Session ID: 
BG1

Maximizing Value from Big Data: A Federal View

8:45am - 9:45am Monday, March 9
Orlando - Orange County Convention Center
W308A
Additional Registration
Extra Fee

Description

Health and medical progress depends on the ability to share and use many kinds of big data with a multitude of stakeholders. In 2019, the nonprofit Center for Open Data Enterprise (CODE) and the U.S. Department of Health and Human Services (HHS) collaborated on a year-long project to improve data sharing for research, diagnosis, treatment, and healthcare delivery. Leaders from HHS and CODE share the Department's plans for improving the health data ecosystem, and provide insights on challenges and solutions in using big data, such as government health data. Learn how HHS’s data sharing strategies can be applied by businesses, academia, and nonprofits. Engage directly with the speakers on topics including the use of health data for AI applications, strategies for balancing health data privacy with access, and applying social determinants of health.

This session is part of a special program called Big Data Symposium. Extra fees and separate registration is required.
This session is part of a special program called HIMSS20 Big Data Symposium: Making it Work. Extra fees and separate registration is required.

Learning Objectives

  • Identify HHS and other health-related datasets of high value, including some of the best ones for specific applications List key strategies for balancing privacy protection with health data access
  • Illustrate how AI applications can improve diagnosis, treatment, and care delivery
  • Explore how social determinants of health data can be used for both public health and individual patient care

Speaker(s)

501(c)3 non-profit organization,
Center For Open Data Enterprise
Exec Dir for Innovation,
Department of Health and Human Services

Continuing Education Credits

CAHIMS
1.00
CME
1.00
CNE
1.00
CPHIMS
1.00

Audience

CIO/CTO/CTIO/Senior IT
Data Scientist
Population Health Management Professional

Level

Intermediate