Upcoming Webinar

Join us for a FREE Webinar on Automating Healthcare Document Processing with AI

October 2, 2024 — 11 am PT / 1 pm CT / 2 pm ET

Key Takeaways

  • The challenges of manual document processing in healthcare 
  • The benefits of AI-powered data extraction in terms of time and resources  
  • A step-by-step guide to implementing AI-driven solutions for healthcare document automation 
  • A look into Astera’s semi-structured and unstructured document management solution 

WEBINAR

Automating Healthcare Document Processing with AI-Powered Data Extraction

About the webinar

Healthcare organizations handle massive volumes of data in the form of patient reports, medical records, and other such documents. Manually processing these documents takes time and can lead to inefficiencies, inaccuracies, and compliance risks. AI-powered document processing is changing the game by enabling healthcare providers to streamline operations, improve accuracy, and accelerate data-driven decision-making. 

In this informative webinar, our expert panelists – Mike O Quinn and Abdullah Rafiq, discussed the future of healthcare document automation. They demonstrated how AI-driven data extraction can optimize workflows, reduce manual errors, and increase processing speed. The webinar also includes a live demo of Astera’s semi-structured and unstructured document management solution, designed to streamline document processing. 

Mike A. O’Quinn

Mike A. O’Quinn has been with Astera Software for over 15 years. He currently leads the product development and IT departments as the Chief Technology Officer. 

Abdullah Rafiq

Abdullah Rafiq is a Data Solutions Manager, specializing in data extraction and integration solutions. He has worked with companies like HCA, UFSO, AmTrust, and Advancial, delivering tailored data solutions to address complex challenges. Abullah excels in empowering organizations with user-friendly, AI-powered data solutions, ensuring seamless project execution and optimal results.  

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Authors:

  • kashaf Mehmood