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

Overview

Journal J Diabetes Res

Publisher Wiley

Specialty Endocrinology

Date 2017 Mar 8

PMID 28265579

Citations 18

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

Affiliations

Soon will be listed here.

Abstract

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.

Citing Articles

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Care Partner Engagement in Secure Messaging Between Patients With Diabetes and Their Clinicians: Cohort Study.

Semere W, Karter A, Lyles C, Reed M, Karliner L, Kaplan C JMIR Diabetes. 2024; 9:e49491.

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A scoping review of empathy recognition in text using natural language processing.

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Automated Strategy Feedback Can Improve the Readability of Physicians' Electronic Communications to Simulated Patients.

Roscoe R, Balyan R, McNamara D, Banawan M, Schillinger D Int J Hum Comput Stud. 2023; 176.

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Identifying Nonpatient Authors of Patient Portal Secure Messages in Oncology: A Proof-of-Concept Demonstration of Natural Language Processing Methods.

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