BIG DATA & HEALTHCARE ANALYTICS: A HIMSS EVENT
SAN FRANCISCO, CA - MAY 15-16, 2017
Dr. Stefano M. Bertozzi is dean and professor of health policy and management at the UC Berkeley School of Public Health. Previously, he directed the HIV and tuberculosis programs at the Bill and Melinda Gates Foundation. Dr. Bertozzi worked at the Mexican National Institute of Public Health as director of its Center for Evaluation Research and Surveys. He was the last director of the WHO Global Programme on AIDS and has also held positions with UNAIDS, the World Bank, and the government of the DRC.
He is currently co-chair of the Health Working Group for the UC–Mexico Initiative and co-editor of the Disease Control Priorities (DCP3) volume on HIV/AIDS, Malaria & Tuberculosis. He has served on governance and advisory boards for WHO, UNAIDS, the Global Fund, PEPFAR, the NIH, Duke University, the University of Washington and the AMA. He has advised NGOs, and ministries of health and social welfare in Asia, Africa and Latin America. He is a member of the National Academy of Medicine. He holds a bachelor’s degree in biology and a PhD in health policy and management from the Massachusetts Institute of Technology. He earned his medical degree at UC San Diego, and trained in internal medicine at UC San Francisco.
Modern big data resources detect variability in provider-level performance in near real time. Quantifying, visualizing, and reporting this variability can improve outcomes by identifying the best and worst performers and creating peer-based incentives for improvement. But the impact of simply describing existing variability is inherently limited without a deeper dive to understand its causes.
That’s where, as Stefano Bertozzi, dean of the UC Berkeley School of Public health, and his colleague Maya Patersen will explain, the emerging field of targeted learning analytics comes in.
In a hospital or other healthcare provider organization, targeted learning analytics can identify modifiable sources contributing to variability of performance and efficiency as well as suggest optimal courses of action that change over time as performance and other factors change. This enables a smarter, more data-driven healthcare system that learns, implements, and evaluates in rapid cycles, to continuously improve performance.
Many questions in healthcare are causal in nature – analytic tools should directly seek to answer those questions.
Current machine learning analytics can predict who is at risk, but do not tell what its causes are, nor what to do about it.
Targeted learning analytics fill this gap, and combine machine learning, causal inference and statistical inference.
Smarter healthcare systems can apply these tools to continuously learn from themselves and map the best course of action, assured of the soundness of their decision-making process.