A Virtual Reality–Based mHealth App to Enhance Health Care Skills and Promote Health Equity by Empowering Health Care Professionals to Provide Empathetic, Compassionate, and Unbiased Patient Care Through Digital Experiential Learning: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Health care professionals' educational preparation and practices significantly influence care experiences and health outcomes. Deficient awareness of the impact of stereotypes, biases, prejudices, and social determinants of health (SDH) can lead to negative care experiences, strained health care professional-patient relationships, and health disparities. Addressing these challenges necessitates enhancing health care professionals' skills, including inclusive communication, cultural humility, recognition of SDH, and fostering empathy and compassion, promoting health equity and better care experiences. OBJECTIVE: This research aims to introduce a mobile health (mHealth) app designed using a digital experiential learning (DEL) approach to strengthen health care professionals' competencies, thereby improving patient care experiences and promoting health equity. The key objectives are to deliver essential health care skills, such as cultivating cultural humility, developing inclusive communication proficiencies, understanding the lasting impact of SDH, comprehending how implicit and explicit biases affect health outcomes, fostering compassionate and empathetic clinical attitudes, and promoting continuous professional development. Together, these aims advance patient-centered care and help reduce health disparities. METHODS: The mHealth app integrates virtual reality-based serious role-playing hypothetical scenarios and a life course module to provide health care professionals with immersive first-person learning experiences. The scenarios include a Syrian refugee with limited English proficiency and an African American pregnant woman with a history of opioid use disorder, each lasting ≈30 minutes to deliver essential health care skills. The pre- and postassessment questionnaires are integrated within the app to measure the learning outcomes of diverse professionals who voluntarily engaged with the app. Distinct hypotheses were formulated and evaluated, referring to specific questionnaire items to assess the app's impact on professionals' skills and attitudes. RESULTS: The mHealth app significantly enhanced health care professionals' skills and attitudes, including increased confidence, preparedness for patient interactions, awareness of the lasting impact of SDH, positive beliefs, reduced prejudice, improved perspectives on patient responsibility and external factors, as well as greater compassion and empathy. These outcomes, obtained through evaluating distinct hypotheses and analysis supported by multiple statistical approaches, including CI analysis, Cohen d effect sizes, odds ratios, 1-tailed paired t tests, and descriptive response distributions, directly align with the study's objectives, fostering health equity and patient-centered care. Overall, the app effectively improved health care professionals' competencies, contributing to better care experiences and outcomes while promoting health equity. CONCLUSIONS: This study delivers a comprehensive data-driven evaluation that validates the effectiveness of the mHealth app in enhancing health care professionals' skills and fostering health equity. It addresses challenges in health care education by delivering a scalable, accessible, and immersive learning platform. Supported by virtual reality technology and an experiential learning framework, the mHealth app presents a promising avenue to empower health care professionals with skills to provide patient-centered care in diverse, complex settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".