An Orthopaedic perspective on developing a Patient Experience Survey tool
Notice bibliographique
Résumé
Introduction: Our program’s aim is to integrate patients' values and perceptions of quality care into clinical decision-making and integrated orthopaedic care models that are reflective of the community/population that is served. Our institution currently has limited patient experience data or standardized tools that would allow us to comprehensively understand patient perspective across our orthopaedic populations. To that end, we designed a survey to understand patients’ experiences and any gaps that would require actionable change in real-time. Objectives and Methodology: The goal was to develop a survey tool that can meaningfully capture patient needs and preferences that impact their experiences and help identify any gaps or areas of opportunity to address. An environmental scan was completed which identified two validated patient experience surveys used to inform our survey development: CIHI’s CPES-IC (Canadian Institue for Health Information Canadian Patient Experience Survey - Inpatient Care) and OHA (Ontario Hospital Association) Outpatient Experience Survey. The survey was tested across a multidisciplinary team via feedback surveys and focus groups: 6 Patient Partners, 1 Clinical Manager, 3 Advanced Practice Practitioners, 3 Orthopaedic Surgeons, 4 Researchers, and 2 Administrators. Findings were collated and used to iterate the survey content until consensus was reached on a final patient experience survey. Highlights or Results or Key Findings: The focus of the survey is on understanding the patient’s Outpatient experience with their care team in orthopaedic clinics from referral to assessment. The survey was designed to cover multiple orthopaedic populations including patients with Hip and Knee, Foot and Ankle, Shoulder, Elbow and Spine issues. It focuses on two key components of the patient journey: 1) Pre-appointment and 2) Post-appointment. The pre-appointment pathway was deemed to be important as patient experience is often captured once care is delivered and it was important to understand how patients perceive their pre-appointment experience defined as the time from referral through day of appointment up to the time patients are seen (arrival at hospital, checking in to clinic). Specifically, the pre-appointment survey has up to 42 questions that aim to capture the patients’ perspectives on the clinic’s communication practices, wait time to be contacted, wait time for an appointment, how prepared patients felt for their appointment, how they felt about access to the clinic, their experiences with care transitions, their thoughts on transportation and any education that they received, and their overall experience. The post-appointment survey has up to 41 questions focused on understanding the patients’ perspectives on care transitions, the clinic space; wait time to see the practitioner, expectations, engagement in decision making, communication practices, education they received, and any other factors impacting their experience (e.g. confidentiality, empathy, trust, dignity). Conclusion and Next Steps: The survey, reviewed with our patient partners and multidisciplinary team, was found to be comprehensive and have good face validity. The survey will be implemented and the data will be analyzed to understand themes around areas of success and areas of improvement to advance our integrated models of care in order to provide high quality patient-centered care.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,069 | 0,103 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,004 |
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 source (Gemma direct ou Codex distillé), 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 ».