The Promise and Pitfalls of Electronic Health Data features Julian Wolfson, assistant professor in biostatistics at the U of MN School of Public Health
Most of our health information is now recorded and archived digitally, providing a valuable source of data for researchers seeking to explore the complex relationships between genetics, demographics, behavior, and disease. However, electronic health databases are often extremely messy due to missing, incomplete, or ambiguous information, increasing the risks of drawing faulty conclusions that fail to replicate in future studies.
Julian provides examples of how electronic health data are being used to predict and treat disease; highlights possible sources of bias inherent in these data; and describes how researchers are developing new techniques to address these problems.
To view the WebEx recording of Julian’s talk, click here.