CAN WE ACHIEVE HIGH COMPLIANCE IN COLLECTING PATIENT-REPORTED OUTCOMES? THE OTTAWA EXPERIENCE
Bibliographic record
Abstract
Assessing quality of care in orthopaedics is important to our patients and surgeons as well as governmental agencies. Patient reported outcome measures (PROMs) represent the cornerstone to assess the effectiveness of our interventions. The purpose of this study was to assess the compliance and sustainability of collecting PROMs in a tertiary academic center in 9 clinical orthopedic surgery units with a total of 39 condition groups. 1616 patients were identified over a 10-month period. The age distribution was 18–92 years with a mean of 58.3 years, with 50.1% of the patients being female. Patients were identified and consented for participation at the time of surgical consent. The condition group and the side of the procedure were recorded by the surgeon. Patients were sent an automated email with pre-operative questionnaires within a week of consenting. Once the surgery was scheduled, patients were contacted up to three times by a quality improvement research team member within a week prior to their surgery, to remind them to complete the questionnaires. The same combination of auto-generated questionnaire emails and phone call reminders was used at post-operative time points. The team consisted of one full-time research coordinator and two assistants. Patients on average answered ~5 questionnaires at each collection timepoint. PROM collection was only considered complete if all assigned questionnaires were completed. A total of 1366 patients consented to be included in the pre-operative portion of the study, for a consent rate of 84.5%. Eight hundred twelve of these patients gave prior consent in-clinic, and 554 provided verbal consent over the phone. The automated email questionnaires were completed in 305 cases, for an initial compliance rate of 37.6%. With the addition of the phone-call protocol reminder, a total of 1155 patients completed the questionnaires, increasing the overall compliance rate to 84.6%. Post-operative compliance was assessed at the 3-month and 1 year time point with a compliance of 47.6% and of 54.8%, respectively with the automated email alone. With the addition of the phone-call protocol compliance increased to 67.9% and 70.4%, respectively. Collecting PROMs for a variety of musculoskeletal conditions with a high compliance rate is achievable. However, this requires a coordinated effort with multiple touch points and financial support from the institution.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".