Graduated: March 12, 2021
Ensuring Patient Privacy and Accuracy of Analytical Methods to Support Evidence-Based Healthcare
Over the past two decades, healthcare providers substantially increased their use of electronic health record (EHR) systems. While the early roll outs of these systems have been fraught with complications and the quality of data from these systems is questionable at times, these EHR systems continue to improve. EHRs are primed to become the core of the data driven healthcare system, with the potential to serve as a platform for population health analytics and predictive model development. However, EHRs represent a high risk for exposing patient records and business practices to nefarious actors. Creating infrastructure to deliver predictive methods to clinical records while protecting patient privacy is key to building a reliable healthcare analytics platform. In this dissertation, I focus on three areas with four aims for building a safe and private data analytics platform on the electronic health record. The aims are to: (1) evaluate the University of Washington EHR as a generalizable public health repository, (2) Pilot a Model-to-Data framework as a method to deliver predictive analytic methods to clinical records (3) Scale the Model-to-Data pipeline to host a community challenge, delivering outside models to electronic health records and (4) Develop a patient portal to enable the return of clinically actionable research results.
Last Known Position:
Research Scientist, Sage Bionetworks
Sean Mooney-Chair, Brian Shirts, David Crosslin, Justin Guinney