Q-Centrix and Realyze Intelligence to Jointly Advance Clinical Data...
Healthcare Tech Outlook

Q-Centrix and Realyze Intelligence to Jointly Advance Clinical Data Automation and Improve Patient Care

AI and NLP translation technologies developed by Realyze Intelligence and Q-Centrix assist the industry in uncovering the full potential of clinical data by enhancing unstructured data collecting.

FREMONT, CA: Doctors and nurses can reach out to more patients more efficiently using automation, as it eliminates vital but repetitive activities such as paperwork. Q-Centrix®, the leading exclusive provider of clinical data management solutions to hospitals and health systems, has formed a partnership with Realyze Intelligence, a company that leverages artificial intelligence (AI) and natural language processing (NLP) to capture unstructured clinical data, ensuring that patients with heart conditions or cancer receive the most beneficial treatments.

As part of the UPMC Enterprises innovation, commercialization, and venture capital arm, Realyze was created. Q-industry-leading Centrix's Enterprise Clinical Data Management (eCDMTM) platform will include a sophisticated AI and machine learning component thanks to its partnership with Realyze. Oncology, cardiology, surgical, and other clinical specialties can benefit from the eCDM's ability to harvest high-fidelity clinical data from across health care systems and key clinical segments like oncology, cardiology, and surgical.

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Realyzes NLP will operate as a translation layer using data from Q-proprietary Centrix's models to find clinical information in unstructured fields. Finally, a clinical model of the patient will be created to determine meaning, precedent, and clinical consequences. Now, health systems utilizing Q-Centrix technologies will have access to a best-in-class solution for boosting their clinical data accuracy, expediting the curation process, and bringing their industry-leading accuracy even further to the forefront of the field.

"Q-Centrix has been investing in data automation for several years, testing the capabilities with some of the leading partners in the space. As a result of our efforts, we recognized that the optimal solution would include an AI / NLP based software provider, allowing us to build our own proprietary algorithms on top of that base," states Brian Foy, chief product officer at Q-Centrix. "Because of our scale now working with more than 1,200 facilities and the breadth of our data, we are excited to partner with Realyze and lead the industry in this exciting transformation. As the value of clinical data exponentially grows, we will continue to meaningfully enhance our refined, proprietary process to curate high-quality clinical data using innovative technology (automation and software) and our clinical experts."

“A significant amount of rich clinical information remains untapped as unstructured data in physicians’ notes, requiring extraction by clinical experts,” states Aaron Brauser, president and chief executive officer of Realyze. “Our goal is to not only provide a solution that improves both the capability and efficiency of extraction for hospitals and health systems but also to improve patient understanding leading to better care. Overall, this partnership combines the expertise and technology of both companies to streamline how clinical data translates to patient outcomes.”

In the beginning, Q-Centrix plans to use Realyzes technology in cardiology and oncology. The collaboration may expand into additional clinical areas and data applications, which might significantly impact the advancement and personalization of healthcare.

"Automation will play an important role in the capture of clinical data and will complement the essential human touch," continues Foy. "Clinical data experts are an important element in the deciphering and pinpointing of notable information. This partnership makes the largest group of clinical data experts in the world more efficient, and, as the demand for clinical data increases, Realyzes technology helps us scale the capabilities of our experts to improve the quality, reach and use of clinical data."

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