Date of Award
Doctor of Philosophy (PhD)
Complex Systems and Data Science
Peter S. Dodds
Christopher M. Danforth
The explosive growth of data and computing power of the last decades has had large impacts on a myriad of domains, not in the least on one of society’s most complex systems: healthcare. In this work, a version of the resulting Learning Healthcare System (LHS) is explored and elements of it have been implemented and are in use at the Department of Veterans’ Affairs today. After an overview of what a LHS is and what it could be once executed in its full form, the chapters will describe in detail some of the individual elements and how they address cogs of the LHS’ cyclic system. A data repository and clinical knowledge base to facilitate the LHS, called the Precision Oncology Data Repository (PODR), will be highlighted, as will two applications: one addressing clinical trial enrollment at point of care, and another synchronization data back into the clinics and hospitals at the (on-going) time of the COVID-19 epidemic. Both these applications are heavily utilizing the large Electronic Health Record real-world data to generate actionable knowledge while applying advanced analytics. Lastly, this thesis presents a methodology for re-calibrating and validating sentiment analysis of EHR clinical notes to facilitate a near real-time pulse of the interaction between patients, providers and the hospital, with the goal of delivering new insights and allowing for iterative adaption based on measurable performance.
Number of Pages
Elbers, Danne Charlotte Emily, "Building a Learning Healthcare System: a path to optimizing big health data to inform clinical care decisions" (2022). Graduate College Dissertations and Theses. 1550.