Needs for Successful Engagement in Telemedicine Among Rural Older US Veterans and Their Caregivers: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Telemedicine is an important option for rural older adults who often must travel far distances to clinics or forgo essential care. In 2014, the Geriatric Research, Education, and Clinical Centers (GRECC) of the US Veterans Health Administration (VA) established a national telemedicine network called GRECC Connect. This network increased access to geriatric specialty care for the 1.4 million rural VA-enrolled veterans aged 65 years or older. The use of telemedicine skyrocketed during the COVID-19 pandemic, which disproportionately impacted older adults, exacerbating disparities in specialty care access as overburdened systems shut down in-person services. This surge presented a unique opportunity to study the supports necessary for those who would forgo telemedicine if in-person care were available. OBJECTIVE: In spring 2021, we interviewed veterans and their informal caregivers to (1) elicit their experiences attempting to prepare for a video visit with a GRECC Connect geriatric specialist and (2) explore facilitators and barriers to successful engagement in a telemedicine visit. METHODS: We conducted a cross-sectional qualitative evaluation with patients and their caregivers who agreed to participate in at least 1 GRECC Connect telemedicine visit in the previous 3 months. A total of 30 participants from 6 geographically diverse GRECC Connect hub sites agreed to participate. Semistructured interviews were conducted through telephone or the VA's videoconference platform for home telemedicine visits (VA Video Connect) per participant preference. We observed challenges and, when needed, provided real-time technical support to facilitate VA Video Connect use for interviews. All interviews were recorded with permission and professionally transcribed. A team of 5 researchers experienced in qualitative research analyzed interview transcripts using rapid qualitative analysis. RESULTS: From 30 participant interviews, we identified the following 4 categories of supports participants described regarding successful engagement in telemedicine, as defined by visit completion, satisfaction, and willingness to engage in telemedicine in the future: (1) caregiver presence to facilitate technology setup and communication; (2) flexibility in visit modality (eg, video from home or a clinic or telephone); (3) technology support (eg, determining device compatibility or providing instruction and on-demand assistance); and (4) assurance of comfort with web-based communication, including orientation to features like closed captioning. Supports were needed at multiple points before the visit, and participants stressed the importance of eliciting the varying needs and preferences of each patient-caregiver dyad. Though many initially agreed to a telemedicine visit because of pandemic-related clinic closures, participants were satisfied with telemedicine and willing to use it for other types of health care visits. CONCLUSIONS: To close gaps in telemedicine use among rural older adults, supports must be tailored to individuals, accounting for technology availability and comfort, as well as availability of and need for caregiver involvement. Comprehensive scaffolding of support starts well before the first telemedicine visit.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".