Disparities in Video and Telephone Visits Among Older Adults During the COVID-19 Pandemic: Cross-Sectional Analysis
Bibliographic record
Abstract
BACKGROUND: Telephone and video telemedicine appointments have been a crucial service delivery method during the COVID-19 pandemic for maintaining access to health care without increasing the risk of exposure. Although studies conducted prior to the pandemic have suggested that telemedicine is an acceptable format for older adults, there is a paucity of data on the practical implementation of telemedicine visits. Due to prior lack of reimbursement for telemedicine visits involving nonrural patients, no studies have compared telephone visits to video visits in geriatric primary care. OBJECTIVE: This study aimed to determine (1) whether video visits had longer durations, more visit diagnoses, and more advance care planning discussions than telephone visits during the rapid implementation of telemedicine in the COVID-19 pandemic, and (2) whether disparities in visit type existed based on patient characteristics. METHODS: We conducted a retrospective, cross-sectional analysis of patients seen at two geriatric clinics from April 23 to May 22, 2020. Approximately 25% of patients who had telephone and video appointments during this time underwent chart review. We analyzed patient characteristics, visit characteristics, duration of visits, number of visit diagnoses, and the presence of advance care planning discussion in clinical documentation. RESULTS: Of the 190 appointments reviewed, 47.4% (n=90) were video visits. Compared to telephone appointments, videoconferencing was, on average, 7 minutes longer (mean 37.3 minutes, SD 10 minutes; P<.001) and had, on average, 1.2 more visit diagnoses (mean 5.7, SD 3; P=.001). Video and telephone visits had similar rates of advance care planning. Furthermore, hearing, vision, and cognitive impairment did not result in different rates of video or telephone appointments. Non-White patients, patients who needed interpreter services, and patients who received Medicaid were less likely to have video visits than White patients, patients who did not need an interpreter, and patients who did not receive Medicaid, respectively (P=.003, P=.01, P<.001, respectively). CONCLUSIONS: Although clinicians spent more time on video visits than telephone visits, more than half of this study's older patients did not use video visits, especially if they were from racial or ethnic minority backgrounds or Medicaid beneficiaries. This potential health care disparity merits greater attention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".