Team-Based Text Analytics for Health Information Systems Learning

Abstract

In healthcare operations, narrative text and comments from questionnaires are common and abundant. Making sense of and coming to some shared meanings around text comments from such questionnaires is often time consuming. A lack of resources and expertise may contribute to hesitation and indecisions when deciding on how or if to analyze text. Because of challenges with analyzing text in operational settings, there can be reluctance to capture rich narrative information. Nonetheless, narrative comments can be a source of rich information that with reliable and faster approaches for analyzing may help with informing operational decisions and human-centered design efforts. In this paper, we describe using text analytics approaches for contributing to thematic analysis of users’ comments to help with health information systems learning.Several text analytic approaches were explored as possible pathways to reduce the burden of reviewing comments about training around health information systems. Approaches included topic modelling, keyword extraction and creating word clouds, word co-occurrence and uniquely co-occurring word visualizations, and text classifiers and nomograms that highlights top linguistic features for the trained classifier. The team walked through example approaches and visualizations and decided on next steps.Visualizations of word co-occurrence and uniquely co-occurring word networks and top linguistic features used to train a naïve-bayes text classifier were used to envision possible categories or codes. Regular expressions were iteratively formulated consisting of some combination of words and stems as codes were formulated and extracts were repeatedly reviewed. Code formulation corresponded with refinement of regular expressions. Individual comments could be multi-labeled and not all comments were coded. Static visuals, text examples, regular expressions, and extract quantities were collected, presented, discussed, and refined with the review team.The purpose of this work was to explore text analytic approaches to assist with response interpretation and to apply filtering techniques for addressing concerns of information overload. Addressing concerns about information overload may reduce hesitation with collecting and examining text. By reframing this as a filtering problem, we began to inquire into ways to review, create codes, and code comments more quickly. Including and fine-tuning text analytics approaches may help teams learn more quickly from questionnaire comments about how users perceive working within health information systems. Finally, lowering thresholds for analyzing text may boost motivations for gathering rich information keeping us from missing out on vital viewpoints and language use across time.