Steve G. Langer

The paper below had been published originally without open access, but has been republished with open access.

Health Level 7’s (HL7’s) new standard, FHIR (Fast Health Interoperability Resources), is setting healthcare information technology and medical imaging specifically ablaze with excitement. This paper aims to describe the protocol’s advantages in some detail and explore an easy path for those unfamiliar with FHIR to begin learning the standard using free, open-source tools, namely the HL7 application programming interface (HAPI) FHIR server and the SIIM Hackathon Dataset.

Combining imaging biomarkers with genomic and clinical phenotype data is the foundation of precision medicine research efforts. Yet, biomedical imaging research requires unique infrastructure compared with principally text-driven clinical electronic medical record (EMR) data. The issues are related to the binary nature of the file format and transport mechanism for medical images as well as the post-processing image segmentation and registration needed to combine anatomical and physiological imaging data sources. The SiiM Machine Learning Committee was formed to analyze the gaps and challenges surrounding research into machine learning in medical imaging and to find ways to mitigate these issues. At the 2017 annual meeting, a whiteboard session was held to rank the most pressing issues and develop strategies to meet them. The results, and further reflections, are summarized in this paper.

For clinical departments seeking to successfully navigate the challenges of modern health reform, obtaining access to operational and clinical data to establish and sustain goals for improving quality is essential. More broadly, health delivery organizations are also seeking to understand performance across multiple facilities and often across multiple electronic medical record (EMR) systems. Interpreting operational data across multiple vendor systems can be challenging, as various manufacturers may describe different departmental workflow steps in different ways and sometimes even within a single vendor’s installed customer base. In 2012, The Society for Imaging Informatics in Medicine (SIIM) recognized the need for better quality and performance data standards and formed SIIM’s Workflow Initiative for Medicine (SWIM), an initiative designed to consistently describe workflow steps in radiology departments as well as defining operational quality metrics. The SWIM lexicon was published as a working model to describe operational workflow steps and quality measures. We measured the prevalence of the SWIM lexicon workflow steps in both academic and community radiology environments using real-world patient observations and correlated that information with automatically captured workflow steps from our clinical information systems. Our goal was to measure frequency of occurrence of workflow steps identified by the SWIM lexicon in a real-world clinical setting, as well as to correlate how accurately departmental information systems captured patient flow through our health facility.

In 1999–2003, SIIM (then SCAR) sponsored the creation of several special topic Primers, one of which was concerned with computer security. About the same time, a multi-society collaboration authored an ACR Guideline with a similar plot; the latter has recently been updated. The motivation for these efforts was the launch of Health Information Portability and Accountability Act (HIPAA). That legislation directed care providers to enable the portability of patient medical records across authorized medical centers, while simultaneously protecting patient confidentiality among unauthorized agents. These policy requirements resulted in the creation of numerous technical solutions which the above documents described. While the mathematical concepts and algorithms in those papers are as valid today as they were then, recent increases in the complexity of computer criminal applications (and defensive countermeasures) and the pervasiveness of Internet connected devices have raised the bar. This work examines how a medical center can adapt to these evolving threats.

In 2010, the DICOM Data Warehouse (DDW) was launched as a data warehouse for DICOM meta-data. Its chief design goals were to have a flexible database schema that enabled it to index standard patient and study information, modality specific tags (public and private), and create a framework to derive computable information (derived tags) from the former items. Furthermore, it was to map the above information to an internally standard lexicon that enables a non-DICOM savvy programmer to write standard SQL queries and retrieve the equivalent data from a cohort of scanners, regardless of what tag that data element was found in over the changing epochs of DICOM and ensuing migration of elements from private to public tags. After 5 years, the original design has scaled astonishingly well. Very little has changed in the database schema. The knowledge base is now fluent in over 90 device types. Also, additional stored procedures have been written to compute data that is derivable from standard or mapped tags. Finally, an early concern is that the system would not be able to address the variability DICOM-SR objects has been addressed. As of this writing the system is indexing 300 MR, 600 CT, and 2000 other (XA, DR, CR, MG) imaging studies per day. The only remaining issue to be solved is the case for tags that were not prospectively indexed—and indeed, this final challenge may lead to a noSQL, big data, approach in a subsequent version.

This article illustrates the importance of radiologist engagement in the successful implementation of radiology-information technology (IT) projects through the example of establishing a mobile image viewing solution for health care professionals.With an understanding of the types of decisions that benefit from radiologist input, this article outlines an overall project framework to provide a context for how radiologists might engage in the project cycle.

In the era of health information exchanges, there are trade-offs to consider when sharing a patient’s medical record among all providers that a patient might choose. Exchange among in-network partners on the same electronic medical records (EMR) and other integrated information systems is trivial. The patient identifier is common, as are the relevant departmental systems, to all providers. Difficulties arise when patient records including images (and reports) must be shared among different networks and even with the patients themselves. The National Institutes of Health (NIH) challenged Radiological Society of North America (RSNA) to develop a transport method that could supersede the need for physical media (for patients or other providers), replace point-to-point private networks among providers, and enable image exchange on an ad hoc basis between arbitrary health networks without long legal delays. In concert with the evolving US health care paradigm, patient engagement was to be fundamental. With Integrating Healthcare Enterprise’s (IHE’s) help, the challenge has been met with an operational system.

Workflow is a widely used term to describe the sequence of steps to accomplish a task. The use of workflow technology in medicine and medical imaging in particular is limited. In this article, we describe the application of a workflow engine to improve workflow in a radiology department. We implemented a DICOM-enabled workflow engine system in our department. We designed it in a way to allow for scalability, reliability, and flexibility. We implemented several workflows, including one that replaced an existing manual workflow and measured the number of examinations prepared in time without and with the workflow system. The system significantly increased the number of examinations prepared in time for clinical review compared to human effort. It also met the design goals defined at its outset. Workflow engines appear to have value as ways to efficiently assure that complex workflows are completed in a timely fashion.