Assessing Primary Care Blood Pressure Documentation for Hypertension Management During the COVID-19 Pandemic by Patient and Provider Groups
Bibliographic record
Abstract
Background: Primary care electronic medical record (EMR) data can be used to identify, manage, and screen hypertension cases. However, this approach relies on completeness and accessibility of documented blood pressure (BP) values. With the large switch to virtual care due to the COVID-19 pandemic, we assessed BP documentation in primary care EMRs during the pandemic, across patient and physician groups. Methods: Hypertension-related visits were identified during the pre-pandemic (January 2017 to February 2020) and pandemic (March 2020 to December 2021) periods from a primary care EMR database in Ontario, Canada. Clustered logistic regression models were used to analyze the relationship of physician and patient characteristics with an outcome variable of documented BP. A chart review of 3200 hypertension visits without a BP recorded in structured data fields was conducted to determine if BP was recorded in progress notes. Results: Pre-pandemic, 75.7% of hypertension-related visits (113,966 of 150,511) had a BP recorded in structured documentation, but this significantly decreased to 36.4% (26,660 of 73,239) during the pandemic (odds ratio [OR] = 0.18, 95% confidence interval [CI]: 0.18-0.19). For virtual visits, 14.3% (6357 of 44,572) had a documented BP, vs 74.0% (20,056 of 27,089) for in-person visits. Chart review found that 55.9% of hypertension visits had no associated BP in structured documentation, but did have a BP recorded in the progress note. Male providers, compared to female providers, were less likely to record BPs pre-pandemic (OR = 0.45, 95% CI: 0.32-0.63) and during the pandemic, for both virtual visits (OR = 0.48, 95% CI: 0.32-0.71) and in-person visits (OR = 0.46, 95% CI: 0.33-0.64). Conclusions: BP documented in primary care EMRs declined during the pandemic, most likely due to high rates of virtual visits impacting hypertension detection and management.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".