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

Modern communication between health care professionals and patients increasingly relies upon secure messages (SMs) exchanged through an electronic patient portal. Despite the convenience of secure messaging, challenges include gaps between physician and patient expertise along with the asynchronous nature of such communication. Importantly, less readable SMs from physicians (e.

Leveraging a multidimensional linguistic analysis of constructed responses produced by college readers.

The goal of this study was to assess the relationships between computational approaches to analyzing constructed responses made during reading and individual differences in the foundational skills of reading in college readers. We also explored if these relationships were consistent across texts and samples collected at different institutions and texts. The study made use of archival data that involved college participants who produced typed constructed responses under thinking aloud instructions reading history and science texts.

Before and during COVID-19: A Cohesion Network Analysis of students' online participation in moodle courses.

The COVID-19 pandemic has changed the entire world, while the impact and usage of online learning environments has greatly increased. This paper presents a new version of the grounded in Cohesion Network Analysis, which can be used to evaluate the online activity of students as a plug-in feature to Moodle. A Recurrent Neural Network with LSTM cells that combines global features, including participation and initiation indices, with a time series analysis on timeframes is used to predict student grades, while multiple sociograms are generated to observe interaction patterns.

Age of Exposure 2.0: Estimating word complexity using iterative models of word embeddings.

Age of acquisition (AoA) is a measure of word complexity which refers to the age at which a word is typically learned. AoA measures have shown strong correlations with reading comprehension, lexical decision times, and writing quality. AoA scores based on both adult and child data have limitations that allow for error in measurement, and increase the cost and effort to produce.

Precision communication: Physicians' linguistic adaptation to patients' health literacy.

Little quantitative research has explored which clinician skills and behaviors facilitate communication. Mutual understanding is especially challenging when patients have limited health literacy (HL). Two strategies hypothesized to improve communication include matching the complexity of language to patients’ HL (“universal tailoring”); or always using simple language (“universal precautions”).

Validity of a Computational Linguistics-Derived Automated Health Literacy Measure Across Race/Ethnicity: Findings from The ECLIPPSE Project.

Limited health literacy (HL) partially mediates health disparities. Measurement constraints, including lack of validity assessment across racial/ethnic groups and administration challenges, have undermined the field and impeded scaling of HL interventions. We employed computational linguistics to develop an automated and novel HL measure, analyzing >300,000 messages sent by >9,000 diabetes patients via a patient portal to create a Literacy Profiles.

Challenges and solutions to employing natural language processing and machine learning to measure patients' health literacy and physician writing complexity: The ECLIPPSE study.

Objective: In the National Library of Medicine funded ECLIPPSE Project (Employing Computational Linguistics to Improve Patient-Provider Secure Emails exchange), we attempted to create novel, valid, and scalable measures of both patients' health literacy (HL) and physicians' linguistic complexity by employing natural language processing (NLP) techniques and machine learning (ML). We applied these techniques to > 400,000 patients' and physicians' secure messages (SMs) exchanged via an electronic patient portal, developing and validating an automated patient literacy profile (LP) and physician complexity profile (CP). Herein, we describe the challenges faced and the solutions implemented during this innovative endeavor.

Descriptive examination of secure messaging in a longitudinal cohort of diabetes patients in the ECLIPPSE study.

The substantial expansion of secure messaging (SM) via the patient portal in the last decade suggests that it is becoming a standard of care, but few have examined SM use longitudinally. We examined SM patterns among a diverse cohort of patients with diabetes (N = 19 921) and the providers they exchanged messages with within a large, integrated health system over 10 years (2006-2015), linking patient demographics to SM use. We found a 10-fold increase in messaging volume.

Employing computational linguistics techniques to identify limited patient health literacy: Findings from the ECLIPPSE study.

Objective: To develop novel, scalable, and valid literacy profiles for identifying limited health literacy patients by harnessing natural language processing.

Data Source: With respect to the linguistic content, we analyzed 283 216 secure messages sent by 6941 diabetes patients to physicians within an integrated system's electronic portal. Sociodemographic, clinical, and utilization data were obtained via questionnaire and electronic health records.

Developing and Testing Automatic Models of Patient Communicative Health Literacy Using Linguistic Features: Findings from the ECLIPPSE study.

Patients with diabetes and limited health literacy (HL) may have suboptimal communication exchange with their health care providers and be at elevated risk of adverse health outcomes. These difficulties are generally attributed to patients' reduced ability to both communicate and understand health-related ideas as well as physicians' lack of skill in identifying those with limited HL. Understanding and identifying patients with barriers posed by lower HL to improve healthcare delivery and outcomes is an important research avenue.

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

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.

Secure Messaging with Physicians by Proxies for Patients with Diabetes: Findings from the ECLIPPSE Study.

Background: Little is known about patients who have caregiver proxies communicate with healthcare providers via portal secure messaging (SM). Since proxy portal use is often informal (e.g.

Using natural language processing and machine learning to classify health literacy from secure messages: The ECLIPPSE study.

Limited health literacy is a barrier to optimal healthcare delivery and outcomes. Current measures requiring patients to self-report limitations are time-consuming and may be considered intrusive by some. This makes widespread classification of patient health literacy challenging.

