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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".