Technical Challenges of Enterprise Imaging: HIMSS-SIIM Collaborative White Paper

Clunie, David A.; Dennison, Don K.; Cram, Dawn; Persons, Kenneth R.; Bronkalla, Mark D.; Primo, Henri Rik

doi:10.1007/s10278-016-9899-4

Cited by 35 publications

(43 citation statements)

References 82 publications

(40 reference statements)

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“…Future enhancements could provide the ability to define additional criteria such as referring physician and time of examination. Current standards that can be adapted to serve this function of the PACS / RIS monitor include the DICOM/IHE Instance Availability Notification (IAN) (7) . DICOMweb standards can be used to support offsite image analysis, which would be limited by more traditional DICOM transfer methods (8) .…”

Section: Discussionmentioning

confidence: 99%

Building Blocks for Integrating Image Analysis Algorithms into a Clinical Workflow

Juluru

Shih

Elnajjar

et al. 2020

Preprint

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Purpose Starting from a broad-based needs assessment and utilizing an image analysis algorithm (IAA) developed at our institution, the purpose of this study was to define generalizable building blocks necessary for the integration of any IAA into a clinical practice. Methods An IAA was developed in our institution to process lymphoscintigraphy exams. A team of radiologists defined a set of building blocks for integration of this IAA into clinical workflow. The building blocks served the following roles: (1) Timely delivery of images to the IAA, (2) quality control, (3) IAA results processing, (4) results presentation & delivery, (5) IAA error correction, (6) system performance monitoring, and (7) active learning. Utilizing these modules, the lymphoscintigraphy IAA was integrated into the clinical workflow at our institution. System performance was tested over a 1 month period, including assessment of number of exams processed and delivered, and error rates and corrections. Results From June 26-July 27, 2019, the building blocks were used to integrate IAA results from 132 lymphoscintigraphy exams into the clinical workflow, representing 100% of the exams performed during the time period. The system enabled radiologists to correct 21 of the IAA results. All results and corrections were successfully stored in a database. A dashboard allowed the development team to monitor system performance in real-time. Conclusions We describe seven building blocks that optimize the integration of IAAs into clinical workflow. The implementation of these building blocks in this study can be used to inform development of more robust, standards-based solutions.

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Section: Discussionmentioning

confidence: 99%

Building Blocks for Integrating Image Analysis Algorithms into a Clinical Workflow

Juluru

Shih

Elnajjar

et al. 2020

Preprint

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“…HIMSS-SIIM have attempted to provide solutions to challenges identified in their white papers but these remain as guidelines. 37,38 Although a minor issue, there are some image producers who do not appreciate the accessibility of patient images or the integration into a longitudinal medical record. These image producers believe that only the diagnostic report is the significant product and that images are only important to the diagnostic interpreter.…”

Section: Workflow Changes Of Enterprise Imagingmentioning

confidence: 99%

“…Some technical limitations are identified in a white paper by Clunie et al 37 They identify that with diagnostic imaging, some ECG systems will maintain raw data in proprietary formats and only allow a post processed image of the waveform through. Similarly, ophthalmic imaging devices do the same but only allow software analysis to be achieved through vendor specific software.…”

Section: Workflow Changes Of Enterprise Imagingmentioning

confidence: 99%

“…However, in integration to enterprise imaging storage, VL requires the support of true colour images, different metadata requirements and acquisition workflow as opposed to conventional radiology. 37 Images that undergo lossy compression can lose accurate detail and start to blur on review. Lossless compression can preserve this image information but will result in larger file sizes.…”

Section: Workflow Changes Of Enterprise Imagingmentioning

confidence: 99%

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Enterprise Imaging: The Next Frontier in Healthcare Technology–A Liturature Review

Liao

Seeram²

2018

Radiol Open J

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A review of the literature was performed to evaluate, review and discuss the imaging systems of picture archiving and communication system (PACS), vendor neutral archive (VNAs) and enterprise imaging. Method A search through the databases of PubMed, OvidMedline, Scopus and Embase was performed utilizing several keywords relevant to image storage in various combinations of "OR" and "AND". Articles were carefully assessed according to inclusion and exclusion criteria where only articles published in the last 10 years were obtained to collect recent information. Additionally, articles found from the reference sections of electronically sourced articles were also reviewed. Results The review revealed that traditional PACS suffer from several limitations of data storage, migration and maintenance. VNAs improve upon this situation by allowing images from different departments to be shared easily and extend this ability to in between organizations. Physical copies of images are no longer needed and applications such as teleradiology and mobile imaging are possible. Enterprise imaging attempts to provide a format that allows for organizations to govern the management of image sharing and storage between institutions using a set of 7 characteristics that define an efficient system. Conclusion Image storage technology has experienced several advancements in recent years. Traditional PACS imaging has allowed for image capture, viewing, storage and analysis but is unable to perform effective image sharing across institutions. VNAs have provided a system to surpass this limitation by normalizing proprietary digital imaging and communications in medicine (DICOM) formats used by PACS vendors. With the advent of new technology, enterprise imaging has been developed as a system that enables the management of multi-departmental and multi-institutional image sharing in one system.

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“…Many of the solutions in the USA are still proprietary. & Image type standardization [6]. Though one might immediately think of the Digital Imaging and Communications in Medicine (DICOM) [7] standard when discussing medical imaging, providers also need solutions that address images and related content in other formats, such as a JPEG image of a wound captured with a digital camera or a PDF of an EKG tracing.…”

Section: Introductionmentioning

confidence: 99%

Interoperability and Considerations for Standards-Based Exchange of Medical Images: HIMSS-SIIM Collaborative White Paper

Persons

Nagels²,

Carr

et al. 2019

J Digit Imaging

Self Cite

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This white paper explores the considerations of standards-based interoperability of medical images between organizations, patients, and providers. In this paper, we will look at three different standards-based image exchange implementations that have been deployed to facilitate exchange of images between provider organizations. The paper will describe how each implementation uses applicable technology and standards; the image types that are included; and the governance policies that define participation, access, and trust. Limitations of the solution or non-standard approaches to solve challenges will also be identified. Much can be learned from successes elsewhere, and those learnings will point to recommendations of best practices to facilitate the adoption of image exchange.

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Technical Challenges of Enterprise Imaging: HIMSS-SIIM Collaborative White Paper

Cited by 35 publications

References 82 publications

Building Blocks for Integrating Image Analysis Algorithms into a Clinical Workflow

Building Blocks for Integrating Image Analysis Algorithms into a Clinical Workflow

Enterprise Imaging: The Next Frontier in Healthcare Technology–A Liturature Review

Interoperability and Considerations for Standards-Based Exchange of Medical Images: HIMSS-SIIM Collaborative White Paper

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