Katherine Kim: What Counts as Clinical Data? Incorporating Self-Collected Observations into CVD Research
“For the top 10% of people likely to be readmitted to the hospital, the best predictor of actual readmission was self-reported data.”
Kathy Kim is the director of Health Innovation Research in the Center for Health and Technology. She’s been involved in many different kinds of community based research; here, she talks about her study showing that self-reported data can play a key role in predicting hospital readmission. Looking ahead, she suggests a set of widely diverse data types we ought to be thinking about when looking for signals about who may need more help after surgery, such as neighborhood pollution, social networks and fluid balance.
Highlights from the QS Symposium 2018
Introduction to the Quantified Self Symposium 2018
Reza Mirza: The History and Future of Single-Subject Science
Hugo Campos: 10 Years With An Implantable Cardiac Device, Still No Data Access
Jana Beck: Carb Intake and 60 Lipid Measurements
Azure Grant: Lessons from Blood Testers, a Participant-Led Project
Dorothy D. Sears: Circadian Rhythms and Cardiometabolic Health
Carsten Skarke: Characterizing the Chronobiome with “Supertrackers”
Whitney E. Boesel: Cholesterol Variability Across Postpartum Menstrual Cycles
Xiao Li: Finding the Signal in Rich Self-Collected Data
Jeffrey Olgin: Data Aggregation for N-of-1 to “N-of-Many-Ones”
Dana Lewis: Social Infrastructure for Participant-Led Research
Camille Nebeker: Informed Consent, Self-Consent
Steven Steinhubl: Where “All of Us” Meets All of Us
Sunita Vohra: What N-of-1 Can Do
