Impact of health portal enrolment with email reminders at an academic rheumatology clinic
Bibliographic record
Abstract
Missed appointments reduce the quality, safety and efficiency of healthcare delivery. 'No-Shows' (NS) have been identified as a problem within the rheumatology clinic at Sunnybrook Health Sciences Center in Toronto, Ontario. NS were studied through a prospective chart review and telephone interviews. Over 6 months, 110 NS took place (rate 2.5-6.8%). From interviews, 85% of NS were attributed to forgetting, being unaware of the appointment, having the wrong date, or another miscommunication. Fifty-seven percent of patients were interested in an appointment reminder, including electronic reminders (46%). Patients were encouraged to enroll in the hospital's electronic patient portal, MyChart, and email reminders were implemented at one clinic for portal users. A detailed follow-up card was also given to patients. Process measures included portal enrolment, email reminder receipt, and call volumes. Outcome measures were NS and patient and staff satisfaction. During the intervention, 120/274 (44%) surveyed patients had MyChart accounts. Of these, 73 (61%) received the e-mail reminder and 72 (99%) found the e-mail helpful. Twenty-two patients knew about their appointment from the e-mail reminder alone. Improvement in attendance was seen after 3.5 months, but it was not sustained thereafter. Prior to this intervention there was no appointment reminder system at this clinic, and the email reminder demonstrated high patient satisfaction. Low portal enrolment, technical difficulties, and the inability of the intervention to reach new patients were possible reasons why the intervention was unsuccessful at reducing NS.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".