Telemedicine Preparedness Among Older Adults With Chronic Illness: Survey of Primary Care Patients
Bibliographic record
Abstract
BACKGROUND: Older adults are a high priority for telemedicine given their elevated COVID-19 risk and need for frequent provider contact to manage chronic illnesses. It seems that many older adults now use smartphones but few studies have examined their overall readiness for telemedicine. OBJECTIVE: The aim of this study is to survey older primary care patients about their telemedicine preparedness, including internet usage, internet-capable devices, telemedicine experiences and concerns, and perceived barriers. Results were used to inform a telemedicine preparedness training program. METHODS: Community-dwelling older adult patients (aged 65-81 years; N=30) with a chronic health condition that could be managed remotely who were present at a family medicine clinic that primarily serves an urban African American population for a prescheduled in-person appointment were asked to complete a brief survey written for this study. Data were collected February-June 2021 at a large, urban, Midwestern hospital. To minimize patient burden, the survey was limited to 10 questions, focused on the most critical topics. RESULTS: Most participants (21/30, 70%) reported having a device that could be used for telemedicine and using the internet. However, about half had only a single connected device, and messaging and video calling were the most commonly used applications. Few used email and none used online shopping or banking. Only 7 patients had had telemedicine appointments. Telemedicine users were younger than nonusers and used more internet functions than nonusers. Only 2 people reported problems with their telemedicine visits (technology and privacy). Nearly all respondents recognized avoiding travel and COVID-19 exposure as telemedicine benefits. The most common concerns were loss of the doctor-patient connection and inability to be examined. CONCLUSIONS: Most older adults reported having devices that could be used for telemedicine, but their internet use patterns did not confirm the adequacy of their devices or skills for telemedicine. Doctor-patient conversations could be helpful in addressing telemedicine concerns but device and skill gaps must be addressed as well.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".