Development and implementation of a patient reported experience measurement program to transform cancer care.
Notice bibliographique
Résumé
343 Background: Improving the patient experience is central to providing high-quality, value-based care. Patient reported experience measures (PREMs) are tools to evaluate and improve quality of care. Your Voice Matters (YVM) is a validated survey created by Cancer Care Ontario that captures patient experience in outpatient settings at Regional Cancer Centres across Ontario, Canada. While most hospitals actively collect patient experience data, few have moved to action feedback to improve outcomes. Princess Margaret Cancer Centre (PM) implemented a systematic approach to PREMs evaluation to advance operational excellence in an outpatient setting. Methods: PM distributes the YVM survey monthly to patients with outpatient visits via email using a Redcap survey platform (93% of patient emails are on file). In addition to the standardized YVM questions, the survey captures respondent comments in an open text field asking about one thing PM can improve and comments are thematically coded and trended monthly. Patients reporting a very poor experience with care (defined as an overall experience of 1/5) who wish to connect with PM are contacted to provide additional information. Key performance indicators (KPIs) were established and visualized in run charts in a Power BI dashboard. KPIs were disseminated to staff and leadership to identify opportunities for improvement. Results: Starting in 2022, approximately 10,600 surveys were disseminated monthly to patients and 1,100 surveys returned (10% response rate) with 10-15 patients contacted each month regarding their poor experience. Long wait times was the top concern across three main outpatient clinics; in the Gynecology clinics, in March 2022, only 45% (35/77) of respondents rated wait times as very good (5/5) and 32% (11/34) of comments were wait times complaints. A comprehensive root cause analysis found that the visit booking system, distribution of clinics across the weekdays, and supports in clinic were key contributors to long wait times. In response, Gynecology clinic resources were reorganized to launch a new Physician Assistant clinic on the underutilized Friday afternoon clinic timeslot, a new streamlined visit process was established, and the booking system was optimized. Within six months, there was a 39% improvement in wait times on weekdays, a 60% increase in the number of patients waiting less than 30 minutes, and a 37% increase in visit capacity on underutilized Fridays. The corresponding patient experience data over the same timeframe improved seen as an increase from 45% to 52% of patients who rated wait times to see the provider as 5/5. Conclusions: A programmatic approach to PREMs evaluation and response can drive improvement in practice. Facilitators include a data-driven methodologic approach to quality improvement, leadership engagement at all levels, and elevation of frontline staff as local experts and change agents.
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 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 | 0,001 | 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 ».