Jeffrey Rewley, PhD, MS, is an advanced fellow in health services research, working with the Nudge Unit at the University of Pennsylvania and the Center for Health Equity Research and Promotion (CHERP) at the Philadelphia VA Medical center. He completed his doctoral work in the NIH Oxford Cambridge Scholars Program, where he was advised by Dr. Laura Koehly at the Social and Behavioral Research Branch (SBRB) of the National Human Genome Research Institute (NHGRI), Prof. Felix Reed-Tsochas at the Said Business School at Oxford University, and Dr. Chris Marcum, also at the SBRB. His dissertation can be viewed here.
His research is currently multi-streamed, with ongoing effort involved in social network analysis, electronic medical records, behavioral phenotyping, and health equity. In his dissertation he focused on the intersection of the first two, examining how patients co-located in hospital wards (co-presence) impacted health outcomes. His current work at the VA and the University of Pennsylvania extends this work to include randomized clinical trials, where he will be implementing social network interventions to ameliorate some health outcomes affected by one's social network. He is also in the process of incorporating health equity concerns into his research, as the US population is much more heterogeneous than the British population he focused on in his dissertation. This also includes work with Dr. Marco Haenssgen and colleagues examining the effect of social networks and precarity on antibiotic usage in low and middle income countries.
His work on social networks analysis includes both methodological and empirical contributions. Methodologically, he developed an algorithm, extending the triad census to include nodal attributes, or colors. This algorithm reduced the computational time necessary to calculate this by orders of magnitude over the naive approach. The empirical work includes a recent paper in Network Science on the social influence between patients in a chemotherapy ward. Importantly, this work used large observational datasets to answer research questions typically requiring detailed ethnographic or survey research for a fraction of the cost. The empirical work also examines how co-presence networks constructed from electronic medical records can be used to predict nosocomial infection (Chapter 3).
He has recently begun incorporating behavioral phenotyping into his work as a way to identify subsets of patients most at-risk of certain outcomes, or who would benefit most from certain interventions. As part of the former, he used co-presence networks built from electronic medical records along with biomarker data to infer a class of patients with nosocomial infection below the threshold of microbiological tests. These inferred infections are "subclinical infections" (Chapter 4), and may have many adverse effects on both individuals and the disease dynamics of nosocomial infection spread. He is also working on a project analyzing the results of the Lose It trial to examine latent groups in the participants, and determine if they responded differentially to the trial. Future work will include using this type of latent class analysis to optimally allocate participants to different arms of a trial.