Presented by Sarah Lotspeich
Assistant Professor
Department of Statistical Sciences
Wake Forest University

The allostatic load index (ALI) is a composite measure of cumulative stress, which reflects the multiple interrelated physiological regulatory systems that underlie healthy functioning. Data from electronic health records (EHR) present a huge opportunity to operationalize the ALI in the learning health system and identify at-risk patients on a large scale, except they are prone to missingness and errors. Ignoring these data quality issues could lead to biased statistical results and incorrect clinical decisions. Validation of EHR data (e.g., through chart reviews) can provide better-quality data, but realistically, only a subset of patients’ data can be validated and most protocols do not recover missing data. Using a representative sample of 1000 patients from the EHR at an extensive learning health system (100 of whom could be validated), we propose methods to design, conduct, and analyze statistically efficient and robust studies of the ALI and healthcare utilization. With measurement error methods, we incorporate all available patient information (validated or unvalidated) into statistical models. Using optimal design theory, we examine ways to strategically select the most informative patients for validation. Incorporating clinical expertise, we devise a novel validation protocol to promote the quality and completeness of EHR data. Most recently, we experimented with how this human chart review protocol could be scaled up through the development of a clinically-driven, LLM-enhanced data recovery algorithm based on diagnosis codes.

A seminar tea will be held at 2:45 p.m. in University Office Plaza, Room 240. All are Welcome.