Prescription Refill Adherence Before and After Patient Portal Registration in Among General Practice Patients in England Using the Clinical Practice Research Datalink: Longitudinal Observational Study
Bibliographic record
Abstract
Background: Patient portal use has been associated with improved patient health and improved adherence to medications, including statins. However, there is limited research on the association between patient portal registration and outcomes such as statin prescription refill adherence in the context of the National Health Service of England, where patient portals have been widely available since 2015. Objective: We aimed to explore statin prescription refill adherence among general practice patients in England. Methods: This study was approved by the Clinical Practice Research Datalink Independent Scientific Advisory Committee (ID: 21_000411). We used patient-level general practice data from the Clinical Practice Research Datalink in England. The data included patients with cardiovascular disease, diabetes, and chronic kidney disease, who were registered on the patient portal. The primary aim was to investigate whether statin prescription refill adherence, defined as ≥80% adherence based on the medication possession ratio, improved after patient portal registration. We used a multilevel logistic regression model to compare aggregate adherence 12 months before and 12 months after patient portal registration. Results: We included 44,141 patients in the study. The analysis revealed a 16% reduction in the odds of prescription refill adherence 12 months after patient portal registration (odds ratio [OR]: 0.84, 95% CI 0.81-0.86) compared to 12 months before registration in the fully adjusted model for patient- and practice-level variables. Conclusions: This study evaluated prescription refill adherence after patient portal registration. Registering to the portal does not fully explain the mechanisms underlying the relationship between patient portal use and health-related outcomes such as medication adherence. Although a small reduction in prescription refill adherence was observed, this reduction disappeared when the follow up time was reduced to 6 months. Given the limitations of the study, reduction in prescription refill adherence cannot be entirely attributable to patient portal registration. However, there may be potential confounding factors influencing this association which should be explored through future research.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".