Predicting the readability of physicians’ secure messages to improve health communication using novel linguistic features: Findings from the ECLIPPSE study

Precision communication: Physicians’ linguistic adaptation to patients’ health literacy

“…We attempted to address fundamental questions related to the potential importance of linguistic tailoring by applying a series of innovative approaches. First, we used advanced computational linguistics, harnessing an unprecedented, large linguistic dataset containing 10 years of written communications to analyze both patients’ and physicians’ SMs, classifying them as reflecting low or high HL or low or high linguistic complexity, respectively, based on recently developed and validated NLP and machine learning algorithms ( 32 , 34 , 47 ). On the basis of these classifications, we characterized each physician-patient dyad as either concordant or discordant.…”

“…Physicians’ communicative complexity scores were generated by a classification model adapted from one previously reported ( 32 ). The basic steps of model development followed the same sequence as the HL measure ( 34 ).…”

“…With the linguistic features as predictors and complexity as the dependent variable, a classifier model was built using a cross-validation technique that allows training and testing on independent data. The final model, named the physician complexity profile, contained 24 linguistic features that correctly classified the SMs within the test with a good degree of accuracy (AUC, 0.75) ( 32 ). In applying this model to the current dyadic dataset, we were able to streamline the number of linguistic features to comprise 14 indices rather than the original 24 (table S1), including text cohesion, lexical sophistication, lexical diversity, and syntax, while retaining equivalent accuracy.…”

“…The current study uses data from a first-of-its-kind set of analyses generated from the ECLIPPSE Study (Employing Computational Linguistics to Improve Patient-Physician Secure email Exchange) ( 11 , 32 , 33 ), harnessing an unprecedented linguistic dataset that contains nearly a quarter million patient-physician SMs exchanged between >5000 dyads via an online patient portal. Leveraging advancements in machine learning and natural language processing (NLP), our interdisciplinary team developed and validated methods for automatically and accurately measuring patients’ HL and physicians’ language complexity from online SMs, classifying them each as high or low ( 32 , 34 ). One of the goals of the ECLIPPSE Study is to measure the appropriateness of physician’s linguistic complexity for a particular patient’s HL and determine whether concordance is associated with patient understanding.…”

The Power in Coming Together

“…4 The openness of information that was shared on the listserver demonstrated an increased level of trust within the group and was similar to reports from others. 6 , 7 In addition to the high level of trust, the shared goals, comparable to those shared by dedicated project teams, focused the CNOs/CNEs and supported open knowledge sharing. 8 …”