Health Care Providers’ Experiences and Perceptions With Telehealth Tools in a Hospital-at-Home Program: Mixed Methods Study
Bibliographic record
Abstract
Background: The growing demand for hospital-based care, driven by aging populations and constrained resources, has accelerated the adoption of telehealth tools such as teleconsultations and remote monitoring in hospital-at-home (HaH) programs. Despite their increasing use in delivering acute care at home, studies exploring health care providers' experiences and perceptions of these tools within HaH settings remain limited. Objective: This study aimed to understand the experiences and perspectives of health care providers toward teleconsultations and vital signs monitoring systems within a HaH program in Singapore to optimize effectiveness and address challenges in future implementation. Methods: A convergent mixed methods approach that combines qualitative in-depth interviews with an electronic survey designed based on the 5 domains (usefulness, ease of use, effectiveness, reliability, and satisfaction) of the Telehealth Usability Questionnaire was used. Results: In total, 37 surveys and 20 interviews were completed. Participants responded positively to the use of both teleconsultation and vital signs monitoring with a mean total score of each method being 4.55 (SD 0.44) and 4.52 (SD 0.42), respectively. Significantly higher mean ratings were observed among doctors compared with other health care providers for usefulness (P=.03) and ease of use (P=.047) in teleconsultations. Health care providers with fewer years of clinical experience also perceived the use of vital signs monitoring to be more effective (P=.02) and more usable (P=.04) than those with more years of experience. Qualitative analysis identified four themes: (1) benefits of telehealth for health care providers such as improved work convenience, efficiency, and satisfaction; (2) challenges of telehealth implementation relating to communication and technology; (3) perspectives on telehealth impact; and (4) enablers for successful implementation. Comparing both datasets, qualitative findings were aligned with and confirmed quantitative results. Conclusions: This study highlighted the benefits and usability of telehealth among health care providers. However, challenges relating to patient communication, technological issues, and delivery of care were also discussed along with enablers for successful implementation. These insights can inform strategies to optimize future implementation of telehealth in HaH.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".