Numerous initiatives are in place to support value based care in radiology including decision support using appropriateness criteria, quality metrics like radiation dose monitoring, and efforts to improve the quality of the radiology report for consumption by referring providers. These initiatives are largely data driven. Organizations can choose to purchase proprietary registry systems, pay for software as a service solution, or deploy/build their own registry systems. Traditionally, registries are created for a single purpose like radiation dosage or specific disease tracking like diabetes registry. This results in a fragmented view of the patient, and increases overhead to maintain such single purpose registry system by requiring an alternative data entry workflow and additional infrastructure to host and maintain multiple registries for different clinical needs. This complexity is magnified in the health care enterprise whereby radiology systems usually are run parallel to other clinical systems due to the different clinical workflow for radiologists. In the new era of value based care where data needs are increasing with demand for a shorter turnaround time to provide data that can be used for information and decision making, there is a critical gap to develop registries that are more adapt to the radiology workflow with minimal overhead on resources for maintenance and setup. We share our experience of developing and implementing an open source registry system for quality improvement and research in our academic institution that is driven by our radiology workflow.
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http://dx.doi.org/10.1007/s10278-017-9959-4 | DOI Listing |
Emerg Radiol
December 2024
Emergency Radiology, Department of Radiology, Massachusetts General Hospial, Boston, USA.
Background: Emergency/trauma radiology artificial intelligence (AI) is maturing along all stages of technology readiness, with research and development (R&D) ranging from data curation and algorithm development to post-market monitoring and retraining.
Purpose: To develop an expert consensus document on best research practices and methodological priorities for emergency/trauma radiology AI.
Methods: A Delphi consensus exercise was conducted by the ASER AI/ML expert panel between 2022-2024.
Mater Sociomed
January 2024
Al Farabi Kazakh National University, Almaty, Kazakhstan.
Background: Effective radiology diagnostic services are crucial for the timely and precise diagnosis and treatment of stroke, a medical emergency, in multidisciplinary hospitals. However, the efficiency of these services might be impeded by various logistical and operational challenges present in a multidisciplinary hospital setup.
Objective: This review endeavours to explore the ways for optimizing stroke management in multi-disciplinary hospitals, delving into its benefits, current challenges, and future prospects.
Curr Probl Diagn Radiol
December 2024
Department of Radiology, NYU Langone Health, New York, NY, United States. Electronic address:
This paper discusses the use of AutoHotkey (AHK) and programmable peripheral computing devices to enhance the workflow of diagnostic radiologists. Multiple features designed and coded by an emergency teleradiologist to optimize efficiency and complete redundant tasks with ease are presented. The full AutoHotkey script, which currently supports Visage PACS, PowerScribe 360, and Epic EHR, is available in the article appendix.
View Article and Find Full Text PDFJ Dent
December 2024
Centre for Translational Medicine, Semmelweis University, Budapest, Hungary; Department of Prosthodontics, Semmelweis University, Budapest, Hungary. Electronic address:
Objectives: Numerous studies have been conducted on the adaptation of dental restorations fabricated by additive (AM) and subtractive manufacturing (SM); however, the results are conflicting. This systematic review and meta-analysis aimed to evaluate the fit and trueness of fixed restorations made by AM compared to SM.
Data: Studies investigating internal fit, marginal fit, and trueness of fixed prostheses were involved.
Pediatr Radiol
December 2024
Harvard Medical School, 25 Shattuck St, Boston, MA, 02115, USA.
Magnetic resonance imaging (MRI) is an essential tool in pediatric imaging. It offers detailed, high-contrast images without ionizing radiation, making it particularly suitable for children. Creating an efficient MRI service is challenging given the balancing priorities of image quality and scan time and the overlying logistical challenges, including MRI safety protocols, the need for sedation in certain patient populations, and flexibility to accommodate patients at different phases of care.
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