Patient-Reported Experiences in Outpatient Telehealth Heart Failure Management
Bibliographic record
Abstract
BACKGROUND: With the onset of coronavirus disease 2019 (COVID-19), the delivery of routine outpatient heart failure (HF) care abruptly shifted to telehealth. Appropriate HF management extensively relies upon patient-reported symptoms. With the growing attention towards patient-centered care, our team recognized an invaluable opportunity to solicit patient-reported subjective experiences regarding telehealth. METHODS: In total, 127 patients with a known diagnosis of HF were contacted by phone for participation in an online questionnaire. The tool consisted of questions generated by the investigators and from prior validated patient-reported experience measures. The intention was to assess the quality of care in our HF clinic and to solicit feedback regarding telehealth. RESULTS: Thirty-five patients provided a response. Questions with the most favorable outcomes were in line with our predetermined themes of interpersonal matter, communication, and perceived quality of care. The worst performing questions exhibited a lack of satisfaction with and perceived quality of telehealth. Only 9% (n = 3) preferred follow-up via telehealth, 69% (n = 22) preferred in-person, and 22% (n = 7) were indifferent. CONCLUSIONS: Given the multitude of benefits of telehealth, especially appropriate social distancing, telehealth is quite likely here to stay. In sum, with the rapid change in care delivery, patients currently perceive the care delivered via telehealth to be of inferior quality. This lack of quality can be largely attributed to the lack of physical examination, depersonalization of healthcare, and likely, a lack of familiarity with the platform. We urge our colleagues to solicit similar feedback from their patients to improve their own telehealth efforts.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".