Improving Access to Cardiovascular Care Through Telehealth: A Single-Center Experience
Bibliographic record
Abstract
Background: Historically, access to healthcare has been a serious shortcoming of our healthcare system. Approximately 14.5% of US adults lack readily available access to health care and this has been worsened by the coronavirus disease 2019 (COVID-19) pandemic. There are limited data on the use of telehealth in cardiology. We share our single-center experience in improving access to care via telehealth at the University of Florida, Jacksonville cardiology fellows' clinic. Methods: Demographic and social variables were collected 6 months before and 6 months after the initiation of telehealth services. The effect of telehealth was determined via Chi-square and multiple logistic regression while controlling for demographic covariates. Results: We analyzed 3,316 cardiac clinic appointments over 1 year. Of these, 1,569 and 1,747 were before and after the start of telehealth, respectively. Fifteen percent (272 clinical encounters) out of the 1,747 clinic visits during the post-telehealth era were through telehealth, completed via audio or video consultation. Overall, there was a 7.2 % increase in attendance after the implementation of telehealth (P value < 0.001). Patients who attended their scheduled follow-up had significantly greater odds of being in the post-telehealth group while controlling for marital status and insurance type (odds ratio (OR): 1.31, 95% confidence interval (CI): 1.07 - 1.62). Patients who attended had higher odds of having City-Contract insurance - an institution-specific indigenous care plan (OR: 3.51, 95% CI: 1.79 - 6.87) compared to private insurance. Patients who attended also had higher odds of being previously married (OR: 1.34, 95% CI: 1.05 - 1.70) or married/dating (OR: 1.39, 95% CI: 1.05 - 1.82) compared to patients who were single. Surprisingly, telehealth did not lead to an increase in the use of Mychart, our electronic patient portal (P value = 0.55). Conclusions: Telehealth enhanced patients' access to care by improving appointment show-rate in a cardiology fellows' clinic during the COVID-19 pandemic. Telehealth as a resource adjunct to traditional care in cardiology fellows' clinic should be further explored.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".