The Next Frontier in Communication and the ECLIPPSE Study: Bridging the Linguistic Divide in Secure Messaging.
Dean SchillingerDanielle McNamaraScott CrossleyCourtney Rees LylesHoward H MoffetUrmimala SarkarNicholas DuranJill Y AllenJennifer Y LiuDanielle OrynNeda RatanawongsaAndrew J KarterPublished in: Journal of diabetes research (2017)
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.
Keyphrases
- end stage renal disease
- healthcare
- ejection fraction
- type diabetes
- chronic kidney disease
- newly diagnosed
- prognostic factors
- palliative care
- mental health
- public health
- emergency department
- cardiovascular disease
- primary care
- health information
- risk assessment
- deep learning
- social media
- patient reported
- smoking cessation