Racial Disparities in Patient-Provider Communication During Telehealth Visits Versus Face-to-face Visits Among Asian and Native Hawaiian and Other Pacific Islander Patients With Cancer: Cross-sectional Analysis
Bibliographic record
Abstract
BACKGROUND: Telehealth visits increase patients' access to care and are often rated as "just as good" as face-to-face visits by oncology patients. Telehealth visits have become increasingly more common in the care of patients with cancer since the advent of the COVID-19 pandemic. Asians and Pacific Islanders are two of the fastest growing racial groups in the United States, but there are few studies assessing patient satisfaction with telemedicine among these two racial groups. OBJECTIVE: Our objective was to compare satisfaction with communication during telehealth visits versus face-to-face visits among oncology patients, with a specific focus on Asian patients and Native Hawaiian and other Pacific Islander (NHOPI) patients. METHODS: We surveyed a racially diverse group of patients who were treated at community cancer centers in Hawaii and had recently experienced a face-to-face visit or telehealth visit. Questions for assessing satisfaction with patient-physician communication were adapted from a previously published study of cancer survivors. Variables that impact communication, including age, sex, household income, education level, and cancer type and stage, were captured. Multivariable logistic models for patient satisfaction were created, with adjustments for sociodemographic factors. RESULTS: Participants who attended a face-to-face visit reported higher levels of satisfaction in all communication measures than those reported by participants who underwent a telehealth encounter. The univariate analysis revealed lower levels of satisfaction during telehealth visits among Asian participants and NHOPI participants compared to those among White participants for all measures of communication (eg, when asked to what degree "[y]our physician listened carefully to you"). Asian patients and NHOPI patients were significantly less likely than White patients to strongly agree with the statement (P<.004 and P<.007, respectively). Racial differences in satisfaction with communication persisted in the multivariate analysis even after adjusting for sociodemographic factors. There were no significant racial differences in communication during face-to-face visits. CONCLUSIONS: Asian patients and NHOPI patients were significantly less content with patient-physician communication during telehealth visits when compared to White patients. This difference among racial groups was not seen in face-to-face visits. The observation that telehealth increases racial disparities in health care satisfaction should prompt further exploration.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".