Precision Health and a Learning Health system: Culture, Experimental Principles, and Data Science 1.0 CNE


Nurses, even those with graduate training in research methodology have not been introduced to a formal structure of thought for a learning health system, which, if implemented in their work ecosystem, they would be a critical component thereof.  In addition, even if they have had statistical training, most classical training would not discuss modern machine learning methods, algorithms, or applications, and, if they would, would almost certainly not link such back to the principles of a learning health system. The following presentation aims to narrow this practice gap by educating on the ideas driving a learning health system and by imparting a non-technical understanding of the under-the-hood mechanics of machine learning algorithms (so that they are not just a mysterious blackbox).
 
 
Accreditation Statement: University of Texas at Austin School of Nursing is accredited as a provider of nursing continuing professional development by the American Nurses Credentialing Center's Commission on Accreditation.
 
Requirements for Successful Completion: To receive contact hours for this continuing education activity, the participant must complete the entire online module and complete and submit the evaluation form. Once the evaluation form has been submitted, a “Certificate of Successful Completion” will be awarded for 1.0 contact hours and will be available in your Learning Express account under "View/Print CE Credit".

Learning outcome: Registered nurses will report desire to change practice related to knowledge increase regarding empowering patients to use health technology to address chronic disease self-management. Focus will be on challenges specific to the aging population, youth, and underserved populations, exploring digital health research possibilities and limitations within the framework of ethical and legal boundaries.
 
The activity’s Nurse Planner has determined that no one who has the ability to control the content of this CNE activity – planning committee members and presenters/authors/content reviewers – has a conflict of interest.   
 
This activity expires May 1, 2024
 
Click on the bar below to access the video content for this course. The sharing of links or content is strictly prohibited. 
 

Fee

$20.00

CE Hours

1.00

CE Units

0.100

Activity Type

  • Knowledge

Target Audience(s)

  • Registered Nurses
  • Researchers

 

 

Nurses, even those with graduate training in research methodology have not been introduced to a formal structure of thought for a learning health system, which, if implemented in their work ecosystem, they would be a critical component thereof.  In addition, even if they have had statistical training, most classical training would not discuss modern machine learning methods, algorithms, or applications, and, if they would, would almost certainly not link such back to the principles of a learning health system. The following presentation aims to narrow this practice gap by educating on the ideas driving a learning health system and by imparting a non-technical understanding of the under-the-hood mechanics of machine learning algorithms (so that they are not just a mysterious blackbox).

Speaker(s)/Author(s)

Paul Rathouz picture

Paul Rathouz, PhD
Professor, UT Austin


Brief Bio : Paul Rathouz's, PhD, collaborative research includes work in the field of developmental psychopathology, as well as on a variety of epidemiological studies, and in the area of health services research (including cluster-randomized trials). Dr. Rathouz's two current foci are: health services and outcomes research in surgery (in particular trials of interventions to improve provider-patient communications); and population-based studies in developmental disabilities, including a longitudinal study of speech and language development in children with cerebral palsy.

Release Date: May 1, 2022
Credit Expiration Date: May 1, 2024

CE Hours

1.00

Fee

$20.00