Telehealth Acceptance and Perceived Barriers Among Health Professionals: Pre-Post Evaluation of a Web-Based Telehealth Course
Bibliographic record
Abstract
Background: The rapid expansion of telehealth underscores the need for comprehensive telehealth education among health care professionals. Despite increasing recognition of telehealth's importance, many practitioners remain underprepared, particularly in navigating legal aspects, technology, and patient engagement. Objective: This study aimed to evaluate the impact of a web-based telehealth training course on health care professionals' telehealth acceptance and their perceived barriers to telehealth adoption. Methods: An interventional study with a pre-post design was used in Austria. A total of 365 health professionals enrolled in an asynchronous web-based course covering general telehealth principles (concepts, legal and technical aspects, practical implementation) and profession-specific content (eg, nursing, speech therapy, and physiotherapy). Of these, 217 completed the course, and 185 met inclusion criteria for analysis. Participants' telehealth acceptance (covering telemetry, telephasis, and telepraxis) and perceived barriers were assessed via standardized questionnaires before and after the course. Satisfaction with the training was measured post-intervention using the Training Evaluation Inventory. Qualitative insights were gathered from open-ended survey questions and 2 focus groups, transcribed, and summarized. Results: Post-intervention, overall telehealth acceptance increased significantly (P<.001, r=0.21), particularly for telemetry (remote assessment and monitoring), telepraxis (remote interventions), video call-based, and asynchronous telehealth. Perceived barriers to telehealth use-such as uncertainty about legal frameworks, data protection, and reduced quality of care-diminished significantly (P<.001, r=0.39). Post-intervention satisfaction was high, with a total median Training Evaluation Inventory score of 76 (IQR 13). Participants rated the course highly for its clarity, breadth of content, and inclusion of profession-specific modules. Qualitative feedback highlighted a desire for more hands-on demonstrations, interactive components, and guidance on institutional support and patient accessibility. Conclusions: A structured, on-demand telehealth course significantly improved health professionals' awareness, acceptance, and intention to use telehealth and reduced perceived barriers. While the findings highlight that targeted web-based training can increase clinicians' confidence and readiness to use telehealth, it remains uncertain whether this will lead to an increase in its utilization. Future initiatives should incorporate blended-learning formats with additional practical examples, real-time discussions, and ongoing support to enhance long-term integration of telehealth into clinical workflows. On a policy level, we suggest coordinated actions at the EU, national, and institutional levels to standardize telehealth education and facilitate its practical implementation in everyday clinical practice.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".