Predictive Analytics

Predictive Analytics

Identification: INFN1404
Volume: 29
Issue: 3
Credits (Post Test and/or Evaluation Required)
Available until 09/30/2016
  • 1.10 - CH
  • 0.00 - Rx


Contact hours available until 9/30/2016.
Requirements for Successful Completion:
Complete the learning activity in its entirety and complete the online CNE evaluation.
Faculty, Planners and Authors Conflict of Interest Disclosure:
The authors, editor, planning committee, and education director reported no actual or potential conflict of interest in relation to this continuing nursing education article.
Commercial Support and Sponsorship:
No commercial support or sponsorship declared.
Accreditation Statement:
This educational activity has been provided by Anthony J. Jannetti, Inc. Anthony J. Jannetti, Inc. is accredited as a provider of continuing nursing education by the American Nurses Credentialing Center’s Commission on Accreditation.
Anthony J. Jannetti, Inc. is a provider approved by the California Board of Registered Nursing, provider number CEP 5387. Licensees in the state of CA must retain this certificate for four years after the CNE activity is completed.
This article was reviewed and formatted for contact hour credit by Rosemarie Marmion, MSN, RN-BC, NE-BC, Director of Education Services for Anthony J. Jannetti, Inc.

The purpose of this continuing nursing education article is to increase nurses’ and other health care professionals’ awareness of the power predictive analytic tools have in shaping how data can be utilized for better patient care and lay a foundation for more evidence based predictive analytic tools for the future. After studying the information presented in this article, you will be able to:
  1. Identify two evidence-based predictive analytic tools being used within electronic health records.
  2. Explain the importance of trending scores to utilize patterns as aids to clinical judgment.
  3. Describe how leadership, policies, practice, and stakeholders must align for predictive analytic tools to be most effective and supportive of best practice.
  4. Explain why it is important to have an evidence-based algorithm to guide clinical decisions related to the use of a predictive analytics tool.



Credits: None available.

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