Effects of a Web-Based Patient Portal on Patient Satisfaction and Missed Appointment Rates: Survey Study
Bibliographic record
Abstract
BACKGROUND: Although electronic medical record (EMR)-tethered patient portals are common in other countries, they are still emerging in Canada. OBJECTIVE: We aimed to report user satisfaction and the effects of a patient portal on medical appointment attendance in a Canadian cohort of patients within our publicly funded health care system. METHODS: Two surveys were deployed, via email, at 2 weeks and 6 months following the first recorded patient portal access. Database audits of visit attendance were used to supplement and cross reference survey data. RESULTS: Between January 2016 and July 2018, 4296 patients accessed the patient portal. During the study, 28% (957/3421) consented patient portal users responded to one or more semistructured electronic surveys. Of respondents, 93% (891/957) reported that the patient portal was easy to use, 51% (492/975) reported it saved time when scheduling an appointment, and 40% (382/957) reported that they had to repeat themselves less during appointments. Respondents reported patient portal-related changes in health system use, with 48% (462/957) reporting avoiding a clinic visit and 2.7% (26/957) avoiding an emergency department visit. Across 19,968 visits in clinics where the patient portal was introduced, missed appointments were recorded in 9.5% (858/9021) of non-patient portal user visits, compared with 4.5% (493/9021) for patient portal users, representing a 53% relative reduction in no-show rates. CONCLUSIONS: Early experience with an EMR-tethered patient portal showed strong reports of positive patient experience, a self-reported decrease in health system use, and a measured decrease in missed appointment rates. Implications on the expanded use of patient portals requires more quantitative and qualitative study in Canada.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".