Using Health Information Technology to Foster Engagement: Patients’ Experiences with an Active Patient Health Record
Bibliographic record
Abstract
Personal health records (PHRs) typically employ "passive" communication strategies, such as non-personalized medical text, rather than direct patient engagement in care. Currently there is a call for more active PHRs that directly engage patients in an effort to improve their health by offering elements such as personalized medical information, health coaches, and secure messaging with primary care providers. As part of a randomized clinical trial comparing "passive" with "active" PHRs, we explore patients' experiences with using an "active" PHR known as HealthTrak. The "passive" elements of this PHR included problem lists, medication lists, information about patient allergies and immunizations, medical and surgical histories, lab test results, health reminders, and secure messaging. The active arm included all of these elements and added personalized alerts delivered through the secure messaging platform to patients for services coming due based on various demographic features (including age and sex) and chronic medical conditions. Our participants were part of the larger clinical trial and were eligible if they had been randomized to the active PHR arm, one that included regular personalized alerts. We conducted focus group discussions on the benefits of this active PHR for patients who are at risk for cardiovascular disease. Forty-one patients agreed to participate and were organized into five separate focus group sessions. Three main themes emerged from the qualitatively analyzed focus groups: participants reported that the active PHR promoted better communication with providers; enabled them to more effectively partner with their providers; and helped them become more proactive about tracking their health information. In conclusion, patients reported improved communication, partnership with their providers, and a sense of self-management, thus adding insights for PHR designers hoping to address low adoption rates and other patient barriers to the development and use of the technology.
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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 | 0.016 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".