My research is focused on the meaningful use of Electronic Health Records data with an interest in both deriving inference from EHR data and developing risk prediction models and clinical decision support tools with EHRs. I am interested in understanding the potential and limitations of EHRs for clinical research and adapting methods for the analytic challenges that arise, As such my research sits at the intersection of Biostatistics, Bioinformatics and Epidemiology. I enjoy collaborating with both clinicians and methodologists and involving students into these projects.

Informed Presence

A particular focus is identifying biases that may arise in the use of EHRs. One such bias is what we term informed presence bias. This arises from the observation that people only interact with the health system when they are sick. While this is a missing data problem – we are missing healthy observation – the potential for bias lies in what we observe as opposed to what we miss. One of my foci is identifying situations in which this bias can arise, characterizing the problem it can engender and ultimately developing solutions for addressing it.

Dynamic and Longitudinal Risk Prediction

One of the key ways that EHR data have been used is to develop risk prediction models. EHRs allow us to observe patients repeatedly  over time. We can exploit these repeated measurements to better characterize a patient’s risk profile and/or develop dynamic (time updated) risk models. I have been working on understanding what are the best way to incorporate these repeated measures into risk models. I am also interested in developing effective and robust approaches for dynamic prediction. In one collaboration, we have been working with clinicians at Duke Hospital to evaluate and improve a risk tool for time updated detection of patient deterioration.

Clinical Decision Support Tools for Health Systems

I work closely with members of the Duke University Medical Center to develop and implement clinical decision support tools. We are working on two such tools: 30-day patient readmission and patient visit no-show. The goal is to develop a tool that can be implemented within a real time environment and inform intervention strategies.

Development of Resources to Access and Analyze EHR Data

EHR data represent a unique and valuable data source to understand a person’s clinical status. Unfortunately EHR data are stored in complex and hard to access format. Our group is working to make Duke EHR generally and reproducibly available to clinical researchers. We have stood up the Clinical Research DataMart (CRDM) which allows users to set up regular data pulls for both research purposes or tracking of clinical populations.