Connecting Home-Based Self-Monitoring of Blood Pressure Data Into Electronic Health Records for Hypertension Care: A Qualitative Inquiry With Primary Care Providers
Bibliographic record
Abstract
BACKGROUND: There is a lack of research on how to best incorporate home-based self-measured blood pressure (SMBP) measurements, combined with other patient-generated health data (PGHD), into electronic health record (EHR) systems in a way that promotes primary care workflow without burdening the primary care team with irrelevant or superfluous data. OBJECTIVE: The purpose of this study was to explore the perspectives of primary care providers in utilizing SMBP measurements and integrating SMBP data into the clinical workflow for the management of hypertension in the primary care setting. METHODS: A total of 13 primary care physicians were interviewed in total; 5 in individual interviews and 8 in a focus group. The interview questions were centered on (1) the value of SMBP in hypertension care, (2) needs of viewing SMBP and desired visual display, (3) desired alert algorithm and critical values, (4) needs for other PGHD, and (5) workflow of primary care team in utilizing SMBP. The interviews were audiotaped and transcribed verbatim, and a thematic analysis was performed to extract overarching themes. RESULTS: The primary care experience of the 13 providers ranged from 5 to 35 years. The following themes emerged from the individual and focus group interviews: (1) ways to utilize SMBP measurements in primary care, (2) preferred visual display of SMBP, (3) patient condition determines preferred scheduling of patient SMBP measurements and provider's preferred frequency of viewing SMBP data, (4) effect of patient condition on alert parameters, (5) location to receive critical value alerts, (6) primary recipient of critical value alerts, and (7) the need of additional PGHD (eg, emotional stressors, food diary, and medication adherence) to provide context of SMBP values. CONCLUSIONS: The perspectives of primary care providers need to be incorporated into the design of a built-in interface in the EHR to incorporate SMBP and other PGHD. Future usability evaluation should be conducted with mock-up interfaces to solicit opinions on the optimal alert frequency and mechanism to best fit the workflow in the primary care setting. Future studies should examine how the utilization of a built-in interface that fully integrates SMBP measurements and PGHD into EHR systems can support patient self-management and thus, improve patient outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".