Patient Perspectives With Telehealth Visits in Cardiology During COVID-19: Online Patient Survey Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The rise of COVID-19 and the issue of a mandatory stay-at-home order in March 2020 led to the use of a direct-to-consumer model for cardiology telehealth in Kentucky. Kentucky has poor health outcomes and limited broadband connectivity. Given these and other practice-specific constraints, the region serves as a unique context to explore the efficacy of telehealth in cardiology. OBJECTIVE: This study aims to determine the limitations of telehealth accessibility, patient satisfaction with telehealth relative to in-person visits, and the perceived advantages and disadvantages to telehealth. Our intent was two-fold. First, we wanted to conduct a rapid postassessment of the mandated overhaul of the health care delivery system, focusing on a representative specialty field, and how it was affecting patients. Second, we intend to use our findings to make suggestions about the future application of a telehealth model in specialty fields such as cardiology. METHODS: We constructed an online survey in Qualtrics following the Patient Assessment of Communication During Telemedicine, a patient self-report questionnaire that has been previously developed and validated. We invited all patients who had a visit scheduled during the COVID-19 telehealth-only time frame to participate. Questions included factors for declining telehealth, patient satisfaction ratings of telehealth and in-person visits, and perceived advantages and disadvantages associated with telehealth. We also used electronic medical records to collect no-show data for in-person versus telehealth visits to check for nonresponse bias. RESULTS: A total of 224 respondents began our survey (11% of our sample of 2019 patients). Our recruitment rate was 86% (n=193) and our completion rate was 62% (n=120). The no-show rate for telehealth visits (345/2019, 17%) was nearly identical to the typical no-show rate for in-person appointments. Among the 32 respondents who declined a telehealth visit, 20 (63%) cited not being aware of their appointment as a primary factor, and 15 (47%) respondents cited their opinion that a telehealth appointment was not medically necessary as at least somewhat of a factor in their decision. Both in-person and telehealth were viewed favorably, but in-person was rated higher across all domains of patient satisfaction. The only significantly lower mean score for telehealth (3.7 vs 4.2, P=.007) was in the clinical competence domain. Reduced travel time, lower visit wait time, and cost savings were seen as big advantages. Poor internet connectivity was rated as at least somewhat of a factor by 33.0% (35/106) of respondents. CONCLUSIONS: This study takes advantage of the natural experiment provided by the COVID-19 pandemic to assess the efficacy of telehealth in cardiology. Patterns of satisfaction are consistent across modalities and show that telehealth appears to be a viable alternative to in-person appointments. However, we found evidence that scheduling of telehealth visits may be problematic and needs additional attention. Additionally, we include a note of caution that patient satisfaction with telehealth may be artificially inflated during COVID-19 due to external health concerns connected with in-person visits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it