Assessing Physician and Patients' Linguistic Concordance
NLP and machine learning applied to 250,000+ secure exchanges between diabetes patients and their physicians, measuring how well doctors adapt to patient health literacy.
Award: NIH R01 LM012355 (Role: Co-I)
The aim in this project is to use natural language processing and machine learning techniques to quantify linguistic concordance and its prevalence among physician-patient dyads and to examine the extent to which it is associated with patients’ self-reported understanding of their physicians’ communication. The dataset involves ~5,000 ethnically diverse patients with diabetes and ~1,100 of their physicians, contributing an unprecedented number of physician-patient secure communication exchanges (>250,000). So far, we have discovered that physicians’ ability to adapt the complexity of their language to better match the health literacy of their patients is an interactive communication competency that can promote shared meaning - a proximal communication outcome in the pathway to both intermediate and distal health outcomes. This work is in collaboration with the University of California, San Francisco and Kaiser Permanente Health System.
Selected papers
- Schillinger, D., Duran, N. D., McNamara, D. S., Crossley, S. A., Balyan, R., & Karter, A. J. (2021). Precision communication: Physicians’ linguistic adaptation to patients’ health literacy. Science Advances, 7, eabj2836. [PDF]
- Schillinger, D., McNamara, D., Crossley, S., Lyles, C., Moffet, H. H., Sarkar, U., Duran, N. D., Allen, J., Liu, J., Oryn, D., Ratanawongsa, N., & Karter, A. J. (2017). The next frontier in communication and the ECLIPPSE study: Bridging the linguistic divide in secure messaging. Journal of Diabetes Research, 2017, 1348242. [PDF]
See the full list on the publications page.