The Impact of Telemedicine Visits on the Controlling High Blood Pressure Quality Measure During the COVID-19 Pandemic: Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Telemedicine visit use vastly expanded during the COVID-19 pandemic, and this has had an uncertain impact on cardiovascular care quality. OBJECTIVE: We sought to examine the association between telemedicine visits and the failure to meet the Controlling High Blood Pressure (BP) quality measure from the Centers for Medicare & Medicaid Services. METHODS: This was a retrospective cohort study of 32,727 adult patients with hypertension who were seen in primary care and cardiology clinics at an urban, academic medical center from February to December 2020. The primary outcome was a failure to meet the Controlling High Blood Pressure quality measure, which was defined as having no BP recorded or having a last recorded BP of ≥140/90 mm Hg (ie, poor BP control). Multivariable logistic regression was used to assess the association between telemedicine visit use during the study period (none, 1 telemedicine visit, or ≥2 telemedicine visits) and poor BP control; we adjusted for demographic and clinical characteristics. RESULTS: During the study period, no BP was recorded for 2.3% (486/20,745) of patients with in-person visits only, 27.1% (1863/6878) of patients with 1 telemedicine visit, and 25% (1277/5104) of patients with ≥2 telemedicine visits. After adjustment, telemedicine use was associated with poor BP control (1 telemedicine visit: odds ratio [OR] 2.06, 95% CI 1.94-2.18; P<.001; ≥2 telemedicine visits: OR 2.49, 95% CI 2.31-2.68; P<.001; reference: in-person visits only). This effect disappeared when the analysis was restricted to patients with at least 1 recorded BP (1 telemedicine visit: OR 0.89, 95% CI 0.83-0.95; P=.001; ≥2 telemedicine visits: OR 0.91, 95% CI 0.83-0.99; P=.03). CONCLUSIONS: Increased telemedicine visit use is associated with poorer performance on the Controlling High Blood Pressure quality measure. However, telemedicine visit use may not negatively impact BP control when BP is recorded.
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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 | 0.022 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".