Rumi Chunara, PhD

Rumi Chunara, PhD
Associate Professor
College of Global Public Health and Polytechnic School of Engineering
New York University

Date:Thursday April 13, 2017
Time:3:00 PM - 4:00 PM EDT
Location:Stokes Auditorium, CHOP Main Hospital

Participatory Data and Public Health:New Opportunities and Challenges

Internet and mobile tools enable us to garner data from individuals that have been increasingly seen as opportunities to augment public health. Data mining and machine learning from these data provides opportunity to learn about aspects of our health that we cannot otherwise garner as quickly or in as high resolution. Further these mediums provide opportunities to reach individuals at scale, or on topics that are otherwise not disclosed. Simultaneously the aggregation of these data leads to many novel epidemiological and biostatistical questions; for example how to characterize and account for biases resulting from observational data sources, and what are the relevant spatial and temporal representations of measures from these participatory data sources? Dr. Chunara will discuss some of the work we have been doing in regards to understanding high-frequency representations of health-related behaviors, assess the value of community sourced infectious disease surveillance and intervention information for influenza and dengue, and how to quantitate new measurements of the social environment in HIV risk.

About Rumi Chunara, PhD
Named one of MIT’s 2014 Top 35 Innovators Under 35, Rumi Chunara is an innovative leader, accomplished computer engineer and scientist whose unique approach to medical and public health research - gathering health data through the Internet and mobile technology - is revolutionizing how public health experts collect health information. She is a joint Associate Professor of Computer Science and Engineering and Public Health at NYU's College of Global Public Health and Polytechnic School of Engineering. Dr. Chunara holds a PhD at Harvard-MIT Division of Health, Sciences and Technology, and a Bachelor’s degree in Electrical Engineering (Honors) from Caltech. (source: NYU College of Global Public Health)

Learning Objectives

  • Learn how to characterize and account for biases resulting from observational data sources
  • Describe the relevant spatial and temporal representations of measures from these participatory data sources
  • Understand the statistical challenges for participatory data

Continuing Medical Education

ACCME Accreditation Statement
The Children's Hospital of Philadelphia is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.

AMA Credit Designation Statement
The Children's Hospital of Philadelphia designates this live activity for a maximum 1.0 AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with their participation in the activity.

Disclosure Statement
Dr. Chunara has signed and completed Disclosure of Relevant Financial Relationships Forms and affirmed that neither she nor her spouse/partner/family members /have or had any relevant financial relationships or financial affiliations with commercial interest(s) within the past 12 months related directly or indirectly to this educational activity that may pose a conflict of interest within the context of this lecture.

Maintenance of Certification (MOC)
The American Board of Preventive Medicine (ABPM) has approved this activity for a maximum of one (1) LLSA credit towards ABPM MOC Part II requirements. Board certified physicians in Clinical Informatics who wish to receive MOC credit for this session must review the MOC questions prior to the presentation. Answers will be highlighted here following the session.

1. What is participatory data?

  • a. Data generated as a consensus from the “crowd”
  • b. Data generated directly by individuals.
  • c. Data that many people can alter
  • d. None of the above

2. What is a complementary ways participatory data can augment healthcare-based data?

  • a. Can avoid recall bias
  • b. Can get information from potentially more cases reported through healthcare institutions
  • c. Can be used to get information in real-time
  • d. All of the above

3. What is a statistical challenge for participatory data?

  • a. Multiple instances are required for every single data point./li>
  • b. The data are not validated by a doctor, laboratory, etc.
  • c. The data is often non-specific.
  • d. All of the above

Nurse Attendance
Nurses who attend the live educational activity will need to sign-in on the Nurse Sign-In Form for CME Activities in the Stokes Auditorium and will be eligible to receive a certificate of completion, which may be used for re-licensure and re-certification. Nurses, for further information please contact Sharon Miller, MSN, RN, Education Nurse Specialist.This email address is being protected from spambots. You need JavaScript enabled to view it..