Pascal Brandt

The digital transformation of healthcare over the past two decades has led to the proliferation of electronic health record (EHR) databases. These databases present an unprecedented opportunity for biomedical knowledge discovery. Data may be used for several purposes, including epidemiology, operational or clinical quality improvement studies, pragmatic trials and clinical trial recruitment, comparative effectiveness research, predictive modeling, clinical decision support, pharmacovigilance, and genome-wide association studies. In every case, one of the first steps involved is identifying the appropriate cohort of patients matching a set of inclusion and exclusion criteria, using only data available in the EHR. This process, known as EHR-driven phenotyping, is a resource-intensive task that involves many stakeholders, such as clinical experts, informaticists, and database analysts. It is therefore a critical rate-limiting factor that prevents massive scaling of knowledge discovery, and ultimately inhibits our ability to achieve the promise of national imperatives such as the Learning Healthcare System and All of Us. This research will attempt to improve the state of the art of EHR-driven phenotyping in three specific ways. First, we will analyze the variability of a set of existing, clinically validated, phenotype definitions in order to understand the requirements for a formal representation that supports automation. Second, we will assess the suitability of popular and emerging standards for formally representing cohort criteria, and evaluate whether this representation facilitates cross-platform cohort identification. Finally, we will develop and evaluate a fully standards-based system that can be used to create phenotype definitions and execute them against existing EHR data platforms, and evaluate the performance of this system in the context of the extant EHR-driven phenotyping ecosystem.