AICloudQA™

The first platform is capable of simultaneous prospective and retrospective peer learning, significantly mitigating the risk profile of your operations.

Provides automated, user-specific sampling by study type and sub-specialty. This provides high granularity, high value, continuous quality improvement specific to each user and responsive to each individual’s peer learning experience and performance over time.  It is the highest quality assurance in Diagnostic Radiology.

Radiologist to resident, resident to resident, technologist to radiologist and technologists to technologists. All benefit from performance-enhancing, ongoing, anonymized feedback and acquisition of additional knowledge through collaboration and AI-assisted learning.

Uses standards-based messaging to communicate with other systems and presents physicians with a browser-based, worklist and diagnostic quality viewer to create a peer learning solution that’s compatible with any environment.

Meets and exceeds CAR, ACR, Health Quality Ontario and the UK Royal College Guidelines for “best practice” peer review system characteristics including workload balanced peer review. Additionally, AICloudQA provides cross-site, anonymized Peer Review to further enhance reviewer and reviewee anonymity, objectivity and results validity.

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

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.

Removes the need for concern over the acquisition, cost and maintenance of your own servers unless on-site solution deployment is preferred.

The first system on the market capable of providing clients with workload balanced peer review and production reading. This is 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.

Dramatically improves peer learning potential by enabling a much broader and much more rapid scope and scale of review, analysis and user-specific recommendations.

Receive the highest quality assurance in Diagnostic Radiology while dramatically improving both the clinical efficacy of time invested in peer learning (for the benefit of patients) and the clinical efficacy of the peer learning process (for the benefit of physicians).

Applicable across multiple medical disciplines (e.g., pathology, cardiology, ophthalmology, etc.).

As an independent, HIS/RIS/PACS neutral solution, RealTime Medical’s AICloudQA provides users with ongoing HIS/RIS/PACS best of breed flexibility and opportunities going forward while leveraging your existing infrastructure.