Research

My dissertation and book project, Data Values: Moral Entrepreneurship in Digital Health, examines ongoing efforts to build an ethics of AI. Specifically, I study the creation of moral rules that govern the use of digital data for mental healthcare. Researchers increasingly turn to technologies like smartphone apps and wearable devices to produce data for healthcare, but these tools have also provoked heated debates about privacy, data ownership, and algorithmic bias. Formal regulation lags behind, giving researchers wide latitude to establish norms and values of their field. 

To understand this moral self-regulation, I completed three years of ethnographic fieldwork with academic teams developing technology for mental healthcare. I argue researchers’ moral rulemaking is a form of entrepreneurship. Moral entrepreneurship shapes the field of digital health by setting what it focuses on, what it neglects, and whom it serves. As my dissertation analyzes this process, it helps explain how moral ideas are adjudicated amidst uncertainty and it uncovers new mechanisms for health disparities and social inequality in our digital future. 

My dissertation is funded by several competitive national fellowships, including the ASA/NSF Doctoral Dissertation Research Improvement Grant and the Institute for Citizens and Scholars’ Charlotte W. Newcombe Dissertation Grant. One chapter from this project is published in Socius.

A second project theorizes how algorithmic systems like electronic health records (EHRs) yield real-world consequences, using the case of decision-making processes for prostate cancer care. 

For this project, I completed 20 months of ethnographic research and interviews in a urologic cancer clinic. My major paper from this project addresses a dilemma in the social studies of algorithms literature: Algorithmic problems like bias are typically attributed to algorithms’ opacity, yet theorizing opacity as a durable feature cannot explain algorithms’ diverse and inconsistent effects. To address this, my paper outlines a theory of algorithmic affordances—the relational aspects of a technology that frame what can happen through its use—to model how algorithmic effects are mediated by social and organizational conditions. I illustrate my argument with the case of medical decision-making with the EHR, a complex algorithmic system.

This paper has been awarded best paper prizes from the Science, Knowledge, and Technology section and the Economic Sociology section of the American Sociological Association. It is currently under review.

My third project studies the role of information technology in efforts to standardize care for health equity. 

This project analyzes how clinicians use EHRs to document data about social determinants of health (SDOH). Though professional groups and US federal agencies have issued calls to standardize SDOH documentation, little is known about how clinicians currently document and use SDOH in daily practice. To investigate this process, I completed ethnographic observation, interviews, and qualitative coding of EHRs to triangulate how primary care clinicians talk and write about patients’ SDOH. I find that even clinicians who prioritize attention to patients’ SDOH do not typically document them. Instead, the paper identifies three “local standards” clinicians enact to integrate SDOH into their care that may be invisible to or even threatened by formal documentation standards. Taken together, the paper illuminates both challenges and opportunities for the standardization of social data in medical records. 

A paper from this study is published in Social Science & Medicine. It was awarded the Donald W. Light Award from the Medical Sociology section of the American Sociological Association.