Putting Patient Experience Data into Action! A Workshop to Explore the Collection and Use of Patient Experience Data in Practice
Notice bibliographique
Résumé
Health systems globally are in pursuit of the quintuple aim – better population health, improved patient and caregiver experiences, improved clinician experiences, sustainable per capita costs1 and greater health equity. Patients and their care partners (family and friends who provide care) have unique insights on the quality of healthcare delivery, yet, few organizations are poised to meaningfully integrate these voices into care delivery transformation efforts. Patient experience (PE) data comes in many forms including (and not limited to) structured and unstructured survey data, narratives of patient and family complaints or accolades, adverse event reporting, or qualitative interviews and focus groups. These data are often temporally lagging the patient’s interaction with the health system, rarely includes the voices of equity deserving groups (i.e, individuals who have been historically marginalized and underrepresented in research) and are seldom used for improving care. To meet the expectations of patients and care partners, we require a comprehensive, systematic, and sustainable approach to integrating patient and care partner voices into how we reform, design and deliver care. Through a scoping review and stakeholder engagements we have highlighted that methods for the collection and implementation of PE data should be multidimensional and have identified four components of a PE Tool-kit (PET) to move from knowledge to action: 1) engage the community meaningfully; 2) identify and curate multiple forms of PE data; 3) aggregate and synthesize PE data; and 4) integrate PE data into quality improvement efforts. We are conducting this workshop to further explore domains within our proposed Patient Experience Tool-Kit that resonates with an international audience and identify gaps and implementation considerations. The workshop will be grounded by a brief theory burst about the challenges the PET and its components aim to address followed by interactive sessions in small groups to address the following questions: 1)How will the proposed components support the link between the collection and use of PE data into health system improvement efforts? What tool-kit components are missing? 2)What are the barriers and facilitators to implementing the PE Tool-Kit? Note: we will direct participants to use a sociotechnical systems approach when identifying barriers and facilitators which consists of: people, environment (macro/micro), tools (i.e., technology), and tasks (i.e., process). 3)What are the considerations/adaptations to consider that will support the Tool-Kit’s adoption across different sectors of the integrated care system? Introduction to Workshop/ Overview of Activities – 5 mins Presentation of Patient Experience Tool-Kit Components- 10 mins Small group discussions to address the three questions (each table to start with a different question)- 30 mins Large group activity to report back and synthesis small group discussion, including the l use Mentimeter to capture core insights). The facilitators will summarize key insights and highlight core themes captures in the discussion at the end of the activity – 20 minutes Wrap up to assess interest in developing a community of practice for continued learning and sharing- 5 mins
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 | 0,218 | 0,207 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,011 | 0,019 |
| Communication savante | 0,018 | 0,035 |
| Science ouverte | 0,008 | 0,047 |
| Intégrité de la recherche | 0,014 | 0,031 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,005 |
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 ».