Self-Reported Patient and Provider Satisfaction With Neurology Telemedicine Visits After Rapid Telemedicine Implementation in an Urban Academic Center: Cross-Sectional Survey
Bibliographic record
Abstract
BACKGROUND: Many clinics and health systems implemented telemedicine appointment services out of necessity due to the COVID-19 pandemic. OBJECTIVE: Our objective was to evaluate patient and general provider satisfaction with neurology telemedicine implementation at an urban academic medical center. METHODS: Patients who had completed 1 or more teleneurology visits from April 1 to December 31, 2020, were asked to complete a survey regarding their demographic information and satisfaction with teleneurology visits. Providers of all specialties within the same hospital system were given a different survey to gather their experiences of providing telemedicine care. RESULTS: Of the estimated 1500 patients who had completed a teleneurology visit within the given timeframe, 117 (7.8%) consented to complete the survey. Of these 117 respondents, most appointments were regarding epilepsy (n=59, 50.4%), followed by multiple sclerosis (n=33, 28.2%) and neuroimmunology (n=7, 6%). Overall, 74.4% (n=87) of patients rated their experience as 8 out of 10 or higher, with 10 being the highest satisfaction. Furthermore, 75.2% (n=88) of patients reported missing an appointment in the previous year due to transportation issues and thought telemedicine was more convenient instead. A significant relationship between racial or ethnic group and comfort sharing private information was found (P<.001), with 52% (26/50) of Black patients reporting that an office visit is better, compared to 25% (14/52) of non-Black patients. The provider survey gathered 40 responses, with 75% (n=30) of providers agreeing that virtual visits are a valuable tool for patient care and 80% (n=32) reporting few to no technical issues. The majority of provider respondents were physicians on faculty or staff (n=21, 52%), followed by residents or fellows (n=15, 38%) and nurse practitioners or physician assistants (n=4, 10%). Of the specialties represented, 15 (38%) of the providers were in neurology. CONCLUSIONS: Our study found adequate satisfaction among patients and providers regarding telemedicine implementation and its utility for patient care in a diverse urban population. Additionally, while access to technology and technology literacy are barriers to telemedical care, a substantial majority of patients who responded to the survey had access to devices (101/117, 86.3%) and were able to connect with few to no technological difficulties (84/117, 71.8%). One area identified by patients in need of improvement was comfortability in communicating via telemedicine with their providers. Furthermore, while providers agreed that telemedicine is a useful tool for patient care, it limits their ability to perform physical exams. More research and quality studies are needed to further appreciate and support the expansion of telemedical care into underserved and rural populations, especially in the area of subspecialty neurological care.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".