Knowledge Library Vendor Management in the Era of Big Data and Machine Learning
Webinar
Thursday, April 18, 2019

Vendor Management in the Era of Big Data and Machine Learning

The digitization of patient records and push for interoperability has enabled unprecedented data sharing. The ability for researchers and care providers to analyze broad batches of data has improved care delivery and treatment. Covered entities are now turning to vendors with expertise in areas like billing, population health, readmission modelling, etc. to analyze their data sets. While there are many benefits from utilizing third-party vendors, there are additional risks that covered entities must understand when outsourcing data in order to have the proper policies and procedures outlined in their Business Associates’ Agreement.

Learning Objectives:
  • Understand the risks that must be managed when outsourcing data to third-partyvendors - data mixing, machine learning model mixing, and data repurposing - and how to mitigate those risks.
  • Learn how to formulate Business Associate Agreements in the era of big data andmachine learning.
  • Examine different legal and technological processes to ensure vendors compliance withyour BAA, even if your vendor application is managed in the cloud.

To view the webinar replay, please click the link below and complete the information. You will receive an email with a link to the webinar.

 

Watch Now

FEATURED SPEAKER

Dr. Daniel Fabbri
Assistant Professor, Biomedical Informatics & Computer Science, Vanderbilt University.
Founder & CEO, Maize Analytics, Inc.

Daniel Fabbri, Ph.D., is the Founder and CEO of Maize Analytics, as well as an Assistant Professor of Biomedical Informatics and Computer Science at Vanderbilt University. His research focuses on machine learning applied to electronic medical records, clinical data and data privacy. Dr. Fabbri’s research has been sponsored by the National Science Foundation, National Institutes of Health and U.S. Department of Defense. His research on machine learning in healthcare and data privacy has been published in JAMA Internal Medicine, the Journal of the American Medical Informatics Association, Journal of Pediatrics, International Journal of Medical Informatics, and multiple other computer science proceedings.