- AI
- Molecular Imaging
- CT
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- Ultrasound
- MRI
- Facility Management
- Mammography
HIMSS Video Blog: The challenge of interoperability
It was designed as a showcase for the nexus of standards and IT, a testament to the ability of divergent systems to work together in a health information exchange. This “interoperability showcase,” an island on the HIMSS09 exhibit floor of kiosks, employed various clinical scenarios to demonstrate the potential of IT to deliver information that can make a crucial difference in the care of patients.
It was designed as a showcase for the nexus of standards and IT, a testament to the ability of divergent systems to work together in a health information exchange. This “interoperability showcase,” an island on the HIMSS09 exhibit floor of kiosks, employed various clinical scenarios to demonstrate the potential of IT to deliver information that can make a crucial difference in the care of patients.
John T. Donnelly, president of the consulting firm IntePro Solutions, and co-chair of the Integrating the Healthcare Enterprise Patient Care Coordination Planning Committee, notes that standard bodies define the way messages are exchanged between different information systems. But there’s more to making information useful than just exchanging messages.
Four Strategies to Address the Tipping Point in Radiology
January 17th 2025In order to flip the script on the impact of the radiology workforce shortage, radiology groups and practices need to make sound investments in technologies and leverage partnerships to mitigate gaps in coverage and maximize workflow efficiencies.
Can Generative AI Facilitate Simulated Contrast Enhancement for Prostate MRI?
January 14th 2025Deep learning synthesis of contrast-enhanced MRI from non-contrast prostate MRI sequences provided an average multiscale structural similarity index of 70 percent with actual contrast-enhanced prostate MRI in external validation testing from newly published research.
Can MRI-Based AI Enhance Risk Stratification in Prostate Cancer?
January 13th 2025Employing baseline MRI and clinical data, an emerging deep learning model was 32 percent more likely to predict the progression of low-risk prostate cancer (PCa) to clinically significant prostate cancer (csPCa), according to new research.