Cohesion network analysis of CSCL participation.

The broad use of computer-supported collaborative-learning (CSCL) environments (e.g., instant messenger-chats, forums, blogs in online communities, and massive open online courses) calls for automated tools to support tutors in the time-consuming process of analyzing collaborative conversations.

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 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.

Sentiment Analysis and Social Cognition Engine (SEANCE): An automatic tool for sentiment, social cognition, and social-order analysis.

This study introduces the Sentiment Analysis and Cognition Engine (SEANCE), a freely available text analysis tool that is easy to use, works on most operating systems (Windows, Mac, Linux), is housed on a user's hard drive (as compared to being accessed via an Internet interface), allows for batch processing of text files, includes negation and part-of-speech (POS) features, and reports on thousands of lexical categories and 20 component scores related to sentiment, social cognition, and social order. In the study, we validated SEANCE by investigating whether its indices and related component scores can be used to classify positive and negative reviews in two well-known sentiment analysis test corpora. We contrasted the results of SEANCE with those from Linguistic Inquiry and Word Count (LIWC), a similar tool that is popular in sentiment analysis, but is pay-to-use and does not include negation or POS features.

The tool for the automatic analysis of text cohesion (TAACO): Automatic assessment of local, global, and text cohesion.

This study introduces the Tool for the Automatic Analysis of Cohesion (TAACO), a freely available text analysis tool that is easy to use, works on most operating systems (Windows, Mac, and Linux), is housed on a user's hard drive (rather than having an Internet interface), allows for the batch processing of text files, and incorporates over 150 classic and recently developed indices related to text cohesion. The study validates TAACO by investigating how its indices related to local, global, and overall text cohesion can predict expert judgments of text coherence and essay quality. The findings of this study provide predictive validation of TAACO and support the notion that expert judgments of text coherence and quality are either negatively correlated or not predicted by local and overall text cohesion indices, but are positively predicted by global indices of cohesion.

From Generating in the Lab to Tutoring Systems in Classrooms.

Work in cognitive and educational psychology examines a variety of phenomena related to the learning and retrieval of information. Indeed, Alice Healy, our honoree, and her colleagues have conducted a large body of groundbreaking research on this topic. In this article we discuss how 3 learning principles (the generation effect, deliberate practice and feedback, and antidotes to disengagement) discussed in Healy, Schneider, and Bourne (2012) have influenced the design of 2 intelligent tutoring systems that attempt to incorporate principles of skill and knowledge acquisition.

The nature of mind wandering during reading varies with the cognitive control demands of the reading strategy.

Prior studies of mind wandering find the default network active during mind wandering, but these studies have yielded mixed results concerning the role of cognitive control brain regions during mind wandering. Mind wandering often interferes with reading comprehension, and prior neuroimaging studies of discourse comprehension and strategic reading comprehension have shown that there are at least two networks of brain regions that support strategic discourse comprehension: a domain-general control network and a network of regions supporting coherence-building comprehension processes. The present study was designed to further examine the neural correlates of mind wandering by examining mind wandering during strategic reading comprehension.

Comparing comprehension measured by multiple-choice and open-ended questions.

This study compared the nature of text comprehension as measured by multiple-choice format and open-ended format questions. Participants read a short text while explaining preselected sentences. After reading the text, participants answered open-ended and multiple-choice versions of the same questions based on their memory of the text content.

Natural language processing in an intelligent writing strategy tutoring system.

The Writing Pal is an intelligent tutoring system that provides writing strategy training. A large part of its artificial intelligence resides in the natural language processing algorithms to assess essay quality and guide feedback to students. Because writing is often highly nuanced and subjective, the development of these algorithms must consider a broad array of linguistic, rhetorical, and contextual features.

The neural correlates of strategic reading comprehension: cognitive control and discourse comprehension.

Neuroimaging studies of text comprehension conducted thus far have shed little light on the brain mechanisms underlying strategic learning from text. Thus, the present study was designed to answer the question of what brain areas are active during performance of complex reading strategies. Reading comprehension strategies are designed to improve a reader's comprehension of a text.

Computational analyses of multilevel discourse comprehension.

The proposed multilevel framework of discourse comprehension includes the surface code, the textbase, the situation model, the genre and rhetorical structure, and the pragmatic communication level. We describe these five levels when comprehension succeeds and also when there are communication misalignments and comprehension breakdowns. A computer tool has been developed, called Coh-Metrix, that scales discourse (oral or print) on dozens of measures associated with the first four discourse levels.

The bit in the middle and why it's important: a computational analysis of the linguistic features of body paragraphs.

This study examines the relationship between the linguistic characteristics of body paragraphs of student essays and the total number of paragraphs in the essays. Results indicate a significant relationship between the total number of paragraphs and a variety of linguistic characteristics known to affect student essay scores. These linguistic characteristics (e.

Computational methods to extract meaning from text and advance theories of human cognition.

Over the past two decades, researchers have made great advances in the area of computational methods for extracting meaning from text. This research has to a large extent been spurred by the development of latent semantic analysis (LSA), a method for extracting and representing the meaning of words using statistical computations applied to large corpora of text. Since the advent of LSA, researchers have developed and tested alternative statistical methods designed to detect and analyze meaning in text corpora.