CORR Insights®: Do 360-degree Feedback Survey Results Relate to Patient Satisfaction Measures?
Notice bibliographique
Résumé
Where Are We Now? It has been impossible to escape the term “pay-for-performance” in the medical world in recent years. Understanding what this term implies is far more difficult given the uncertainty surrounding the way healthcare reform will shape the practice of medicine in the United States. Rewarding physicians based on patient satisfaction is one model for the implementation of pay-for-performance, however, the measurement and improvement of satisfaction is an evolving field [6]. In the current study, Hageman and colleagues report on the correlation of 360-degree feedback and patient satisfaction and find that coworker ratings on this survey correlate well with patient perceptions. The business world has employed multisource feedback (of which the 360-degree survey is one type) as a tool for managers for many years [2], and this evaluation method has been explored as an adjunct in resident education [9]. As far back as 1999, the College of Physicians and Surgeons of Alberta piloted a voluntary multisource feedback program, the Physician Achievement Review, which was shown to change physician behavior based on the results of the survey given to coworkers, staff, and patients [3]. The Physician Achievement Review program now has become mandatory on a 5-year cycle and serves as a model for other jurisdictions. Other examples of successful implementations of 360-degree feedback exist in medical education and in practice [4]. Where Do We Need To Go? The study of Hageman and colleagues pilots a proprietary survey in a large single-specialty academic orthopaedic group. The methodology demonstrates a link between retrospective patient satisfaction data and 360-degree feedback results. The sample size does not allow for conclusions related to the relationship between patient complaints and survey results. Although this study does not answer practitioner questions regarding the measurement of quality of care, which would allow the implementation of pay-for-performance, it does suggest that prospective employers of physicians could better evaluate new hires on quality. The determination of prospective quality metrics associated with physician 360-degree survey results is required to elevate this line of research. We must also evaluate 360-degree results longitudinally to see if this feedback results in physician behavior change over time. Increased self-awareness and improved interpersonal performance are the intended results of multidirectional feedback programs [8]. How Do We Get There? The next logical step in the implementation of 360-degree feedback surveys would be to demonstrate prospective patient satisfaction can be predicted by surveys, and that interventions related to the data captured by the multisource feedback program can improve patient satisfaction. These results would justify the resources required for implementation of these programs. Of particular value to orthopaedic surgery as a specialty would be the potential improvements in communication between surgeons and other medical team members. Various reports have focused on the need to improve communication with patients [1, 5, 7], however, little research exists on the quality of communication between orthopaedic surgeons and other providers and still less data is available to demonstrate the effect of improved team functioning on patient care. I hope that this initial study will lead to further research that demonstrates interventions like 360-degree feedback can improve the performance of medical teams and increase patient satisfaction.
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 | 0,017 | 0,182 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,028 | 0,007 |
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 ».