exhaustive list of possible extensions.

Implementation of a Point-of-Care Radiologist-Technologist Communication Tool in a Quality Assurance Program

“…Several reusable components for imaging informatics have been previously described [6]. To achieve the desired needs, we devised a system consisting of PACS context integration with a Web application, a Web browser–based interface, a database, a ticketing system, and a dashboarding tool (Fig.…”

A Foundation for Enterprise Imaging: HIMSS-SIIM Collaborative White Paper

“…The EI platform provides an infrastructure that supplies both the image and associated metadata to enterprise data warehouses. Defining and standardizing this metadata across the enterprise provides a repository of data that can provide departments with detailed study statistics, utilization reports, and image acquisition patterns [ 51 – 54 ]. True deep learning and complex neural networks of image data contents are beginning to be developed that will play large roles in the future of EI [ 55 – 58 ].…”

Endowing a Content-Based Medical Image Retrieval System with Perceptual Similarity Using Ensemble Strategy

“…Distinct approaches such as ranking strategies, classification, or similarity queries [1,17] can be used for content retrieval. According to similarity queries in metric spaces, a CBMIR architecture can be designed as interactions between offline and online modules [12,9]. Figure 1 illustrates these modules and their interactions.…”

“…In CBMIR systems, an image can be retrieved by ranking strategies or similarity queries. The similarity queries paradigm has been widely adopted by content-based image retrieval systems [8,9] as it supports both handling and indexing of large datasets. Moreover, it relies on relational database management systems that can be straightforwardly connected to other clinical systems (e.g., PACS) [10].…”