Assessing Health Care Professionals' Mindset in Adopting Telemedicine Post COVID-19: Pilot Questionnaire Study
Bibliographic record
Abstract
BACKGROUND: Amidst the COVID-19 pandemic, the traditional health care model has evolved toward a more patient-centric model. In relation to this trend, digital health services have seen an acceleration, which may have significant implications for the health care model. Due to the impact of COVID-19 on health care facilities, it is important to explore health professionals' willingness to adopt a patient-centric digital health delivery model for medicine and health care. OBJECTIVE: The aim of this study was to pilot a survey that assesses the impact and implementation of telehealth in view of health care providers prior to and post COVID-19. METHODS: A total of 26 volunteer health care professionals participated in the pilot study, of which 19/26 (73%) completed the general demographics portion. Among these respondents, 9/26 (35%) completed the entirety of the survey. The questionnaire included questions relating to general demographics, accessibility and benefits, usability, and engagements with telemedicine. Participants were randomly assigned to 1 of the 4 questionnaire designs (A-D) based on their expertise in telehealth. Of the 9 total participants who completed their randomly assigned questionnaire, 1 (11%) was randomly assigned to A, 3 (33%) were randomly assigned to B, 2 (22%) were randomly assigned to C, and 3 (33%) were randomly assigned to D. RESULTS: Responses and data from the study questionnaire were collected from Qualtrics. Microsoft Excel was used for data organization. Due to limited responses and data, no advanced statistical software was implemented. From the 9 participants who completed the entirety of the survey, responses from those with telehealth experience (n=4) showed that telehealth was preferred for follow-ups, lab results, and consultations, and that with telehealth, there was greater flexibility with appointment times and a decrease in the number of patients seen. Among the 4 health care providers with telehealth experience, all of them believed it improved accessibility and reduced physical barriers; health care professionals believed telehealth reduced translational barriers with patients. Among health care professionals without telehealth experience (n=5), 60% (3/5) reported a decrease in appointments for in-office visits post COVID-19 and strongly agreed or agreed that telehealth could influence the quality of care for patients. All 5 participants also reported no general concerns about telehealth prior to the pandemic and agreed that it would provide accessibility for patients. CONCLUSIONS: Preliminary findings of our pilot study showed initial support of a dynamical shift within the health care model due to the rise in the use of telehealth services between health care providers and patients but no statistically significant results. Further research and investigation with a larger sample size is warranted to better understand the mindset of health care professionals in adopting telemedicine post COVID-19.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".