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

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...

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Main Authors: Dean Schillinger, Danielle McNamara, Scott Crossley, Courtney Lyles, Howard H. Moffet, Urmimala Sarkar, Nicholas Duran, Jill Allen, Jennifer Liu, Danielle Oryn, Neda Ratanawongsa, Andrew J. Karter
Format: Article
Language:English
Published: Wiley 2017-01-01
Series:Journal of Diabetes Research
Online Access:http://dx.doi.org/10.1155/2017/1348242
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author Dean Schillinger
Danielle McNamara
Scott Crossley
Courtney Lyles
Howard H. Moffet
Urmimala Sarkar
Nicholas Duran
Jill Allen
Jennifer Liu
Danielle Oryn
Neda Ratanawongsa
Andrew J. Karter
author_facet Dean Schillinger
Danielle McNamara
Scott Crossley
Courtney Lyles
Howard H. Moffet
Urmimala Sarkar
Nicholas Duran
Jill Allen
Jennifer Liu
Danielle Oryn
Neda Ratanawongsa
Andrew J. Karter
author_sort Dean Schillinger
collection DOAJ
description 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.
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spelling doaj-art-482237b63f9c45d18fc55cc9935a39bf2025-08-20T03:36:07ZengWileyJournal of Diabetes Research2314-67452314-67532017-01-01201710.1155/2017/13482421348242The Next Frontier in Communication and the ECLIPPSE Study: Bridging the Linguistic Divide in Secure MessagingDean Schillinger0Danielle McNamara1Scott Crossley2Courtney Lyles3Howard H. Moffet4Urmimala Sarkar5Nicholas Duran6Jill Allen7Jennifer Liu8Danielle Oryn9Neda Ratanawongsa10Andrew J. Karter11University of California, San Francisco, CA, USAArizona State University, Tempe, AZ, USAGeorgia State University, Atlanta, GA, USAUniversity of California, San Francisco, CA, USAKaiser Permanente, Oakland, CA, USAUniversity of California, San Francisco, CA, USAArizona State University, Tempe, AZ, USAKaiser Permanente, Oakland, CA, USAKaiser Permanente, Oakland, CA, USARedwood Community Health Coalition, Petaluma, CA, USAUniversity of California, San Francisco, CA, USAKaiser Permanente, Oakland, CA, USAHealth 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.http://dx.doi.org/10.1155/2017/1348242
spellingShingle Dean Schillinger
Danielle McNamara
Scott Crossley
Courtney Lyles
Howard H. Moffet
Urmimala Sarkar
Nicholas Duran
Jill Allen
Jennifer Liu
Danielle Oryn
Neda Ratanawongsa
Andrew J. Karter
The Next Frontier in Communication and the ECLIPPSE Study: Bridging the Linguistic Divide in Secure Messaging
Journal of Diabetes Research
title The Next Frontier in Communication and the ECLIPPSE Study: Bridging the Linguistic Divide in Secure Messaging
title_full The Next Frontier in Communication and the ECLIPPSE Study: Bridging the Linguistic Divide in Secure Messaging
title_fullStr The Next Frontier in Communication and the ECLIPPSE Study: Bridging the Linguistic Divide in Secure Messaging
title_full_unstemmed The Next Frontier in Communication and the ECLIPPSE Study: Bridging the Linguistic Divide in Secure Messaging
title_short The Next Frontier in Communication and the ECLIPPSE Study: Bridging the Linguistic Divide in Secure Messaging
title_sort next frontier in communication and the eclippse study bridging the linguistic divide in secure messaging
url http://dx.doi.org/10.1155/2017/1348242
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