Patient and provider perspectives on eHealth interventions in Canada and Australia: a scoping review
Bibliographic record
Abstract
INTRODUCTION: Despite the promises of universal health care in most developed countries, health inequities remain prevalent within and between rural and remote communities. Remote health technologies are often promoted as solutions to increase health system efficiency, to enhance quality of care, and to decrease gaps in access to care for rural and remote communities. However, there is mixed evidence for these interventions, particularly related to how they are received and perceived by health providers and by patients. Health technologies do not always adequately meet the needs of patients or providers. To examine this, a broad-based scoping review was conducted to provide an overview of patient and provider perspectives of eHealth initiatives in rural communities. The unique objective of this review was to prioritize the voices of patients and providers in discussing the disparities between health interventions and needs of people in rural communities. eHealth initiatives were reviewed for rural communities of Australia and Canada, two countries that have similar geographies and comparable health systems at the local level. METHODS: Searches were performed in PubMed, Scopus, and Web of Science with results limited from 2000 to 2018. Keywords included combinations of 'eHealth', 'telehealth', 'telemedicine', 'electronic health', and 'rural/remote'. Individual patient and provider perspectives on health care were identified, followed by qualitative thematic coding based on the type of intervention, the feedback provided, the affected population, geographic location, and category of individual providing their perspective. Quotes from patients and providers are used to illustrate the identified benefits and disadvantages of eHealth technologies. RESULTS: Based on reviewed literature, 90.1% of articles reported that eHealth interventions were largely positive. Articles noted decreased travel time (18%), time/cost saving (15.1%), and increased access to services (13.9%) as primary benefits to eHealth. The most prevalent disadvantages of eHealth were technological issues (24.5%), lack of face-to-face contact (18.6%), limited training (10.8%), and resource disparities (10.8%). These results show where existing eHealth interventions could improve and can inform policymakers and providers in designing new interventions. Importantly, benefits to eHealth extend beyond geographic access. Patients reported ancillary benefits to eHealth that include reduced anxiety, disruption on family life, and improved recovery time. Providers reported closer connections to colleagues, improved support for complex care, and greater eLearning opportunity. Barriers to eHealth are recognized by patient and providers alike to be largely systemic, where lack of rural high-speed internet and unreliability of installed technologies were significant. CONCLUSION: Regional and national governments are seen as the key players in addressing these technical barriers. This scoping review diverges from many reviews of eHealth with the use of first-person perspectives. It is hoped that this focus will highlight the importance of patient voices in evaluating important healthcare interventions such as eHealth and associated technologies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.017 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".