Benefits to AICloudQA

  • Simultaneous Prospective and Retrospective Peer Learning:  The first platform capable of simultaneous prospective and retrospective peer learning and review of current and historical studies.

  • Highest Granularity System for User and Case Specific Continuous Quality Improvement:  provides adjustable, user-specific sampling by study type and sub-speciality.  This provides high granularity, high value, continuous quality improvement specific to each individual user and responsive to their peer learning experience and performance over time.

  • All User Peer Review Feedback:  integrates peer review into your processes and workflows, it provides a continuous educational opportunity.  Specialists and medical learners (e.g., resident to radiologist, resident to resident, technologist to radiologist and technologists to technologists) benefit from performance enhancing, ongoing, anonymized feedback and acquisition of additional knowledge through collaboration and AI assisted pattern recognition and learning assistance. 

  • Minimal Impact to Workflow:  uses standards based messaging to communicate to your current image acquisition systems and presents physicians with a browser based, required actions worklist and diagnostic quality viewer to create a convenient, peer learning experience. 

  • Best Practices: meets and exceeds CAR, ACR, Health Quality Ontario and the UK Royal College Guidelines for “ideal” and “best practice” peer review system characteristics including workload balanced peer review.  Additionally, AICloudQA provides “on the fly” anonymized Peer Review in accordance with best practices, whereby neither the original specialist nor the reviewer knows the other’s identity for enhanced objectivity.

  • Extensive Analytics: Create customizable dashboards or select your own metrics to view and export a wide range of data. 

  • Patient Critical Results Communication:  Unlike other systems, AICloudQATM closes the loop for critical results notification and management.  It comes complete with automated monitoring of critical findings, acknowledgements and required escalation.

  • Cloud-Based:  removes the need for concern over the acquisition, cost and maintenance of your own servers unless on-site solution deployment is preferred.

  • Offsets Increased Workload:  This is the first system on the market capable of providing clients with workload balanced peer review.  Important because workload and fatigue can themselves contribute to diagnostic discrepancies, the ability of RTM’s solution to provide workload balanced peer learning and diagnostic workload balancing in general, helps to offset the increased work associated with peer learning while contributing to quality improvement objectives overall. 

  • AI Assisted Peer Learning:  Will dramatically improve peer learning potential by enabling a much broader and much more rapid scope and scale of review, analysis and user specific recommendations. 

  • Dramatic Increase in Clinical Efficacy:  dramatically improves both the clinical efficacy of peer learning (for the benefit of patients) and the clinical efficiency of the peer learning processes (for the benefit of physicians).

  • Multidisciplinary/Multi-ology:  applicable across multiple medical disciplines.

  • System Neutrality:  As an independent, HIS/RIS/PACS neutral solution, Google and Real Time Medical’s AICloudQA will provide users with ongoing flexibility in your IT strategy and options going forward while leveraging your existing infrastructure.

The Future of Peer Review


AICloudQA™  is a highly secure, expandable healthcare quality assurance system integrating Google’s Cloud Platform, Client Outlook’s eUnity viewer and Real Time Medical’s Intelligent Peer Review solution. The system combines AI-assisted peer learning and AI-assisted workload balancing to increase clinical efficacy and improve acute care performance. AICloudQA™  is currently available for Radiology and Pathology.

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