Improve quality of care and mitigate risk with AICloudQA

AI-assisted peer learning and skills development

Interested in taking your organization’s quality of care to the next level?

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The Future of

Peer Learning

AICloudQA™ is the most advanced diagnostic peer learning, skills development and error avoidance system on the market today. The system combines AI-assisted peer learning with multi-dimensional inputs to increase diagnostic accuracy and improve performance.

Benefits to AICloudQA

The first platform 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.

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.

Dramatically improves 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.

What Customers are Saying?

Slide RealTime Medical’s AICloudQA is the first system I launch in the morning. It provides me with a personalized snapshot on how I’m doing. Individual peers have no idea who's report they are reviewing. It effectively creates an anonymized self-test and an opportunity for that “you’ve still got it” feeling. Dr. Karen Finlay, Chief of Radiology
Hamilton Health Sciences
Slide The system is the only one that I know of, that does all that it does. Offering a multi-pronged approach to peer learning and skills development, with both active program participation as well as formal education and teaching rounds. Dr. Karen Finlay, Chief of Radiology
Hamilton Health Sciences
Slide The solution allows us to get prospective
and timely feedback.
David Wormald MRT(R)(MR), BA, MBA
Assistant VP at HHS and SJHH
Slide This system offers so many advantages as compared with traditional methods of Peer Review. It’s a new era of peer learning for radiologists. Dr. Karen Finlay, Chief of Radiology
Hamilton Health Sciences
Slide With AICloudQA prospective peer review, we are able to change reports prior to their release, which is a huge advantage for patient care and risk mitigation. Dr. Karen Finlay, Chief of Radiology
Hamilton Health Sciences
Slide Additional benefits include Increased organizational creditability; insight based process improvement; and improved standardization of care. Dr. Karen Finlay, Chief of Radiology
Hamilton Health Sciences
Slide The way a peer reports a case, creates interesting insights and enriches our peer rounds discussions around how best to apply various guidelines and new literature. Dr. Karen Finlay, Chief of Radiology
Hamilton Health Sciences
Slide We’re getting an unprecedented level of participation in our monthly quality rounds. The AICloudQA system is a tangible means of further modelling a culture of quality and mentoring for residents, which is very beneficial to us as a teaching institution. Dr. Karen Finlay, Chief of Radiology
Hamilton Health Sciences
Slide Our team is seeing value in the RealTime Medical platform, particularly with the shared learning that's coming out in our rounds with the ability to share different guidelines and literature and being able to marry them up to cases. Dr. Karen Finlay, Chief of Radiology
Hamilton Health Sciences
Slide We wanted the easiest integration process and it ended up that Real Time Medical was the best one to deploy. Dr. Karen Finlay, Chief of Radiology
Hamilton Health Sciences

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