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According to a recent HIMSS survey, 86% of healthcare leaders state that reducing hospital readmission rates was their top analytics priority. Implementing the right analytics strategy and tools to help identify at-risk patients can give providers an extra indication of when and where to focus resources to prevent speedy returns to the hospital and is critical to their success and bottom line.

Watch this webinar as we examine how predictive analytics can be used to help departments across health organizations from clinical, finance, and revenue cycle management determine at-risk patients to help reduce readmission rates.

Learn how self-service analytics helps:

  • Access and integrate data from across your organization
  • Incorporate advanced analytics to impact patient risks
  • Improve analytic accuracy and consistency through a repeatable process


  • Matt-Madden
    Matthew Madden

    Director of Solutions Marketing

  • John-Heisler
    John HeisleR

    VP Business Intelligence and Data Science
    Continuus Technologies

"With the right self-service tools, an organization can create the blueprint of what it wants to do. It can bring in this data and create a set of validation rules that can be easily confirmed by other areas of the business.” 
— “Democratizing Data for Healthcare Success,” Denis Dudzinski, North America Healthcare Sales Director, Alteryx

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