Can You Read Me Now? Unlocking Narrative Data with Natural Language Processing

Making Use of Natural Language Processing to Better Understand Medical Students' Self-Assessment of Clinical Skills

PROBLEM

Reflective practice is necessary for self-regulated learning. Helping medical students develop these skills can be challenging since they are difficult to observe. One common solution is to assign students' reflective self-assessments, which produce large quantities of narrative assessment data. Reflective self-assessments also provide feedback to faculty regarding students' understanding of content, reflective abilities, and areas for course improvement. To maximize student learning and feedback to faculty, reflective self-assessments must be reviewed and analyzed, activities that are often difficult for faculty due to the time-intensive and cumbersome nature of processing large quantities of narrative assessment data.

APPROACH

The authors collected narrative assessment data (2,224 students' reflective self-assessments) from 344 medical students' reflective self-assessments. In academic years 2019-2020 and 2021-2022, students at the University of Cincinnati College of Medicine responded to 2 prompts (aspects that surprised students, areas for student improvement) after reviewing their standardized patient encounters. These free-text entries were analyzed using TopEx, an open-source natural language processing (NLP) tool, to identify common topics and themes, which faculty then reviewed.

OUTCOMES

TopEx expedited theme identification in students' reflective self-assessments, unveiling 10 themes for prompt 1 such as question organization and history analysis, and 8 for prompt 2, including sensitive histories and exam efficiency. Using TopEx offered a user-friendly, time-saving analysis method without requiring complex NLP implementations. The authors discerned 4 education enhancement implications: aggregating themes for future student reflection, revising self-assessments for common improvement areas, adjusting curriculum to guide students better, and aiding faculty in providing targeted upcoming feedback.

NEXT STEPS

The University of Cincinnati College of Medicine aims to refine and expand the utilization of TopEx for deeper narrative assessment analysis, while other institutions may model or extend this approach to uncover broader educational insights and drive curricular advancements.

Big Data and Data Science in Critical Care

The digitalization of the health-care system has resulted in a deluge of clinical big data and has prompted the rapid growth of data science in medicine. Data science, which is the field of study dedicated to the principled extraction of knowledge from complex data, is particularly relevant in the critical care setting. The availability of large amounts of data in the ICU, the need for better evidence-based care, and the complexity of critical illness makes the use of data science techniques and data-driven research particularly appealing to intensivists. Despite the increasing number of studies and publications in the field, thus far there have been few examples of data science projects that have resulted in successful implementations of data-driven systems in the ICU. However, given the expected growth in the field, intensivists should be familiar with the opportunities and challenges of big data and data science. The present article reviews the definitions, types of algorithms, applications, challenges, and future of big data and data science in critical care.