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      symplr Launches symplrAI Evidence Analysis Application for New Operations Platform on AWS

      symplr.com (PRNewsfoto/symplr)

      News provided by

      symplr

      Mar 03, 2025, 08:00 ET

      Share this article

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      AI-powered solution leverages data in the symplr Operations Platform to evaluate new medical devices or technologies

      HOUSTON, March 3, 2025 /PRNewswire/ -- symplr®, a leading provider of enterprise healthcare operations software, announced the launch of symplrAI Evidence Analysis, the first of many AI-powered solutions and integrations debuting on the symplr Operations Platform in 2025. The new solution features a conversational AI chatbot designed to speed clinical research and streamline the medical device and technology decision-making process for health plans. Working with Amazon Web Services (AWS), symplr aims to seamlessly connect and optimize various aspects of hospital operations and will demonstrate its AI-powered capabilities at the 2025 Healthcare Information and Management Systems Society (HIMSS) Global Health Conference & Exhibition in Las Vegas in booth #4348.

      "The volume of clinical and biomedical research is growing exponentially, making it nearly impossible to analyze the efficacy, safety, and cost-effectiveness of every medical device manually," said Tony DiGiorgio, Chief Architect at symplr. "symplrAI Evidence Analysis maximizes efficiency and can reduce time spent reviewing clinical research from days to a matter of minutes by distilling hundreds of pages on different studies into a summary of relevant information, saving healthcare professionals up to 75% of time and helping them be more productive. This is a substantial benefit that gives organizations significant time back to focus on what truly matters—ensuring patients receive the best possible care and appropriate treatment grounded in strong clinical evidence."

      symplrAI Evidence Analysis leverages best-in-class AWS technologies to foster faster and more informed decision-making. Working with AWS experts, the chatbot is built entirely on symplr's Hayes Knowledge Center, which provides health plans with access to the latest clinical evidence.

      symplrAI Evidence Analysis is designed to:

      • Reduce time spent reviewing clinical research without sacrificing scientific rigor
      • Deliver research, grounded in evidence, that is continually updated and prepared by clinicians in alignment with new policies and guidelines
      • Drive cost-savings via faster, more informed decision-making that can help payers decrease wasteful spending and strategically reallocate resources

      symplrAI Evidence Analysis is part of the first-of-its-kind symplr Operations Platform, which unlocks efficiencies and advances how health systems interoperate, connect, and collaborate to overcome critical challenges. The platform unifies disparate systems into a single solution with standardized processes and empowers healthcare organizations to drive exponential value from their systems.

      "It's exciting to see companies like symplr leverage the full breadth of AWS services to help improve healthcare operations," said Robert Page, AWS Sales Director. "Built using a broad range of AI capabilities, including Amazon Bedrock, Amazon Q, and Amazon Sagemaker, and purpose-built health services like AWS HealthLake, symplr's Operations Platform can help accelerate operational efficiency across cross-functional teams."

      To learn more about the symplr Operations Platform and symplr Evidence Analysis, visit our website.

      About symplr
      symplr is a leader in enterprise healthcare operations software and services with a first-of-its-kind operations platform. Trusted in 9 of 10 U.S. hospitals and 400+ U.S. health plans, symplr optimizes operations and maximizes care powered by our cloud-based workforce, quality, provider data management, and spend solutions. Gain efficiencies, reduce complexity and improve outcomes where it matters most. Learn how to stay ahead of change at www.symplr.com.

      SOURCE symplr

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