Quality in Orthopaedic Surgery—An International Perspective
Notice bibliographique
Résumé
Quality is a hallmark of health care, although it is difficult to come to a consensus on who gets to define what "quality health care" is. Most health-care workers enter this field with the goal of improving the health of their patients (and the community), and while everyone tries to do the best job possible, we must continuously seek better methods and techniques for achieving better outcomes. The passion for continuous improvement is fundamental, but passion is not sufficient by itself. There is substantial opportunity to improve quality and reduce cost in health care. Multidisciplinary teams that include physicians, nurses, and other ancillary care providers have led to decreased waiting times to see specialists and have also led to better management of chronic disease. By including ancillary care, providers can increase cancer-screening rates and have the potential to decrease readmissions. Moreover, the addition of hospitalists and physician assistants can produce quality and efficiency outcomes that are commensurate with those enjoyed by traditional house staff. However, truly improving performance is difficult due to questions about how we define "quality," design care processes, measure inputs and outputs, develop multi-stakeholder collaborations, and develop incentive programs for delivering "good" care. There is a definite need for more thorough and robust studies of the impact of pay-for-performance programs, with the inclusion of ancillary care providers. Current research has not shown that there is not enough evidence to be able to determine what incentive structure might "work" in a particular health-care system. Payment systems will continue to evolve to incentivize greater collaboration among providers to yield higher-quality, lower-cost care. Future efforts will necessitate the need for strong physician leadership in helping to develop an optimal care team that is as patient-centered as possible. Technology adds dimensions of capability to making improvement real and systematic, as well as providing safer care with fewer errors and better adherence to proven best practices. The drive for quality with technology produces better clinical outcomes and maximizes efficiencies and financial metrics of organizational performance. Technology also adds capabilities for capturing key metrics and reporting them back to clinicians and others. Improved data transparency informs those who can actually do things differently to produce better results and outcomes. While health-care entities strive to focus on quality of care, measuring and reporting such care in a meaningful way are difficult. The best chance of improving overall care for patients is through the adoption of systems that improve coordination and continuity, not by health-care staff working harder. Only through collaboration and integration can health care incorporate a culture for improving quality and patient safety.
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 | 0,010 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,005 | 0,008 |
| Études des sciences et des technologies | 0,001 | 0,008 |
| Communication savante | 0,008 | 0,007 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,005 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,001 |
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 ».