The Use of Telemedicine to Improve Hypertension in an Urban Primary Care Clinic and Predictors of Improved Blood Pressure
Bibliographic record
Abstract
Background and Objective:Meta-analysis of randomized controlled trials have demonstrated the efficacy of telemedicine in blood pressure (BP) management when compared to conventional care. We initiated a hypertension telehealth clinic in our urban primary care clinic and through this study aim to evaluate the strengths and limitations of telemedicine in hypertension (HTN) control. The primary outcome of the study is to identify the proportion of patients with improved HTN. Secondary outcomes included identifying: predictors for lower BP, predictors of missing telehealth appointments, and comorbid conditions that are more likely to necessitate use of more than 1 antihypertensive medication.Methods and Analysis:Patients seen in the HTN telehealth clinic from May 1st, 2022 to October 31st, 2022 were identified. A retrospective chart review was done to compare the BP during in-person visit prior to first telehealth visit, telehealth visit home BP readings and last recorded in-office BP on chart at end of study period. Descriptive statistical analysis, Chi Square test, and multivariable logistic regression was used to analyze data.Results:Of the 234 appointments, 83% were conducted and 154 patients were seen. A remarkable decrease in percentage of patients with BP >140/90 was seen when comparing in-office visit BP to first telehealth visit home BP, 72% versus 45% respectively. No remarkable difference was noted in percentage of patients with BP >140/90 when comparing first telehealth visit home BP to last in-office BP recorded on chart, 45% and 41% respectively. Patients with diabetes had lower odds of missing appointments, adjusted odds ratio (aOR): 0.34 ([0.12-0.91], P = .03). Patients with partners were more likely to have lower BP at the telehealth visit, aOR:3.2 ([1.15-9.86], P = .03) while patients with obstructive sleep apnea (OSA) (aOR 0.27 ([0.08-0.77], P = .02) and CAD, aOR 0.24 ([0.06-0.8], P = .03) were less likely to have lower BP.Conclusion:The study demonstrated telemedicine as a great tool to prevent overtreatment of hypertension as significant difference between in-office BP and home BP during telehealth visits was noted. We did not see a significant change in blood pressure when comparing home BP at first telehealth visit to the last in-person clinic BP at end of study period.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.006 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".