Picture Archiving and Communication System (PACS) Implementation, Integration &amp; Benefits in an Integrated Health System

Mansoori, Bahar; Erhard, Karen K.; Sunshine, Jeffrey L.

doi:10.1016/j.acra.2011.11.009

Cited by 38 publications

(28 citation statements)

References 24 publications

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“…With one out of four Americans receiving a CT examination 89 and one out of ten receiving an MRI examination 90 annually, millions of medical images are produced each year. Additionally, recent well-implemented advances in US-based digital health systems — such as the Picture Archiving and Communication System (PACS) — have ensured that medical images are electronically organized in a systematic manner 91,92 , with parallel efforts in Europe 93 and developing countries 94 . It is clear that large amounts of medical data are indeed available and are stored in such a manner that enables moderate ease in access and retrieval.…”

Section: Ai Challenges In Medical Imagingmentioning

confidence: 99%

Artificial intelligence in radiology

et al. 2018

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Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. In this O pinion article, we establish a general understanding of AI methods, particularly those pertaining to image-based tasks. We explore how these methods could impact multiple facets of radiology, with a general focus on applications in oncology, and demonstrate ways in which these methods are advancing the field. Finally, we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced.

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Section: Ai Challenges In Medical Imagingmentioning

confidence: 99%

Artificial intelligence in radiology

et al. 2018

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“…The implementation of a PACS provides an overall boost of productivity to an institution and decreases its operational costs [8]. It also creates an excellent opportunity for empowering healthcare practitioners with the capacity to work remotely or perform telemedicine, providing collaborative work environments and facilitating the access and sharing of multimedia information.…”

Section: Picture Archiving and Communication Systemmentioning

confidence: 99%

Anatomy of an Extensible Open Source PACS

et al. 2015

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The conception and deployment of cost effective Picture Archiving and Communication Systems (PACS) is a concern for small to medium medical imaging facilities, research environments, and developing countries' healthcare institutions. Financial constraints and the specificity of these scenarios contribute to a low adoption rate of PACS in those environments. Furthermore, with the advent of ubiquitous computing and new initiatives to improve healthcare information technologies and data sharing, such as IHE and XDS-i, a PACS must adapt quickly to changes. This paper describes Dicoogle, a software framework that enables developers and researchers to quickly prototype and deploy new functionality taking advantage of the embedded Digital Imaging and Communications in Medicine (DICOM) services. This full-fledged implementation of a PACS archive is very amenable to extension due to its plugin-based architecture and out-of-the-box functionality, which enables the exploration of large DICOM datasets and associated metadata. These characteristics make the proposed solution very interesting for prototyping, experimentation, and bridging functionality with deployed applications. Besides being an advanced mechanism for data discovery and retrieval based on DICOM object indexing, it enables the detection of inconsistencies in an institution's data and processes. Several use cases have benefited from this approach such as radiation dosage monitoring, Content-Based Image Retrieval (CBIR), and the use of the framework as support for classes targeting software engineering for clinical contexts.

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“…However, in 2006 a large-scale assessment of a hospitalwide PACS implementation revealed a net savings of $485,157 [3]. Perhaps most importantly, in all of these studies radiologist productivity increased, patient waiting time decreased, and critical results were relayed more efficiently [3][4][5]. Yet, despite these benefits, every PACS platform has hurdles to overcome.…”

Section: Introductionmentioning

confidence: 99%

“…Several studies found implementing a uniform PACS platform from a film-based system improves efficiency, enhances collaboration, and decreases costs [3][4][5]. Early financial assessments suggested that PACS might actually increase costs due to the significant investments required for both hardware and software.…”

Section: Introductionmentioning

confidence: 99%

“…Yet, despite these benefits, every PACS platform has hurdles to overcome. A recent study of Case Western's implementation of a new PACS platform identified unexpected network issues due to high latency of images, which required widespread upgrades in bandwidth across several hospitals within the system [5]. While each hospital must carefully select a PACS based on criteria that suit its own needs, a few qualities have universally stood out.…”

Section: Introductionmentioning

confidence: 99%

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PACS Used While On-Call: A National Survey of Radiology Program Directors and Chief Residents

2014

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The aim of this study was to determine the prevalence of different diagnostic image viewing platforms used by radiologists while on-call, and to assess the opinions and preferences of radiology program directors and chief residents regarding their use. An online survey was sent electronically to radiology residency program directors and chief residents via the Association of University Radiologists. Forty-two radiology program directors and 25 chief residents completed the survey, yielding response rates of 24.9 and 8.5 %, respectively. From the survey results, 10 different Picture Archiving Communications Systems (PACS) were identified; GE (25 %), Philips (17 %), and Agfa Impax (15 %) were the most prevalent. Interestingly, only 5 % of all respondents use a secondary "Digital Imaging and Communications in Medicine" viewer for oncall studies. Perceptions of PACS functionality were generally neutral to weakly positive. Most respondents strongly agreed that it is important to have a single integrated PACS for viewing oncall studies and agreed that the PACS should be integrated into the Electronic Medical Records (EMR). The overwhelming majority of respondents use their institution's PACS while oncall. The results show there is still a wide variety of PACS platforms used by different institutions; however, GE, Phillips, and Agfa were some of the most prevalent. Most radiologists surveyed have neutral to slightly positive perceptions about the functionality and ease of use of their PACS. Finally, while radiologists agree that PACS should be integrated with EMR, only 53 % of respondents currently have this arrangement.

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Picture Archiving and Communication System (PACS) Implementation, Integration & Benefits in an Integrated Health System

Cited by 38 publications

References 24 publications

Artificial intelligence in radiology

Artificial intelligence in radiology

Anatomy of an Extensible Open Source PACS

PACS Used While On-Call: A National Survey of Radiology Program Directors and Chief Residents

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