The relationship between patient experience and real-world digital health access in primary care: A population-based cross-sectional study
Bibliographic record
Abstract
Implementing digital health technologies in primary care is anticipated to improve patient experience. We examined the relationships between patient experience and digital health access in primary care settings in Ontario, Canada. We conducted a retrospective cross-sectional study using patient responses to the Health Care Experience Survey linked to health and administrative data between April 2019-February 2020. We measured patient experience by summarizing HCES questions. We used multivariable logistic regression stratified by the number of primary care visits to investigate associations between patient experience with digital health access and moderating variables. Our cohort included 2,692 Ontario adults, of which 63.0% accessed telehealth, 2.6% viewed medical records online, and 3.6% booked appointments online. Although patients reported overwhelmingly positive experiences, we found no consistent relationship with digital health access. Online appointment booking access was associated with lower odds of poor experience for patients with three or more primary care visits in the past 12 months (adjusted odds ratio 0.16, 95% CI 0.02-0.56). Younger age, tight financial circumstances, English as a second language, and knowing their primary care provider for fewer years had greater odds of poor patient experience. In 2019/2020, we found limited uptake of digital health in primary care and no clear association between real-world digital health adoption and patient experience in Ontario. Our findings provide an essential context for ensuing rapid shifts in digital health adoption during the COVID-19 pandemic, serving as a baseline to reexamine subsequent improvements in patient experience.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".