Building blocks for a clinical imaging informatics environment

Online Error Reporting for Managing Quality Control Within Radiology

Information technology systems within health care, such as picture archiving and communication system (PACS) in radiology, can have a positive impact on production but can also risk compromising quality. The widespread use of PACS has removed the previous feedback loop between radiologists and technologists. Instead of direct communication of quality discrepancies found for an examination, the radiologist submitted a paper-based quality-control report. A web-based issue-reporting tool can help restore some of the feedback loop and also provide possibilities for more detailed analysis of submitted errors. The purpose of this study was to evaluate the hypothesis that data from use of an online error reporting software for quality control can focus our efforts within our department. For the 372,258 radiologic examinations conducted during the 6-month period study, 930 errors (390 exam protocol, 390 exam validation, and 150 exam technique) were submitted, corresponding to an error rate of 0.25 %. Within the category exam protocol, technologist documentation had the highest number of submitted errors in ultrasonography (77 errors [44 %]), while imaging protocol errors were the highest subtype error for computed tomography modality (35 errors [18 %]). Positioning and incorrect accession had the highest errors in the exam technique and exam validation error category, respectively, for nearly all of the modalities. An error rate less than 1 % could signify a system with a very high quality; however, a more likely explanation is that not all errors were detected or reported. Furthermore, staff reception of the error reporting system could also affect the reporting rate.

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

Care providers today routinely obtain valuable clinical multimedia with mobile devices, scope cameras, ultrasound, and many other modalities at the point of care. Image capture and storage workflows may be heterogeneous across an enterprise, and as a result, they often are not well incorporated in the electronic health record. Enterprise Imaging refers to a set of strategies, initiatives, and workflows implemented across a healthcare enterprise to consistently and optimally capture, index, manage, store, distribute, view, exchange, and analyze all clinical imaging and multimedia content to enhance the electronic health record. This paper is intended to introduce Enterprise Imaging as an important initiative to clinical and informatics leadership, and outline its key elements of governance, strategy, infrastructure, common multimedia content, acquisition workflows, enterprise image viewers, and image exchange services.

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

OBJECTIVE

We implemented an Image Quality Reporting and Tracking Solution (IQuaRTS), directly linked from the PACS, to improve communication between radiologists and technologists.

MATERIALS AND METHODS

IQuaRTS launched in May 2015. We compared MRI issues filed in the period before IQuaRTS implementation (May-September 2014) using a manual system with MRI issues filed in the IQuaRTS period (May-September 2015). The unpaired t test was used for analysis. For assessment of overall results in the IQuaRTS period alone, all issues filed across all modalities were included. Summary statistics and charts were generated using Excel and Tableau.

RESULTS

For MRI issues, the number of issues filed during the IQuaRTS period was 498 (2.5% of overall MRI examination volume) compared with 78 issues filed during the period before IQuaRTS implementation (0.4% of total examination volume) (p = 0.0001), representing a 625% relative increase. Tickets that documented excellent work were 8%. Other issues included images not pushed to PACS (20%), film library issues (19%), and documentation or labeling (8%). Of the issues filed, 55% were MRI-related and 25% were CT-related. The issues were stratified across six sites within our institution. Staff requiring additional training could be readily identified, and 80% of the issues were resolved within 72 hours.

CONCLUSION

IQuaRTS is a cost-effective online issue reporting tool that enables robust data collection and analytics to be incorporated into quality improvement programs. One limitation of the system is that it must be implemented in an environment where staff are receptive to quality improvement.

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

Content-based medical image retrieval (CBMIR) is a powerful resource to improve differential computer-aided diagnosis. The major problem with CBMIR applications is the semantic gap, a situation in which the system does not follow the users' sense of similarity. This gap can be bridged by the adequate modeling of similarity queries, which ultimately depends on the combination of feature extractor methods and distance functions. In this study, such combinations are referred to as perceptual parameters, as they impact on how images are compared. In a CBMIR, the perceptual parameters must be manually set by the users, which imposes a heavy burden on the specialists; otherwise, the system will follow a predefined sense of similarity. This paper presents a novel approach to endow a CBMIR with a proper sense of similarity, in which the system defines the perceptual parameter depending on the query element. The method employs ensemble strategy, where an extreme learning machine acts as a meta-learner and identifies the most suitable perceptual parameter according to a given query image. This parameter defines the search space for the similarity query that retrieves the most similar images. An instance-based learning classifier labels the query image following the query result set. As the concept implementation, we integrated the approach into a mammogram CBMIR. For each query image, the resulting tool provided a complete second opinion, including lesion class, system certainty degree, and set of most similar images. Extensive experiments on a large mammogram dataset showed that our proposal achieved a hit ratio up to 10% higher than the traditional CBMIR approach without requiring external parameters from the users. Our database-driven solution was also up to 25% faster than content retrieval traditional approaches.