ABSTRACT:

Electronic health records (EHRs) offer great promises in advancing clinical research and transforming learning health systems. However, complex, temporal EHRs are fraught with biases and present daunting analytical challenges that, if not addressed, can exacerbate health inequities. EHRs data, recorded at irregular time intervals with varying frequencies, are multi-modal and multi-scale including structured data such as labs and vitals, codified data such as diagnosis and procedure codes, and unstructured data such as doctor notes and pathology reports. They are typically incomplete and contain various data errors. What’s more, data gaps and errors in EHRs are often unequally distributed across patient groups: People with less access to care, often people of color or with lower socioeconomic status, tend to have more incomplete EHRs. In this talk, I will discuss these challenges and share my research group’s recent work on developing robust statistical and machine learning methods for addressing some of these challenges. Our experience has demonstrated that a trans-disciplinary health data science approach that involves collaboration between statisticians, informaticians, computer scientists, and physician scientists can accelerate innovation in harnessing the full power of EHRs to tackle complex real-world problems and exert meaningful impact in medicine. To this end, I will also discuss some open questions that present opportunities for future research and collaboration.