Design of a Care Pathway for Preventive Blood Pressure Monitoring: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Electronic health (eHealth) services could provide a solution for monitoring the blood pressure of at-risk patients while also decreasing expensive doctor visits. However, a major barrier to their implementation is the lack of integration into organizations. OBJECTIVE: Our aim was to design a Care Pathway for monitoring the blood pressure of at-risk patients, in order to increase eHealth implementation in secondary preventive care. METHODS: A qualitative design study was used in this research. Data were collected by conducting visual mapping sessions including semistructured interviews with hypertension patients and doctors. The data were transcribed and coded and thereafter mapped into a Care Pathway. RESULTS: Four themes emerged from the results: (1) the current approach to blood pressure measuring has disadvantages, (2) risk and lifestyle factors of blood pressure measuring need to be considered, (3) there are certain influences of the at-home context on measuring blood pressure, and (4) new touchpoints between patients and health professionals need to be designed. These in-depth insights combined with the visualization of the current blood pressure process resulted in our Care Pathway design for monitoring the blood pressure of at-risk patients as secondary preventive care. CONCLUSIONS: The Care Pathway guides the implementation of eHealth devices for blood pressure self-measurement. It showcases the pathway of at-risk patients and increases their involvement in managing their blood pressure. It serves as a basis for a new service using eHealth.
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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 | 0.022 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".