Telemedicine in primary care of older adults: a qualitative study
Bibliographic record
Abstract
Abstract Background The COVID-19 pandemic changed the healthcare system, leading to the rapid evolution and implementation of telemedicine (TM). TM has the potential to improve the quality of primary health care and increase accessibility for the population. However, its use may represent challenges for older people, as they may have distinct needs from the general population due to age-related changes in perceptual, motor, and cognitive capacities. We, thus, aimed to identify potential facilitators and barriers to TM use in primary care for older adults and develop recommendations accordingly. Methods We conducted a qualitative study to explore the challenges associated with TM use among older adults and healthcare professionals (HCPs) in primary care practice. Interviews were conducted with 29 older adults, and three focus groups involving HCPs from four McGill family medicine sites were organized. Employing a hybrid codebook thematic analysis, guided by the Consolidated Framework for Implementation Research (CFIR), we identified facilitators and barriers affecting the optimal use of TM by older adults and HCPs. We synthesized the results from semi-structured interviews and focus groups. These findings were then presented during a deliberative dialogue with eight participants, including family physicians, nurses, a social worker, and a government-level TM expert, to validate our results. The purpose was to gather feedback, identify and refine actionable recommendations. Subsequently, we utilized a thematic analysis using the same codebook to synthesize findings from the deliberative dialogue. Results Participants agreed that TM contributed to maintaining the continuity of care and was particularly convenient when there was an existing or established patient-physician relationship or for addressing minor health issues. TM was found to be beneficial for people with limited mobility, reducing their exposure to potentially high-risk environments. However, participants expressed concerns about the lack of visual contact, causing essential details to be overlooked. Additionally, issues related to miscommunication due to language or hearing barriers were identified. HCPs perceived that most older adults did not consider phone consultations a medical act. Participants were open to a hybrid approach, combining in-person consultations and TM, based on their specific health conditions. Building upon these results, we formulated seven key recommendations. Conclusions Both older adults and HCPs consider TM a good alternative for accessing healthcare services. To improve the effective use of TM, it’s crucial to advocate for a hybrid approach that integrates both in-person and virtual methods. This approach should actively encourage and support individuals in becoming familiar with technological tools.
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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 | 0.000 | 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) | 0.267 | 0.001 |
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; both teacher heads agree on what is shown here.
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".