Patient portal registration and healthcare utilisation in general practices in England: a longitudinal cohort study
Bibliographic record
Abstract
BACKGROUND: Patient portals introduced in most of England's general practices since 2015 have the potential to improve healthcare efficiency. There is a paucity of information on the use of patient portals within the NHS general practices and the potential impact on healthcare utilisation. AIM: To investigate the association between patient portal registration and care utilisation (measured by the number of general practice consultations) among general practice patients. DESIGN & SETTING: A longitudinal analysis using electronic health record data from the Clinical Practice Research Datalink (CPRD). METHOD: = 284 666), aggregating their consultations 1 year before and 1 year after registration. We ran a multilevel negative binomial regression model to examine patient portal registration's association with face-to-face and remote consultations. RESULTS: Patients who registered to the portal had a small decrease in the total number of face-to-face consultations after registering to the patient portal (incidence rate ratio = 0.93, 95% confidence interval [CI] = 0.93 to 0.94). Patients who registered to the portal had an increase in the total number of remote consultations after registering to the portal (incidence rate ratio = 1.16, 95% CI = 1.15 to 1.18). CONCLUSION: The study found minor changes in consultation numbers post-patient portal registration, notably with an increase in remote consultations. While causality between portal registration and consultation number remains unclear, the potential link between patient portal use and healthcare utilisation warrants further investigation, especially within the NHS, where portal impacts are not well-studied. Detailed portal utilisation data could clarify this relationship.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".