CAN WE ACHIEVE HIGH COMPLIANCE IN COLLECTING PATIENT-REPORTED OUTCOMES? THE OTTAWA EXPERIENCE
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».