Personal Health Record implementation in rural primary care: A descriptive exploratory study using RE-AIM framework
Bibliographic record
Abstract
Demand is emerging for personal health records (PHRs), a patient-centric digital tool for engaging in shared decision-making and healthcare data management. This study uses a RE-AIM framework to explore rural patients and providers' perceptions prior to and following implementation of a PHR. Health care providers and their patients were recruited from early-adopter patient medical home clinics and a local patient advisory group. Focus groups were used to explore patient and provider pre-implementation perceptions of PHRs and post-implementation provider perspectives. Patients were invited through participating clinics to use the PHR. An implementation process evaluation was conducted. Multiple methods and data sources were used and included pre-/post-intervention patient surveys, provider interviews, and PHR/EHR administrative data. Both patient and provider focus groups described PHRs as providing a comprehensive health story and enhanced communication. Patients prioritized collection of health promotion data while providers endorsed health-related, clinical data. Both groups expressed the need for managing expectations and setting boundaries on PHR use. The evaluation indicated Reach: 16% of targeted patients participated and an additional 127 patients used the PHR as a tool during the COVID-19 pandemic. Effectiveness: Patient satisfaction with use was neutral, with no significant changes to quality of life, self-efficacy, or patients' activation. Adoption: 44% of eligible clinics participated, primarily those operated publicly versus privately, in smaller communities, and farther from a regional hospital. Implementation: Despite system interoperability expectations, at time of roll out, information exchange standards had not been reached. Additional implementation complications arose from the onset of the pandemic. One clinic on-boarded additional patients resulting in a rapid spike in PHR use. Maintenance: All clinics discontinued PHR within the study period, citing several key barriers to use. RE-AIM offers a valuable process evaluation framework for a comprehensive depiction of impact, and how to drive future success. Interoperability, patient agency and control, and provider training and support are critical obstacles to overcome in PHR implementation.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".