Adopting Telemedicine for the Self-Management of Hypertension: Systematic Review
Bibliographic record
Abstract
BACKGROUND: Hypertension is a chronic condition that affects adults of all ages. In the United States, 1 in 3 adults has hypertension, and about half of the hypertensive population is adequately controlled. This costs the nation US $46 billion each year in health care services and medications required for treatment and missed workdays. Finding easier ways of managing this condition is key to successful treatment. OBJECTIVE: A solution to reduce visits to physicians for chronic conditions is to utilize telemedicine. Research is limited on the effects of utilizing telemedicine in health care facilities. There are potential benefits for implementing telemedicine programs with patients dealing with chronic conditions. The purpose of this review was to weigh the facilitators against the barriers for implementing telemedicine. METHODS: Searches were methodically conducted in the Cumulative Index to Nursing and Allied Health Literature Complete (CINAHL Complete) via Elton B Stephens Company (EBSCO) and PubMed (which queries MEDLINE) to collect information about self-management of hypertension through the use of telemedicine. RESULTS: Results identify facilitators and barriers corresponding to the implementation of self-management of hypertension using telemedicine. The most common facilitators include increased access, increase in health and quality, patient knowledge and involvement, technology growth with remote monitoring, cost-effectiveness, and increased convenience/ease. The most prevalent barriers include lack of evidence, self-management difficult to maintain, no long-term results/more areas to address, and long-term added workload commitment. CONCLUSIONS: This review guides health care professionals in incorporating new practices and identifying the best methods to introduce telemedicine into their practices. Understanding the facilitators and barriers to implementation is important, as is understanding how these factors will impact a successful implementation of telemedicine in the area of self-management of hypertension.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".