Crossley SA, Balyan R, Liu J, Karter AJ, McNamara D, Schillinger D. Predicting the readability of physicians' secure messages to improve health communication using novel linguistic features: Findings from the ECLIPPSE study.
ACTA ACUST UNITED AC 2020;
13:1-13. [PMID:
34306181 DOI:
10.1080/17538068.2020.1822726]
[Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
Background
Low literacy skills impact important aspects of communication, including health-related information exchanges. Unsuccessful communication on the part of physician or patient contributes to lower quality of care, is associated with poorer chronic disease control, jeopardizes patient safety and can lead to unfavorable healthcare utilization patterns. To date, very little research has focused on digital communication between physicians and patients, such as secure messages sent via electronic patient portals.
Method
The purpose of the current study is to develop an automated readability formula to better understand what elements of physicians' digital messages make them more or less difficult to understand. The formula is developed using advanced natural language processing (NLP) to predict human ratings of physician text difficulty.
Results
The results indicate that NLP indices that capture a diverse set of linguistic features predict the difficulty of physician messages better than classic readability tools such as Flesch Kincaid Grade Level. Our results also provide information about the textual features that best explain text readability.
Conclusion
Implications for how the readability formula could provide feedback to physicians to improve digital health communication by promoting linguistic concordance between physician and patient are discussed.
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