Factors Influencing Telehealth Implementation and Use in Frontier Critical Access Hospitals: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Telehealth has potential to help individuals in rural areas overcome geographical barriers and to improve access to care. The factors that influence the implementation and use of telehealth in critical access hospitals are in need of exploration. OBJECTIVE: The aim of this study is to understand the factors that influenced telehealth uptake and use in a set of frontier critical access hospitals in the United States. METHODS: This work was conducted as part of a larger evaluation of a Centers for Medicare & Medicaid Services-funded demonstration program to expand cost-based reimbursement for services for Medicare beneficiaries for frontier critical access hospitals. Our sample was 8 critical access hospitals in Montana, Nevada, and North Dakota that implemented the telehealth aspect of that demonstration. We reviewed applications and progress reports for the demonstration program and conducted in-person site visits. We used a semistructured discussion guide to facilitate conversations with clinical, administrative, and information technology staff. Using NVivo software (QSR International), we coded the notes from the interviews and then analyzed the themes. RESULTS: Several factors influenced the implementation and use of telehealth in critical access hospitals, including making changes to workflow and infrastructure as well as practitioner acceptance and availability. Participants also cited technical assistance and support for implementation as supportive factors. CONCLUSIONS: Frontier critical access hospitals may adopt telehealth to overcome challenges such as distance from specialty practitioners and workforce challenges. Telehealth can be used for provider-to-patient and provider-to-provider interactions to improve access to care, remove barriers, and improve quality. However, the ability of telehealth to improve outcomes is limited by factors such as workflow and infrastructure changes, practitioner acceptance and availability, and financing.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".