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Record W4415261916 · doi:10.1161/hyp.82.suppl_1.th128

Abstract TH128: Home Blood Pressure Monitoring in Subspecialty Clinics: A Needs Assessment

2025· article· en· W4415261916 on OpenAlexaff
Sachin Vidur Pasricha, Paula Harvey, Michelle Bergeron, Tara O’Brien, Leora Brandfield-Day, Lisa Dubrofsky

Bibliographic record

VenueHypertension · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Risk Factors
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreUniversity of Toronto
Fundersnot available
KeywordsSubspecialtyAuditMedical recordDocumentationBlood pressureWhite coat hypertensionQuality management

Abstract

fetched live from OpenAlex

Objectives: Home blood pressure monitoring (HBPM) distinguishes sustained hypertension from masked and white coat hypertension, and has been shown to improve blood pressure control and medication adherence. We suspected that HBPM use may be inadequate at our institution as there is no uniform mechanism for communicating or documenting HBPM. Methods: We developed a quality improvement (QI) initiative to improve uptake of and documentation of HBPM. Our aim was for 50% of patients with hypertension to be performing HBPM on ≥3 days (i.e. ≥6 readings) prior to appointments. We performed a retrospective gap analysis using chart audits to assess HBPM documentation within internal medicine, nephrology, cardiology and endocrinology visits. We also surveyed clinicians to assess current use of and barriers to HBPM. We created an Ishikawa (fishbone) diagram to outline the patient, provider, equipment and organization factors required for adequate HBPM usage. Results: We audited 10 randomly selected charts with a visit diagnosis of “hypertension” from four divisions totalling 40 visits from 17 different providers. HBPM readings were mentioned in 26 out of 40 visits (65%), mostly noted either as estimated ranges (e.g. “150s/90s”) or a few individual readings. Only 3 out of 40 audited visits noted that the patient had taken ≥6 individual HBPM readings. Our survey of 19 clinicians revealed that 84% recommend HBPM, but 79% are unable to obtain adequate data for HBPM more than half the time. 79% reported they would use an electronic medical record integrated tool to record HBPM if available. Our Ishikawa analysis revealed that lack of standardize workflow and lack of an integrated electronic health record to capture HBPM readings are key barriers to HBPM uptake. Conclusions: Our QI initiative revealed a paucity of HBPM integration into clinical care, despite clinician preference and recommendation for HBPM. Lack of an electronic tool that integrates HBPM readings efficiently with routine visits was identified as a modifiable barrier to HBPM uptake, therefore future work is focused on creating and evaluating such a tool.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.002

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.

Opus teacher head0.025
GPT teacher head0.319
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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