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Journal of Diabetes Research
Volume 2017, Article ID 1348242, 9 pages
Review Article

The Next Frontier in Communication and the ECLIPPSE Study: Bridging the Linguistic Divide in Secure Messaging

1University of California, San Francisco, CA, USA
2Arizona State University, Tempe, AZ, USA
3Georgia State University, Atlanta, GA, USA
4Kaiser Permanente, Oakland, CA, USA
5Redwood Community Health Coalition, Petaluma, CA, USA

Correspondence should be addressed to Dean Schillinger; ude.fscu@regnillihcs.naed

Received 4 August 2016; Accepted 12 December 2016; Published 7 February 2017

Academic Editor: Raffaele Marfella

Copyright © 2017 Dean Schillinger et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Health systems are heavily promoting patient portals. However, limited health literacy (HL) can restrict online communication via secure messaging (SM) because patients’ literacy skills must be sufficient to convey and comprehend content while clinicians must encourage and elicit communication from patients and match patients’ literacy level. This paper describes the Employing Computational Linguistics to Improve Patient-Provider Secure Email (ECLIPPSE) study, an interdisciplinary effort bringing together scientists in communication, computational linguistics, and health services to employ computational linguistic methods to (1) create a novel Linguistic Complexity Profile (LCP) to characterize communications of patients and clinicians and demonstrate its validity and (2) examine whether providers accommodate communication needs of patients with limited HL by tailoring their SM responses. We will study >5 million SMs generated by >150,000 ethnically diverse type 2 diabetes patients and >9000 clinicians from two settings: an integrated delivery system and a public (safety net) system. Finally, we will then create an LCP-based automated aid that delivers real-time feedback to clinicians to reduce the linguistic complexity of their SMs. This research will support health systems’ journeys to become health literate healthcare organizations and reduce HL-related disparities in diabetes care.