Utilizing Consumer Health Informatics to Support Management of Hypertension by Clinical Pharmacists in Primary Care: Study Protocol
Bibliographic record
Abstract
BACKGROUND: Hypertension (HTN) is a major public health concern in the United States given its wide prevalence, high cost, and poor rates of control. Multiple strategies to counter this growing epidemic have been studied, and home blood pressure (BP) monitoring, mobile health (mHealth) interventions, and referrals to clinical pharmacists for BP management have all shown potential to be effective intervention strategies. OBJECTIVE: The purpose of this study is to establish feasibility and acceptability of BPTrack, a clinical pharmacist-led mHealth intervention that aims to improve BP control by supporting home BP monitoring and medication adherence among patients with uncontrolled HTN. BPTrack is an intervention that makes home-monitored BP data available to clinical pharmacists for use in HTN management. Secondarily, this study seeks to understand barriers to adoption of this intervention, as well as points of improvement among key stakeholders, so that larger scale dissemination of the intervention may be achieved and more rigorous research can be conducted. METHODS: This study is recruiting up to 25 individuals who have poorly controlled HTN from a Family Medicine clinic affiliated with a large Midwestern academic medical center. Patient participants complete a baseline visit, including installation and instructions on how to use BPTrack. Patient participants are then asked to follow the BP monitoring protocol for a period of 12 weeks, and subsequently complete a follow-up visit at the conclusion of the study period. RESULTS: The recruitment period for the pilot study began in November 2016, and data collection is expected to conclude in late-2017. CONCLUSIONS: This pilot study seeks to document the feasibility and acceptability of a clinical pharmacist-led mHealth approach to managing HTN within a primary care setting. Through our 12-week pilot study, we expect to lend support for this approach, and lay the foundation for translating this approach into wider-scale implementation. This mHealth intervention seeks to leverage the multidisciplinary care team already in place within primary care, and to improve health outcomes for patients with uncontrolled HTN. TRIAL REGISTRATION: Clinicaltrials.gov NCT02898584; https://clinicaltrials.gov/ct2/show/NCT02898584 (Archived by WebCite® at http://www.webcitation.org/6u3wTGbe6).
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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 | 0.031 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.051 | 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".