Self-Measured Blood Pressure Telemonitoring Programs: A Pragmatic How-to Guide
Bibliographic record
Abstract
Self-measured blood pressure (SMBP) telemonitoring is the process of securely storing and tele-transmitting reliably measured, patient self-performed blood pressure (BP) measurements to healthcare teams, while ensuring that these data are viewable and clinically actionable for the purposes of improving hypertension diagnosis and management. SMBP telemonitoring is a vital component of an overall hypertension control strategy. Herein, we present a pragmatic guide for implementing SMBP in clinical practice and provide a comprehensive list of resources to assist with implementation. Initial steps include defining program goals and scope, selecting the target population, staffing, choosing appropriate (clinically validated) BP devices with proper cuff sizes, and selecting a telemonitoring platform. Adherence to recommended data transmission, security, and data privacy requirements is essential. Clinical workflow implementation involves patient enrollment and training, review of telemonitored data, and initiating or titrating medications in a protocolized fashion based upon this information. Utilizing a team-based care structure is preferred and calculation of average BP for hypertension diagnosis and management is important to align with clinical best practice recommendations. Many stakeholders in the United States are engaged in overcoming challenges to SMBP program adoption. Major barriers include affordability, clinician and program reimbursement, availability of technological elements, challenges with interoperability, and time/workload constraints. Nevertheless, it is anticipated that uptake of SMBP telemonitoring, still at a nascent stage in many parts of the world, will continue to grow, propagated by increased clinician familiarity, broader platform availability, improvements in interoperability, and reductions in costs that occur with scale, competition, and technological innovation.
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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 | 0.032 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.014 | 0.011 |
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".