Exploring Care Providers’ Perceptions and Current Use of Telehealth Technology at Work, in Daily Life, and in Education: Qualitative and Quantitative Study
Bibliographic record
Abstract
BACKGROUND: A telehealth technology education curriculum designed to integrate information technology and telecommunication well has great potential to prepare care providers for health care delivery across space, time, and social and cultural barriers. It is important to assess the readiness level of care providers to use and maximize the benefits of telehealth technology in the health care delivery process. Therefore, this study explored care providers' existing experience using technology in various use contexts and compared their familiarity with telehealth technology's relevant features. OBJECTIVE: This study's objective was to explore care providers' familiarity with using technology in different settings and their perceptions of telehealth-driven care performance to lay a foundation for the design of an effective telehealth education program. METHODS: The study used quantitative and qualitative analyses. The online survey included four items that measured care providers' perceptions of care performance when using telehealth technology. Advanced practice registered nurse students rated each item on a 7-point Likert scale, ranging from 1 ("strongly disagree") to 7 ("strongly agree"). They also responded to three open-ended questions about what kinds of health information technology they use at work, after work, and in their current educational program. RESULTS: A total of 109 advanced practice registered nurse students responded to the online survey and open-ended questionnaire. Most indicated that using telehealth technology enhances care performance (mean 5.67, median 6.0, SD 1.36), helps make their care tasks more effective (mean 5.73, median 6.0, SD 1.30), improves the quality of performing care tasks (mean 5.71, median 6.0, SD 1.30), and decreases error in communicating and sharing information with others (mean 5.35, median 6.0, SD 1.53). In addition, our qualitative analyses revealed that the students used the electronic health records technology primarily at work, combined with clinical decision support tools for medication and treatment management. Outside work, they primarily used video-text communication tools and were exposed to some telehealth technology in their education setting. Further, they believe that use of nonhealth technology helps them use health information technology to access health information, confirm their diagnoses, and ensure patient safety. CONCLUSIONS: This research highlights the importance of identifying care providers' existing experience of using technology to better design a telehealth technology education program. By focusing explicitly on the characteristics of care providers' existing technology use in work, nonwork, and educational settings, we found a potential consistency between practice and education programs in care providers' requirements for technology use, as well as areas of focus to complement their frequent use of nonhealth technologies that resemble telehealth technology. Health policymakers and practitioners need to provide compatible telehealth education programs tailored to the level of care providers' technological familiarity in both their work and nonwork environments.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| 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".