Andy Gao

Purpose: Visual analytics is a nascent and underexplored area in the medical education literature that can assist in synthesizing large volumes of data and information to provide actionable insight. This scoping review aims to identify: 1) the characteristics of the studies that have applied visual analytics in medical education, 2) how visual analytics are being used to support medical education efforts. Method: This study followed the Joanna Briggs Institute (JBI) scoping review guidelines to search four databases in mid-April 2026: PubMed, IEEE Xplore, Scopus, ACM Digital, along with Google Scholar. Key search terms included “medical education” and “visual analytics”; papers that did not implement visual analytics systems in the medical education context were excluded. Variables of the eligible papers were extracted and further analyzed in terms of study characteristics (publication year, study location, primary focus & level of trainees assessed) and application of visual analytics (type of visualization, framework guidance, hardware/software, evaluation technique). Results: Of the 39 eligible papers from 584 screened, 20 (51.3%) were published in the United States. Using visual analytics for learner competency assessment and quality improvement of training programs were the primary focuses. For trainees, nearly all papers focused on assessing residents and medical students. More than 28 (71.8%) studies utilized an interactive tool/dashboard and/or applied an evaluation technique. Only 14 (35.9%) involved a framework-guided design of the visualization. Conclusions: This review highlights that while visual analytics is maturing in medical education, systemic application requires adopting framework-guided visualization design, expertise-matched tools, and goal-oriented evaluation. Future research should prioritize interoperable frameworks for knowledge sharing, controlled field studies assessing impacts on learner competencies, and patient outcomes, and "cross-continuum" dashboards spanning pre-medical through faculty levels. These strategies allow educators and learners to make sense of complex educational data through visual analytics dashboards, ultimately enhancing assessment and patient care quality.

Background: Poor sleep hygiene persists in college students today, despite its heavy implications on adolescent development and academic performance. Although sleep patterns in undergraduates have been broadly investigated, no study has exclusively assessed the sleep patterns of premedical undergraduate students. A gap also exists in the knowledge of how students perceive their sleep patterns compared to their actual sleep patterns. Objective: This study aims to address 2 research questions: What are the sleep patterns of premedical undergraduate students? Would the proposed study protocol be feasible to examine the perception of sleep quality and promote sleep behavioral changes in premedical undergraduate students? Methods: An anonymous survey was conducted with premedical students in the Medical Science Baccalaureate program at an R1: doctoral university in the Midwest United States to investigate their sleep habits and understand their demographics. The survey consisted of both Pittsburg Sleep Quality Index (PSQI) questionnaire items (1-9) and participant demographic questions. To examine the proposed protocol feasibility, we recruited 5 students from the survey pool for addressing the perception of sleep quality and changes. These participants followed a 2-week protocol wearing Fitbit Inspire 2 watches and underwent preassessments, midassessments, and postassessments. Participants completed daily reflections and semistructured interviews along with PSQI questionnaires during assessments. Results: According to 103 survey responses, premedical students slept an average of 7.1 hours per night. Only a quarter (26/103) of the participants experienced good sleep quality (PSQI<5), although there was no significant difference (P=.11) in the proportions of good (PSQI<5) versus poor sleepers (PSQI≥5) across cohorts. When students perceived no problem at all in their sleep quality, 50% (14/28) of them actually had poor sleep quality. Among the larger proportion of students who perceived sleep quality as only a slight problem, 26% (11/43) of them presented poor sleep quality. High stress levels were associated with poor sleep quality. This study reveals Fitbit as a beneficial tool in raising sleep awareness. Participants highlighted Fitbit elements that aid in comprehension such as being able to visualize their sleep stage breakdown and receive an overview of their sleep pattern by simply looking at their Fitbit sleep scores. In terms of protocol evaluation, participants believed that assessments were conducted within the expected duration, and they did not have a strong opinion about the frequency of survey administration. However, Fitbit was found to provide notable variation daily, leading to missing data. Moreover, the Fitbit app’s feature description was vague and could lead to confusion. Conclusions: Poor sleep quality experienced by unaware premedical students points to a need for raising sleep awareness and developing effective interventions. Future work should refine our study protocol based on lessons learned and health behavior theories and use Fitbit as an informatics solution to promote healthy sleep behaviors.

Interactive data visualization can be a viable way to discover patterns in patient-generated health data and enable health behavior changes. However, very few studies have investigated the design and usability of such data visualization. The present study aimed to (1) explore user experiences with sleep data visualizations in the Fitbit app, and (2) focus on end users’ perspectives to identify areas of improvement and potential solutions. The study recruited eighteen pre-medicine college students, who wore Fitbit watches for a two-week sleep data collection period and participated in an exit semi-structured interview to share their experience. A focus group was conducted subsequently to ideate potential solutions. The qualitative analysis identified six pain points (PPs) from the interview data using affinity mapping. Four design solutions were proposed by the focus group to address these PPs and illustrated by a set of mock-ups. The study findings informed four design considerations: (1) usability, (2) transparency and explainability, (3) understandability and actionability, and (4) individualized benchmarking. Further research is needed to examine the design guidelines and best practices of sleep data visualization, to create well-designed visualizations for the general population that enables health behavior changes.

BACKGROUND Poor sleep hygiene persists in college students today, despite its heavy implications on adolescent development and academic performance. Although sleep patterns in undergraduates have been broadly investigated, no study has exclusively assessed the sleep patterns of premedical undergraduate students. A gap also exists in the knowledge of how students perceive their sleep patterns compared to their actual sleep patterns. OBJECTIVE This study aims to address 2 research questions: What are the sleep patterns of premedical undergraduate students? Would the proposed study protocol be feasible to examine the perception of sleep quality and promote sleep behavioral changes in premedical undergraduate students? METHODS An anonymous survey was conducted with premedical students in the Medical Science Baccalaureate program at an R1: doctoral university in the Midwest United States to investigate their sleep habits and understand their demographics. The survey consisted of both Pittsburg Sleep Quality Index (PSQI) questionnaire items (1-9) and participant demographic questions. To examine the proposed protocol feasibility, we recruited 5 students from the survey pool for addressing the perception of sleep quality and changes. These participants followed a 2-week protocol wearing Fitbit Inspire 2 watches and underwent preassessments, midassessments, and postassessments. Participants completed daily reflections and semistructured interviews along with PSQI questionnaires during assessments. RESULTS According to 103 survey responses, premedical students slept an average of 7.1 hours per night. Only a quarter (26/103) of the participants experienced good sleep quality (PSQI<5), although there was no significant difference (P=.11) in the proportions of good (PSQI<5) versus poor sleepers (PSQI≥5) across cohorts. When students perceived no problem at all in their sleep quality, 50% (14/28) of them actually had poor sleep quality. Among the larger proportion of students who perceived sleep quality as only a slight problem, 26% (11/43) of them presented poor sleep quality. High stress levels were associated with poor sleep quality. This study reveals Fitbit as a beneficial tool in raising sleep awareness. Participants highlighted Fitbit elements that aid in comprehension such as being able to visualize their sleep stage breakdown and receive an overview of their sleep pattern by simply looking at their Fitbit sleep scores. In terms of protocol evaluation, participants believed that assessments were conducted within the expected duration, and they did not have a strong opinion about the frequency of survey administration. However, Fitbit was found to provide notable variation daily, leading to missing data. Moreover, the Fitbit app’s feature description was vague and could lead to confusion. CONCLUSIONS Poor sleep quality experienced by unaware premedical students points to a need for raising sleep awareness and developing effective interventions. Future work should refine our study protocol based on lessons learned and health behavior theories and use Fitbit as an informatics solution to promote healthy sleep behaviors.